![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "asaW4NA0jK20"
+ },
+ "source": [
+ "\n",
+ "# Assignment 1\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Xv1Wc4ECfZwV",
+ "outputId": "388d6254-8991-42d5-b61f-c5bace95b996"
+ },
+ "source": [
+ "%pylab inline"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "LD_A0Idy5NY0"
+ },
+ "source": [
+ "from scipy.misc import electrocardiogram # scipy is a python scientific computing library\n",
+ "ecg = electrocardiogram() # let's load the scipy ECG dataset"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "NFw0i1_4-ixY"
+ },
+ "source": [
+ "SAMPLING_RATE = 360 # this data was sampled with 360 Hz"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MXCidk9L_VG8"
+ },
+ "source": [
+ "**Task 1:** Plot the data in the range from 9 to 11 seconds! [30 Points]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "s2n86tUP_pMZ",
+ "outputId": "3492aecf-420d-49b4-cc03-f2880eb5642b",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 297
+ }
+ },
+ "source": [
+ "# TODO your plotting code goes here\n",
+ "# please take the sampling rate into account to only plot the range\n",
+ "# between 9 and 11 seconds\n",
+ "\n",
+ "from9to11 = ecg[8*SAMPLING_RATE:11*SAMPLING_RATE]\n",
+ "# plt.plot(from9to11)\n",
+ "time_steps = np.linspace(9,12, len(from9to11))\n",
+ "plt.figure()\n",
+ "plt.plot(time_steps, from9to11)\n",
+ "plt.xlabel(\"Time [s]\")\n",
+ "plt.ylabel(\"Amplitude\")"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'Amplitude')"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 6
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Hev7LYxba6Db"
+ },
+ "source": [
+ "\n",
+ "#Assignment 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ux1g-TwrEe5N",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "f6155dab-e427-4e85-cf5d-532c6fcb22c9"
+ },
+ "source": [
+ "%pylab inline"
+ ],
+ "execution_count": 1,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "-MaZXclAEiGA",
+ "outputId": "2d953bee-228c-419b-bf50-df2e8d8d7d15"
+ },
+ "source": [
+ "# we will be using the EEG/MEG analysis library MNE\n",
+ "# documentation is available here: https://mne.tools/stable/index.html\n",
+ "!pip install -U mne"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting mne\n",
+ " Downloading mne-0.24.1-py3-none-any.whl (7.4 MB)\n",
+ "\u001b[K |████████████████████████████████| 7.4 MB 3.6 MB/s \n",
+ "\u001b[?25hRequirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from mne) (1.21.5)\n",
+ "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from mne) (1.4.1)\n",
+ "Installing collected packages: mne\n",
+ "Successfully installed mne-0.24.1\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "5q6p7Vp2Ek06"
+ },
+ "source": [
+ "import mne # let's import MNE\n",
+ "\n",
+ "# .. and the sample dataset\n",
+ "from mne.datasets import sample\n",
+ "\n",
+ "# These data were acquired with the Neuromag Vectorview system at \n",
+ "# MGH/HMS/MIT Athinoula A. Martinos Center Biomedical Imaging. \n",
+ "# EEG data from an electrode cap was acquired simultaneously with the MEG. \n",
+ "\n",
+ "### EXPERIMENT DESCRIPTION ###\n",
+ "# In this experiment, checkerboard patterns were presented to the subject into\n",
+ "# the left and right visual field, interspersed by tones to the left or right ear.\n",
+ "# The interval between the stimuli was 750 ms. Occasionally a smiley face was \n",
+ "# presented at the center of the visual field. The subject was asked to press a \n",
+ "# key with the right index finger as soon as possible after the appearance of the face."
+ ],
+ "execution_count": 3,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "wPiwovpdImU4",
+ "outputId": "e18eab26-e239-4011-bcf3-1cddd60b353a"
+ },
+ "source": [
+ "# and let's load it!\n",
+ "data_path = sample.data_path()\n",
+ "raw = mne.io.read_raw_fif(data_path + '/MEG/sample/sample_audvis_raw.fif')"
+ ],
+ "execution_count": 35,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Opening raw data file /root/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...\n",
+ " Read a total of 3 projection items:\n",
+ " PCA-v1 (1 x 102) idle\n",
+ " PCA-v2 (1 x 102) idle\n",
+ " PCA-v3 (1 x 102) idle\n",
+ " Range : 25800 ... 192599 = 42.956 ... 320.670 secs\n",
+ "Ready.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cl4dZERpcBEh"
+ },
+ "source": [
+ "**Task 1:** How many EEG channels were used when acquiring the data? [15 Points]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "37bn9S-dMn0p",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "outputId": "8be20217-0693-4ca2-d4be-90850e8766e7"
+ },
+ "source": [
+ "# Hint: You can use raw.info or raw.ch_names to figure this out!\n",
+ "# TODO: YOUR ANSWER\n",
+ "'''\n",
+ "60 EEG Channels\n",
+ "'''\n"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'\\n60 EEG Channels\\n'"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 5
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8pSy9pW5cKO_"
+ },
+ "source": [
+ "**Task 2:** Let's look at some channels! [20 Points]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 661
+ },
+ "id": "dpcL98RXOBZO",
+ "outputId": "6a949ec7-7221-4f5f-bcba-792ff33e2544"
+ },
+ "source": [
+ "# the code below plots EEG channels 1-8 for 3 seconds after 2 minutes\n",
+ "chs = ['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004', 'EEG 005', 'EEG 006', 'EEG 007', 'EEG 008']\n",
+ "chan_idxs = [raw.ch_names.index(ch) for ch in chs]\n",
+ "ecg1to8 = raw.plot(order=chan_idxs, start=120, duration=3)"
+ ],
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Using matplotlib as 2D backend.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/matplotlib/colors.py:263: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+ " c = np.array(c)\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| Measurement date | \n", + "December 03, 2002 19:01:10 GMT | \n", + " \n", + "
|---|---|
| Experimenter | \n", + "MEG | \n", + " \n", + "Participant | \n", + "Unknown | \n", + " \n", + "
| Digitized points | \n", + "146 points | \n", + "
| Good channels | \n", + "204 Gradiometers, 102 Magnetometers, 9 Stimulus, 60 EEG, 1 EOG | \n", + "
| Bad channels | \n", + "MEG 2443, EEG 053 | \n", + "
| EOG channels | \n", + "EOG 061 | \n", + "
| ECG channels | \n", + "Not available | \n", + "
| Sampling frequency | \n", + "600.61 Hz | \n", + "
| Highpass | \n", + "0.10 Hz | \n", + "
| Lowpass | \n", + "172.18 Hz | \n", + "
| Projections | \n", + "PCA-v1: off PCA-v2: off PCA-v3: off | \n",
+ "
| Filenames | \n", + "sample_audvis_raw.fif | \n", + "
| Duration | \n", + "00:04:37 (HH:MM:SS) | \n", + "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Hev7LYxba6Db"
+ },
+ "source": [
+ "\n",
+ "#Assignment 3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ux1g-TwrEe5N",
+ "outputId": "be251bb0-049e-4c3f-8801-da64e19bddb2"
+ },
+ "source": [
+ "%pylab inline"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cl4dZERpcBEh"
+ },
+ "source": [
+ "**Task 1:** Download and install 3D Slicer [15 Points]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "37bn9S-dMn0p"
+ },
+ "source": [
+ "# 3D Slicer is available for Windows, Linux, and Mac here: https://slicer.org\n",
+ "# Please use the stable version 4.11.2xxxxx !"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wFEDA1lMhZPM"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8pSy9pW5cKO_"
+ },
+ "source": [
+ "**Task 2:** Let's load some Ultrasound data! [15 Points]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "dpcL98RXOBZO"
+ },
+ "source": [
+ "# Please download this file: https://cs480.org/data/ultrasound.nrrd !\n",
+ "# Then, drag and drop the file into 3D Slicer and click OK."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "99OwONlCchwX"
+ },
+ "source": [
+ "# TODO Is this a traditional ultrasound image?\n",
+ "# TODO: YOUR_ANSWER (YES or NO and WHY)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "No. Because traditional ultrasounds can generate the image only in 2D and we don't have time as a dimension. We can navigate this image through time."
+ ],
+ "metadata": {
+ "id": "5ROtMoUSfSz1"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eGFoOHsrcy2L"
+ },
+ "source": [
+ "**Task 3:** Visualize the ultrasound data! [30 Points]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DoZQlOqmUkWg"
+ },
+ "source": [
+ "# Please use the Volume Rendering module in 3D slicer to render the data.\n",
+ "# Then, activate Cropping and adjust the bounding box to show the fetus.\n",
+ "# Hint: The MRI Default color map creates a nice visual."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_DnKFQyMdPDF"
+ },
+ "source": [
+ "# TODO Please post a screenshot of your rendering in the text box below by \n",
+ "# using the Upload image button after double-click."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "uADEXVqZj44j"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rLSbojsodr9X"
+ },
+ "source": [
+ "**Task 4:** Segment the fetus! [40 Points]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "yPrMJuVcgsD4"
+ },
+ "source": [
+ "# Switch to the Legacy->Editor module and click OK to create the GenericAnatomyColors label map.\n",
+ "# Select the PAINT EFFECT and activate THRESHOLD PAINTING.\n",
+ "# Hint: Reduce the RADIUS for a smaller brush.\n",
+ "# Hint: Adjust the lower threshold to not include the black background.\n",
+ "# Hint: Switch to 'Red Slice only' view and use the arrow keys to navigate through the slices.\n",
+ "# \n",
+ "# TODO: Mark the Fetus by drawing on each slice!\n",
+ "# Hint: Start at Slice ~83mm to ~112mm\n",
+ "# TODO: Take a screenshot of one slice after painting and upload it below."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "re82LVBuwJkw"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NN8fOim3y3kq"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "rWLWC3Nty-Iz"
+ },
+ "source": [
+ "# TODO: Use the MAKEMODELEFFECT and click APPLY.\n",
+ "# This will create a 3D mesh of your segmentation.\n",
+ "# TODO: Switch to '3D Only' view and take a screenshot of the segmented fetus in 3D!\n",
+ "# TODO: Upload the screenshot below."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "2C7ACEKowRee"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NmHxBDVMzhZE"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EdpGq1eRzi3u"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4yr4Boxce011"
+ },
+ "source": [
+ "**Bonus Task:** Use Slicer Python to analyze the image data. [33 Points]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "UJU2Xel-e_-f"
+ },
+ "source": [
+ "# Activate the console with View -> Python Interactor.\n",
+ "# Now, you can run Python commands in 3D Slicer (don't run the commands here but in 3D Slicer!).\n",
+ "#\n",
+ "# 1) Let's install mahotas into the 3D Slicer environment.\n",
+ "pip_install('mahotas')\n",
+ "\n",
+ "# \n",
+ "# 2) Grab the image data as a numpy array.\n",
+ "# Hint: You need to replace $MRML_ID with the correct value.\n",
+ "# Hint: You can find that value in the DATA module, if you activate 'Show MRML IDs'\n",
+ "node = slicer.mrmlScene.GetNodeByID('$MRML_ID')\n",
+ "data_as_array = slicer.util.arrayFromVolume(node)\n",
+ "\n",
+ "#\n",
+ "# 3) Now, data_as_array is a numpy array containing the image data.\n",
+ "# We can print the shape and also the max value.\n",
+ "print(data_as_array.shape)\n",
+ "### ANSWER ####\n",
+ "'''\n",
+ "(143, 224, 238)\n",
+ "'''\n",
+ "print(data_as_array.max())\n",
+ "### ANSWER ####\n",
+ "'''\n",
+ "254\n",
+ "'''\n",
+ "#\n",
+ "# 4) Please use mahotas to generate the histogram of the ultrasound data.\n",
+ "import mahotas as mh\n",
+ "pixelcounts = mh.fullhistogram(data_as_array)\n",
+ "\n",
+ "#\n",
+ "# TODO: Please answer the following questions.\n",
+ "# TODO: Max. Intensity Value in data_as_array:\n",
+ "# YOUR_ANSWER\n",
+ "### ANSWER ###\n",
+ "'''\n",
+ "254\n",
+ "'''\n",
+ "# TODO: How many pixels with this max. intensity value?\n",
+ "# YOUR_ANSWER\n",
+ "### ANSWER ###\n",
+ "'''\n",
+ "1\n",
+ "''' \n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "p7V4io1ngtUR"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EHfGf23KfHb-"
+ },
+ "source": [
+ "# You did it!!\n",
+ "# ,------.\n",
+ "# `-____-' ,-----------.\n",
+ "# ,i--i. | |\n",
+ "# / @ @ \\ / HEE HEE! |\n",
+ "# | -.__.- | ___-' J\n",
+ "# \\. ,/ \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"'\n",
+ "# ,\\\"\"\"\"/.\n",
+ "# ,' `--' `.\n",
+ "# (_,i' `i._)\n",
+ "# | |\n",
+ "# | ,. |\n",
+ "# | | | |\n",
+ "# `-' `-' -bodom-"
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/04/Copy_of_CS480_Assignment_4.ipynb b/04/Copy_of_CS480_Assignment_4.ipynb
new file mode 100644
index 0000000..e3215e3
--- /dev/null
+++ b/04/Copy_of_CS480_Assignment_4.ipynb
@@ -0,0 +1,879 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "Copy of CS480 Assignment 4.ipynb",
+ "provenance": [],
+ "collapsed_sections": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DMrBgGVKWEPJ"
+ },
+ "source": [
+ "\n",
+ "#Assignment 4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Ll5BVb2mZS2_"
+ },
+ "source": [
+ "# In this assignment, we will create a Random Forest classifier to detect pneumonia!"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "2lscACx4iuKi",
+ "outputId": "9120a949-37fd-4774-d64a-aa0665b1c459"
+ },
+ "source": [
+ "# load numpy and matplotlib\n",
+ "%pylab inline"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['clf']\n",
+ "`%matplotlib` prevents importing * from pylab and numpy\n",
+ " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "3yT4p3dDmJgI",
+ "outputId": "42da8940-1810-434e-a4f2-d286c794cbe2"
+ },
+ "source": [
+ "# we are using mahotas again, so lets install it!\n",
+ "!pip install mahotas"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: mahotas in /usr/local/lib/python3.7/dist-packages (1.4.12)\n",
+ "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mahotas) (1.21.5)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Lj9vr_3CmOYG"
+ },
+ "source": [
+ "# our imports include sklearn this time\n",
+ "import mahotas as mh\n",
+ "import os\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "import random"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "esXxAeAUWyB3"
+ },
+ "source": [
+ "**Task 1:** Download, extract, and load the data! [15 Points]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "yPO5ufNBmgwS",
+ "outputId": "a1a4c5b1-45c9-4074-da01-ad42f80d54db",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "source": [
+ "# Please download this file: https://cs480.org/data/xray.zip\n",
+ "# This is a subset of the Kaggle Pneumonia Dataset!\n",
+ "#\n",
+ "# TODO: Upload xray.zip using the file panel on the left.\n",
+ "!wget https://cs480.org/data/xray.zip"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "--2022-04-04 00:13:35-- https://cs480.org/data/xray.zip\n",
+ "Resolving cs480.org (cs480.org)... 185.199.108.153, 185.199.109.153, 185.199.110.153, ...\n",
+ "Connecting to cs480.org (cs480.org)|185.199.108.153|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 64445812 (61M) [application/zip]\n",
+ "Saving to: ‘xray.zip.1’\n",
+ "\n",
+ "xray.zip.1 100%[===================>] 61.46M 194MB/s in 0.3s \n",
+ "\n",
+ "2022-04-04 00:13:36 (194 MB/s) - ‘xray.zip.1’ saved [64445812/64445812]\n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ynJMN1ugiyik"
+ },
+ "source": [
+ "# Run this code to extract the zip file and to create\n",
+ "# the folder structure of normal/ and pneumonia/ images.\n",
+ "# Each folder contains the first images of the Kaggle dataset.\n",
+ "import zipfile\n",
+ "with zipfile.ZipFile('xray.zip', 'r') as zip_ref:\n",
+ " zip_ref.extractall('.')\n",
+ "\n",
+ "NORMAL_DIR = 'normal/'\n",
+ "PNEUMONIA_DIR = 'pneumonia/'"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "u61SolZ7mQ2l"
+ },
+ "source": [
+ "# Here we load the first image from the normal dataset.\n",
+ "img = mh.imread(NORMAL_DIR + 'IM-0115-0001.jpeg')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "4td0kmDkXlot"
+ },
+ "source": [
+ "# TODO: Please display this image and print the dimensions!"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ypOjAS1Dd4QG"
+ },
+ "source": [
+ "# TODO: YOUR CODE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SSEZ_i8EmZqf",
+ "outputId": "eaf91b7a-dbb5-4395-862d-c5de6882cac7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 287
+ }
+ },
+ "source": [
+ "matplotlib.pyplot.imshow(img)\n",
+ "print(img.shape)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "(1858, 2090)\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DMrBgGVKWEPJ"
+ },
+ "source": [
+ "\n",
+ "#Assignment 5"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Ll5BVb2mZS2_"
+ },
+ "source": [
+ "# In this assignment, we will visualize and explore a CT scan!"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "2lscACx4iuKi",
+ "outputId": "6738bed2-9f1b-4c68-c6d4-27574a303790"
+ },
+ "source": [
+ "# load numpy and matplotlib\n",
+ "%pylab inline"
+ ],
+ "execution_count": 1,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "3yT4p3dDmJgI",
+ "outputId": "485bfbb3-7d1c-4dc8-d166-956085f7c353"
+ },
+ "source": [
+ "# we are using pydicom, so lets install it!\n",
+ "!pip install pydicom"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting pydicom\n",
+ " Downloading pydicom-2.3.0-py3-none-any.whl (2.0 MB)\n",
+ "\u001b[K |████████████████████████████████| 2.0 MB 4.3 MB/s \n",
+ "\u001b[?25hInstalling collected packages: pydicom\n",
+ "Successfully installed pydicom-2.3.0\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "f_SOOwQf78z-"
+ },
+ "source": [
+ "**Task 1**: Download and visualize data with SliceDrop! [20 Points]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vXxbYHfalVQt"
+ },
+ "source": [
+ "# Please download https://cs480.org/data/ct.zip and extract it on your computer!\n",
+ "# This is a CT scan of an arm in DICOM format."
+ ],
+ "execution_count": 3,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!wget https://cs480.org/data/ct.zip"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "FLyE7R9awbVJ",
+ "outputId": "66b3da2c-7458-43a4-eb5a-64aae66248af"
+ },
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "--2022-04-12 00:29:23-- https://cs480.org/data/ct.zip\n",
+ "Resolving cs480.org (cs480.org)... 185.199.108.153, 185.199.109.153, 185.199.110.153, ...\n",
+ "Connecting to cs480.org (cs480.org)|185.199.108.153|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 5847706 (5.6M) [application/zip]\n",
+ "Saving to: ‘ct.zip’\n",
+ "\n",
+ "ct.zip 100%[===================>] 5.58M --.-KB/s in 0.08s \n",
+ "\n",
+ "2022-04-12 00:29:24 (70.1 MB/s) - ‘ct.zip’ saved [5847706/5847706]\n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import zipfile\n",
+ "with zipfile.ZipFile('ct.zip', 'r') as zip_ref:\n",
+ " zip_ref.extractall('.')"
+ ],
+ "metadata": {
+ "id": "91B2KKH0wnUz"
+ },
+ "execution_count": 5,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import os\n",
+ "from glob import glob"
+ ],
+ "metadata": {
+ "id": "SMcZKx-_wqgF"
+ },
+ "execution_count": 15,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "data = os.listdir(\"ct/\")"
+ ],
+ "metadata": {
+ "id": "jUqOHaKrwyNz"
+ },
+ "execution_count": 7,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wwtBQpTP8QbS"
+ },
+ "source": [
+ "# 1) Let's explore the data without loading it.\n",
+ "# TODO: Without loading the data, how many slices are there?"
+ ],
+ "execution_count": 8,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "E1Xdz3xw8ZJ6",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "360575ae-03be-49a7-dfcc-384e47ae0ba2"
+ },
+ "source": [
+ "# TODO: YOUR_ANSWER\n",
+ "print(len(data))\n",
+ "#220"
+ ],
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "220\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZJHks55e8a8K"
+ },
+ "source": [
+ "# 2) Let's visualize the data with SliceDrop! \n",
+ "# Go to https://slicedrop.com and drag'n'drop all .dcm files into the browser.\n",
+ "# Please use the 2D sliders to show axial, sagittal, and coronal slices in 3D."
+ ],
+ "execution_count": 10,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "guioyU-e9Q2s"
+ },
+ "source": [
+ "# TODO Please post a screenshot of SliceDrop's 3D View in the text box below by \n",
+ "# using the Upload image button after double-click."
+ ],
+ "execution_count": 11,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "M-CNGPbOc_xs"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EL0cayyf9JPo"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KbpOUGY9fKLw"
+ },
+ "source": [
+ "**Task 2**: Load the data using pydicom as a 3D volume and then reslice it! [35 Points]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kleZbthp9LcC"
+ },
+ "source": [
+ "# TODO: Please upload ct.zip using the file panel on the left.\n",
+ "# Then use the following snippet to extract the data."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "JE2DknZUfSG8"
+ },
+ "source": [
+ "import zipfile\n",
+ "with zipfile.ZipFile('ct.zip', 'r') as zip_ref:\n",
+ " zip_ref.extractall('.')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wOiAyhP2fSJm"
+ },
+ "source": [
+ "# 1) Now loop through all the DICOM files and store them in a 3D numpy array.\n",
+ "# Hint: You can either store them in a list first or read the dimensions of a\n",
+ "# single image slice to properly create the 3D numpy array.\n",
+ "# Hint 2: os.listdir(DIR) gives a list of filenames in a directory.\n",
+ "# Hint 2b: This list is not sorted - make sure you sort it.\n",
+ "# Hint 3: The dcmread function loads a single DICOM file.\n",
+ "# Hint 4: You can then use .pixel_array to access the image data."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "qcnvddIGjVOO"
+ },
+ "source": [
+ "from pydicom import dcmread\n",
+ "import sys"
+ ],
+ "execution_count": 16,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KN6vNSJpiRf0"
+ },
+ "source": [
+ "# TODO: YOUR CODE FOR LOADING THE VOLUME AS A 3D NUMPY ARRAY\n",
+ "img_1 = dcmread(\"ct/\"+data[120])\n",
+ "# data = sorted(data)"
+ ],
+ "execution_count": 71,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "slices = []\n",
+ "skipcount = 0\n",
+ "files = []\n",
+ "for f in data:\n",
+ " img = dcmread(\"ct/\"+f)\n",
+ " if hasattr(img, 'SliceLocation'):\n",
+ " slices.append(img)\n",
+ " else:\n",
+ " skipcount = skipcount + 1\n",
+ "print(skipcount)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "QgTsl46QLYtj",
+ "outputId": "6cc7994c-79c8-4134-b93e-4ab74a584d02"
+ },
+ "execution_count": 72,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "0\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "img = img_1.pixel_array\n",
+ "plt.imshow(img, cmap='gray')"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 287
+ },
+ "id": "Nv2jQsEQgLfJ",
+ "outputId": "1169020a-0fe4-415e-8c40-99dbf88be823"
+ },
+ "execution_count": 73,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DMrBgGVKWEPJ"
+ },
+ "source": [
+ "\n",
+ "#Assignment 6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Ll5BVb2mZS2_"
+ },
+ "source": [
+ "# In this assignment, we will train a U-Net classifer to detect mitochondria\n",
+ "# in electron microscopy images!"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "GhpX7CGfyuqw"
+ },
+ "source": [
+ "### IMPORTANT ###\n",
+ "#\n",
+ "# Activate GPU support: Runtime -> Change Runtime Type\n",
+ "# Hardware Accelerator: GPU\n",
+ "#\n",
+ "### IMPORTANT ###"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "2lscACx4iuKi",
+ "outputId": "3df81b84-cbce-4819-8e6b-8b4b5253b915"
+ },
+ "source": [
+ "# load numpy and matplotlib\n",
+ "%pylab inline"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZmB60TxJyQ61",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "46b7aa12-2215-4cd5-84a3-ef66b6119e0f"
+ },
+ "source": [
+ "# load the unet tensorflow code\n",
+ "!pip install git+https://github.com/jakeret/unet.git"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting git+https://github.com/jakeret/unet.git\n",
+ " Cloning https://github.com/jakeret/unet.git to /tmp/pip-req-build-w5abnglz\n",
+ " Running command git clone -q https://github.com/jakeret/unet.git /tmp/pip-req-build-w5abnglz\n",
+ "Building wheels for collected packages: unet\n",
+ " Building wheel for unet (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for unet: filename=unet-0.0.post0.dev81+gf557a51-py2.py3-none-any.whl size=25688 sha256=2caa1f7838aea85c19d09d3e9173b67c3f784f7e0d056584e1d3f456817f0365\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-p7v6si7v/wheels/22/00/93/ce57529ed355f160088cbb6ce086e55168770913b40cf624ba\n",
+ "Successfully built unet\n",
+ "Installing collected packages: unet\n",
+ "Successfully installed unet-0.0.post0.dev81+gf557a51\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "gn19r-d9yZVk"
+ },
+ "source": [
+ "# .. and use it!\n",
+ "# Note: There are a ton of U-Net implementations but this one is easier to use!\n",
+ "import unet"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uMSdlwXfyRAv"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "f_SOOwQf78z-"
+ },
+ "source": [
+ "**Task 1**: Study the unet circle example and learn about mitochondria! [15 Points]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "rfTKzDOLysUW"
+ },
+ "source": [
+ "# The unet package allows to train a U-Net classifier with little code.\n",
+ "# As discussed in class, the U-Net can be used to label/segment/annotate images."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kI8AJjDmysXR"
+ },
+ "source": [
+ "# TODO: Please study the Circles example \n",
+ "# https://github.com/jakeret/unet/blob/master/notebooks/circles.ipynb"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QM4GKfzTysZv"
+ },
+ "source": [
+ "# TODO: ... and look how the (artificial) dataset is generated\n",
+ "# https://github.com/jakeret/unet/blob/master/src/unet/datasets/circles.py"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "P2BsS24Cyscm",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 52
+ },
+ "outputId": "27bf5360-02c2-482d-f245-c71b3c6a91bf"
+ },
+ "source": [
+ "# 1) Question\n",
+ "# TODO: Please describe what the U-Net is trained to do.\n",
+ "#\n",
+ "# TODO: YOUR ANSWER\n",
+ "'''\n",
+ "The model is segmentation model that segments circles in the images. So it's a binary clkassification model but instead of classifying the whole image, this model classifies each pixel in the image as circle\n",
+ "or not circle. \n",
+ "'''"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "\"\\nThe model is segmentation model that segments circles in the images. So it's a binary clkassification model but instead of classifying the whole image, this model classifies each pixel in the image as circle\\nor not circle. \\n\""
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 7
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vKAyYg6yzXnw",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 52
+ },
+ "outputId": "f6ae3054-cd47-4baa-e6b9-aeec680eb50a"
+ },
+ "source": [
+ "# 2) Question\n",
+ "# TODO: In circles.py, what does the following mean:\n",
+ "# channels = 1\n",
+ "# classes = 2\n",
+ "#\n",
+ "# TODO: YOUR ANSWER\n",
+ "'''\n",
+ "Channels mean if the image is RGB or grayscale image. In this case, they are grayscale images. \n",
+ "Classes mean the number of labels. As mentioned above, this segementation is either circle or not circle, hence two labels. \n",
+ "'''"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'\\nChannels mean if the image is RGB or grayscale image. In this case, they are grayscale images. \\nClasses mean the number of labels. As mentioned above, this segementation is either circle or not circle, hence two labels. \\n'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 8
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "iRDRxjS-9LYi"
+ },
+ "source": [
+ "# 3) Question\n",
+ "# TODO: What are mitochondria and what is their appearance in\n",
+ "# electron microscopy data?\n",
+ "# Hint: You can validate your answer in Task 4!\n",
+ "#\n",
+ "# TODO: YOUR ANSWER"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XdcmkK1i1Yv8"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KbpOUGY9fKLw"
+ },
+ "source": [
+ "**Task 2**: Setup a datagenerator for mitochondria images! [45 Points]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kleZbthp9LcC",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "5979be31-a57c-4ec2-da22-b7a55c06df6b"
+ },
+ "source": [
+ "# TODO: \n",
+ "# Download https://cs480.org/data/mito.npz to your computer.\n",
+ "# Then, please upload mito.npz using the file panel on the left.\n",
+ "!wget https://cs480.org/data/mito.npz"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "--2022-04-25 22:39:56-- https://cs480.org/data/mito.npz\n",
+ "Resolving cs480.org (cs480.org)... 185.199.108.153, 185.199.109.153, 185.199.110.153, ...\n",
+ "Connecting to cs480.org (cs480.org)|185.199.108.153|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 37610178 (36M) [application/octet-stream]\n",
+ "Saving to: ‘mito.npz’\n",
+ "\n",
+ "mito.npz 100%[===================>] 35.87M --.-KB/s in 0.1s \n",
+ "\n",
+ "2022-04-25 22:39:57 (240 MB/s) - ‘mito.npz’ saved [37610178/37610178]\n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "cJJB5etk0O3B"
+ },
+ "source": [
+ "# The code below is similar to the circles.py file from Task 1.\n",
+ "# We follow Tensorflow conventions to design a tf.data.Dataset for training\n",
+ "# the U-Net.\n",
+ "#\n",
+ "# TODO: Please add four different data augmentation methods in the code block\n",
+ "# below. (image normalization to 0..1, horizontal data flip, vertical data flip,\n",
+ "# rotation by 90 degrees)\n",
+ "#\n",
+ "# Hint: https://github.com/jakeret/unet/blob/master/src/unet/datasets/oxford_iiit_pet.py#L25"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "d-aohs2x0O5s"
+ },
+ "source": [
+ "import tensorflow as tf\n",
+ "import tensorflow_datasets as tfds\n",
+ "from tensorflow_datasets.core import DatasetInfo\n",
+ "from typing import Tuple, List\n",
+ "\n",
+ "IMAGE_SIZE = (256, 256)\n",
+ "channels = 1\n",
+ "classes = 2\n",
+ "\n",
+ "def load_data(count:int, splits:Tuple[float]=(0.7, 0.2, 0.1), **kwargs) -> List[tf.data.Dataset]:\n",
+ " return [tf.data.Dataset.from_tensor_slices(_build_samples(int(split * count), **kwargs))\n",
+ " for split in splits]\n",
+ "\n",
+ "\n",
+ "def _build_samples(sample_count:int, **kwargs) -> Tuple[np.array, np.array]:\n",
+ "\n",
+ " # here we load the mitochondria data\n",
+ " loaded = np.load('mito.npz')\n",
+ " loadedimages = loaded['arr_0'][0].copy()\n",
+ " loadedmasks = loaded['arr_0'][1].copy()\n",
+ "\n",
+ " # now let's go to numpyland\n",
+ " images = np.empty((sample_count, IMAGE_SIZE[0], IMAGE_SIZE[1], 1))\n",
+ " labels = np.empty((sample_count, IMAGE_SIZE[0], IMAGE_SIZE[1], 2))\n",
+ " for i in range(sample_count):\n",
+ " image, mask = loadedimages[i], loadedmasks[i]\n",
+ "\n",
+ " image = image.reshape((IMAGE_SIZE[0], IMAGE_SIZE[1], 1)).astype(np.float)\n",
+ " mask = mask.reshape((IMAGE_SIZE[0], IMAGE_SIZE[1], 1))\n",
+ "\n",
+ " #\n",
+ " # TODO: Normalize the image to 0..1\n",
+ " #\n",
+ " # TODO: YOUR CODE\n",
+ " image = tf.cast(image, tf.float32) / 255.0\n",
+ " # mask -=1\n",
+ " #\n",
+ " # TODO: Use Tensorflow to flip the image horizontally\n",
+ " #\n",
+ " if tf.random.uniform(()) > 0.5:\n",
+ " #\n",
+ " # TODO: YOUR CODE\n",
+ " #\n",
+ " image = tf.image.flip_left_right(image)\n",
+ " mask = tf.image.flip_left_right(mask)\n",
+ "\n",
+ " #\n",
+ " # TODO: Use Tensorflow to flip the image vertically\n",
+ " #\n",
+ " if tf.random.uniform(()) > 0.5:\n",
+ " #\n",
+ " # TODO: YOUR CODE\n",
+ " #\n",
+ " image = tf.image.flip_up_down(image)\n",
+ " mask = tf.image.flip_up_down(mask)\n",
+ "\n",
+ " #\n",
+ " # TODO: Use Tensorflow to rotate the image 90 degrees\n",
+ " #\n",
+ " if tf.random.uniform(()) > 0.5:\n",
+ " #\n",
+ " # TODO: YOUR CODE\n",
+ " #\n",
+ " image = tf.image.rot90(image)\n",
+ " mask = tf.image.rot90(mask)\n",
+ "\n",
+ "\n",
+ " # augmentation done, let's store the image\n",
+ " images[i] = image\n",
+ "\n",
+ " # here we split the mask to background and foreground\n",
+ " fg = np.zeros((IMAGE_SIZE[0], IMAGE_SIZE[1], 1), dtype=np.bool)\n",
+ " fg[mask == 255] = 1\n",
+ " bg = np.zeros((IMAGE_SIZE[0], IMAGE_SIZE[1], 1), dtype=np.bool)\n",
+ " bg[mask == 0] = 1\n",
+ " \n",
+ " labels[i, :, :, 0] = bg[:,:,0]\n",
+ " labels[i, :, :, 1] = fg[:,:,0]\n",
+ "\n",
+ " return images, labels"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eqsEUwQKfXnu"
+ },
+ "source": [
+ "**Task 3**: Let's train the U-Net! [25 Points]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "pnF1RIg1nVv7",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "28d29747-e2b7-4766-f7ae-1922edccc9f9"
+ },
+ "source": [
+ "#\n",
+ "# We can now create our training, validation, and testing data by calling\n",
+ "# our methods from Task 2.\n",
+ "#\n",
+ "train, val, test = load_data( 660, splits=(0.7, 0.2, 0.1) )"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:28: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+ "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:72: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+ "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:74: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+ "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ODwQX4rN3VfX",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "outputId": "a2abe41c-fbcd-4c14-b92d-526011ebf35c"
+ },
+ "source": [
+ "# 1) Question\n",
+ "# TODO: We have in total 660 images + 660 masks. Based on the code above,\n",
+ "# how many images are used for training alone?\n",
+ "#\n",
+ "# TODO: YOUR ANSWER\n",
+ "'''\n",
+ "The training, validation and test set are spit according to 70%, 20% and 10%. So the training data is 462 images.\n",
+ "'''"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'\\nThe training, validation and test set are spit according to 70%, 20% and 10%. So the training data is 462 images.\\n'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 14
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "cCY5ao3X3uYF"
+ },
+ "source": [
+ "#\n",
+ "# Let's setup the U-Net!\n",
+ "#\n",
+ "LEARNING_RATE = 1e-3\n",
+ "\n",
+ "unet_model = unet.build_model(channels=channels,\n",
+ " num_classes=classes,\n",
+ " layer_depth=5,\n",
+ " filters_root=64,\n",
+ " padding=\"same\")\n",
+ "\n",
+ "unet.finalize_model(unet_model, learning_rate=LEARNING_RATE)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "MYezkNl-4Fu3"
+ },
+ "source": [
+ "#\n",
+ "# And, let's setup the trainer...\n",
+ "#\n",
+ "trainer = unet.Trainer(checkpoint_callback=False,\n",
+ " learning_rate_scheduler=unet.SchedulerType.WARMUP_LINEAR_DECAY,\n",
+ " warmup_proportion=0.1,\n",
+ " learning_rate=LEARNING_RATE)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bPs0NxVy3ua3",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 922
+ },
+ "outputId": "bb7211ec-9a06-46b4-c8e0-39849cb9ca12"
+ },
+ "source": [
+ "#\n",
+ "# ...and train the U-Net for 50 epochs with a batch_size of 10!\n",
+ "#\n",
+ "# TODO: Please complete the code below.\n",
+ "# Hint: Don't forget to use training and validation data.\n",
+ "# Hint 2: This will take roughly 30 minutes!\n",
+ "#\n",
+ "trainer.fit(unet_model,\n",
+ " train,\n",
+ " val,\n",
+ " epochs=50,\n",
+ " batch_size=10)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch 1/50\n",
+ "47/47 [==============================] - 89s 2s/step - loss: 0.5407 - categorical_crossentropy: 0.5407 - categorical_accuracy: 0.8024 - mean_iou: 0.3449 - dice_coefficient: 0.6321 - auc: 0.8299 - learning_rate: 9.9911e-05 - val_loss: 0.3310 - val_categorical_crossentropy: 0.3310 - val_categorical_accuracy: 0.9434 - val_mean_iou: 0.3924 - val_dice_coefficient: 0.7332 - val_auc: 0.9839\n",
+ "Epoch 2/50\n",
+ "47/47 [==============================] - 67s 1s/step - loss: 0.3446 - categorical_crossentropy: 0.3446 - categorical_accuracy: 0.9115 - mean_iou: 0.4273 - dice_coefficient: 0.8002 - auc: 0.9249 - learning_rate: 2.9991e-04 - val_loss: 0.4064 - val_categorical_crossentropy: 0.4064 - val_categorical_accuracy: 0.9434 - val_mean_iou: 0.3620 - val_dice_coefficient: 0.6732 - val_auc: 0.9841\n",
+ "Epoch 3/50\n",
+ "47/47 [==============================] - 67s 1s/step - loss: 0.3222 - categorical_crossentropy: 0.3222 - categorical_accuracy: 0.9115 - mean_iou: 0.4390 - dice_coefficient: 0.8249 - auc: 0.9279 - learning_rate: 4.9991e-04 - val_loss: 0.2398 - val_categorical_crossentropy: 0.2398 - val_categorical_accuracy: 0.9434 - val_mean_iou: 0.4365 - val_dice_coefficient: 0.8177 - val_auc: 0.9837\n",
+ "Epoch 4/50\n",
+ "47/47 [==============================] - 67s 1s/step - loss: 0.3044 - categorical_crossentropy: 0.3044 - categorical_accuracy: 0.9115 - mean_iou: 0.4442 - dice_coefficient: 0.8327 - auc: 0.9314 - learning_rate: 6.9991e-04 - val_loss: 0.2229 - val_categorical_crossentropy: 0.2229 - val_categorical_accuracy: 0.9434 - val_mean_iou: 0.4462 - val_dice_coefficient: 0.8352 - val_auc: 0.9840\n",
+ "Epoch 5/50\n",
+ "47/47 [==============================] - 67s 1s/step - loss: 0.3019 - categorical_crossentropy: 0.3019 - categorical_accuracy: 0.9115 - mean_iou: 0.4453 - dice_coefficient: 0.8329 - auc: 0.9341 - learning_rate: 8.9991e-04 - val_loss: 0.2274 - val_categorical_crossentropy: 0.2274 - val_categorical_accuracy: 0.9434 - val_mean_iou: 0.4441 - val_dice_coefficient: 0.8283 - val_auc: 0.9840\n",
+ "Epoch 6/50\n",
+ "47/47 [==============================] - 67s 1s/step - loss: 0.3058 - categorical_crossentropy: 0.3058 - categorical_accuracy: 0.9115 - mean_iou: 0.4449 - dice_coefficient: 0.8315 - auc: 0.9357 - learning_rate: 9.8890e-04 - val_loss: 0.2313 - val_categorical_crossentropy: 0.2313 - val_categorical_accuracy: 0.9434 - val_mean_iou: 0.4421 - val_dice_coefficient: 0.8233 - val_auc: 0.9842\n",
+ "Epoch 7/50\n",
+ "47/47 [==============================] - 67s 1s/step - loss: 0.2984 - categorical_crossentropy: 0.2984 - categorical_accuracy: 0.9115 - mean_iou: 0.4484 - dice_coefficient: 0.8373 - auc: 0.9369 - learning_rate: 9.6668e-04 - val_loss: 0.2090 - val_categorical_crossentropy: 0.2090 - val_categorical_accuracy: 0.9434 - val_mean_iou: 0.4552 - val_dice_coefficient: 0.8469 - val_auc: 0.9844\n",
+ "Epoch 8/50\n",
+ "47/47 [==============================] - 67s 1s/step - loss: 0.2829 - categorical_crossentropy: 0.2829 - categorical_accuracy: 0.9115 - mean_iou: 0.4536 - dice_coefficient: 0.8411 - auc: 0.9426 - learning_rate: 9.4445e-04 - val_loss: 0.2028 - val_categorical_crossentropy: 0.2028 - val_categorical_accuracy: 0.9434 - val_mean_iou: 0.4602 - val_dice_coefficient: 0.8506 - val_auc: 0.9849\n",
+ "Epoch 9/50\n",
+ "47/47 [==============================] - 67s 1s/step - loss: 0.2756 - categorical_crossentropy: 0.2756 - categorical_accuracy: 0.9115 - mean_iou: 0.4578 - dice_coefficient: 0.8439 - auc: 0.9470 - learning_rate: 9.2223e-04 - val_loss: 0.2046 - val_categorical_crossentropy: 0.2046 - val_categorical_accuracy: 0.9434 - val_mean_iou: 0.4609 - val_dice_coefficient: 0.8454 - val_auc: 0.9851\n",
+ "Epoch 10/50\n",
+ "47/47 [==============================] - 67s 1s/step - loss: 0.2712 - categorical_crossentropy: 0.2712 - categorical_accuracy: 0.9115 - mean_iou: 0.4610 - dice_coefficient: 0.8453 - auc: 0.9499 - learning_rate: 9.0001e-04 - val_loss: 0.2055 - val_categorical_crossentropy: 0.2055 - val_categorical_accuracy: 0.9434 - val_mean_iou: 0.4625 - val_dice_coefficient: 0.8425 - val_auc: 0.9851\n",
+ "Epoch 11/50\n",
+ "47/47 [==============================] - 67s 1s/step - loss: 0.2654 - categorical_crossentropy: 0.2654 - categorical_accuracy: 0.9115 - mean_iou: 0.4647 - dice_coefficient: 0.8475 - auc: 0.9530 - learning_rate: 8.7779e-04 - val_loss: 0.1986 - val_categorical_crossentropy: 0.1986 - val_categorical_accuracy: 0.9434 - val_mean_iou: 0.4686 - val_dice_coefficient: 0.8482 - val_auc: 0.9854\n",
+ "Epoch 12/50\n",
+ "47/47 [==============================] - 67s 1s/step - loss: 0.2583 - categorical_crossentropy: 0.2583 - categorical_accuracy: 0.9115 - mean_iou: 0.4692 - dice_coefficient: 0.8495 - auc: 0.9564 - learning_rate: 8.5557e-04 - val_loss: 0.1980 - val_categorical_crossentropy: 0.1980 - val_categorical_accuracy: 0.9434 - val_mean_iou: 0.4715 - val_dice_coefficient: 0.8475 - val_auc: 0.9855\n",
+ "Epoch 13/50\n",
+ "47/47 [==============================] - 67s 1s/step - loss: 0.2530 - categorical_crossentropy: 0.2530 - categorical_accuracy: 0.9115 - mean_iou: 0.4729 - dice_coefficient: 0.8519 - auc: 0.9588 - learning_rate: 8.3334e-04 - val_loss: 0.1914 - val_categorical_crossentropy: 0.1914 - val_categorical_accuracy: 0.9434 - val_mean_iou: 0.4777 - val_dice_coefficient: 0.8534 - val_auc: 0.9858\n",
+ "Epoch 14/50\n",
+ "47/47 [==============================] - 67s 1s/step - loss: 0.2457 - categorical_crossentropy: 0.2457 - categorical_accuracy: 0.9115 - mean_iou: 0.4777 - dice_coefficient: 0.8542 - auc: 0.9617 - learning_rate: 8.1112e-04 - val_loss: 0.1900 - val_categorical_crossentropy: 0.1900 - val_categorical_accuracy: 0.9434 - val_mean_iou: 0.4814 - val_dice_coefficient: 0.8541 - val_auc: 0.9860\n",
+ "Epoch 15/50\n",
+ "47/47 [==============================] - 67s 1s/step - loss: 0.2408 - categorical_crossentropy: 0.2408 - categorical_accuracy: 0.9115 - mean_iou: 0.4817 - dice_coefficient: 0.8563 - auc: 0.9637 - learning_rate: 7.8890e-04 - val_loss: 0.1864 - val_categorical_crossentropy: 0.1864 - val_categorical_accuracy: 0.9434 - val_mean_iou: 0.4854 - val_dice_coefficient: 0.8574 - val_auc: 0.9862\n",
+ "Epoch 16/50\n",
+ " 7/47 [===>..........................] - ETA: 50s - loss: 0.1775 - categorical_crossentropy: 0.1775 - categorical_accuracy: 0.9416 - mean_iou: 0.4882 - dice_coefficient: 0.8755 - auc: 0.9827 - learning_rate: 7.7636e-04"
+ ]
+ },
+ {
+ "output_type": "error",
+ "ename": "KeyboardInterrupt",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DMrBgGVKWEPJ"
+ },
+ "source": [
+ "\n",
+ "#Assignment 7"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Ll5BVb2mZS2_"
+ },
+ "source": [
+ "# In this assignment, we will explore the Imaging Data Commons,\n",
+ "# a new service from the National Cancer Institute.\n",
+ "#\n",
+ "# We will find and explore a PET dataset and then perform a MIP reconstruction."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "W1m0WCBb6h5n"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uMSdlwXfyRAv"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "f_SOOwQf78z-"
+ },
+ "source": [
+ "**Task 1**: Browse the IDC datasets and find a combined CT or MRI with PET! [15 Points]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XdcmkK1i1Yv8"
+ },
+ "source": [
+ "#\n",
+ "# Visit https://portal.imaging.datacommons.cancer.gov/ and find a dataset\n",
+ "# that includes structural imaging such as CT or MRI combined with a PET scan.\n",
+ "#\n",
+ "# You can start by selecting the type of a cancer case such as Bladder, Brain etc.\n",
+ "# Hint: Head and Neck include many PET scans.\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "AHFYoKTkPppz"
+ },
+ "source": [
+ "# TODO: Open the integrated OHIF viewer to view the data.\n",
+ "#\n",
+ "# 1) Select a collection, then a case, then a study in the IDC interface.\n",
+ "# 2) To open the OHIF viewer, click on the view icon.\n",
+ "# 3) Paste a screenshot of the viewer after the data was loaded below.\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rMhD_uQU7mAq"
+ },
+ "source": [
+ "TODO: Add screenshot"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "y-1_MZ8yswDN"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "This is data for 4D Lung collection and 100_HM10395 case"
+ ],
+ "metadata": {
+ "id": "GUwTeLxWs46M"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KbpOUGY9fKLw"
+ },
+ "source": [
+ "**Task 2**: Visualize structural and PET side-by-side! [30 Points]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9UNuaugR0O8k"
+ },
+ "source": [
+ "# In the OHIF viewer, please change the layout to show the structural image\n",
+ "# next to the PET scan.\n",
+ "#\n",
+ "# TODO: Use the LAYOUT functionality in the toolbar to switch to a 2-column layout."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "d2J1AD8M77Tx"
+ },
+ "source": [
+ "# TODO: Find the cancerous lesion.\n",
+ "#\n",
+ "# 1) Navigate through the PET image to find the cancerous lesion."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2xkdKCnm77W2"
+ },
+ "source": [
+ "# TODO: Sync the structural scan (CT or MR) to the PET scan with the lesion.\n",
+ "#\n",
+ "# Hint: The \"Loc\" label in the bottom left of both scans should roughly match.\n",
+ "# Hint 2: The Levels tool allows Window/Level adjustment to increase contrast.\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "4QQ39tAk77Z2"
+ },
+ "source": [
+ "# TODO: Paste a screenshot showing the 2-column layout below."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "Bvbq1uoUus2B"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Q5dsPqcE875J"
+ },
+ "source": [
+ "TODO: Add screenshot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "dbYv-XZjPFZa"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eqsEUwQKfXnu"
+ },
+ "source": [
+ "**Task 3**: Use an MIP to reslice the PET scan! [35 Points]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XGYzUOUK4__u"
+ },
+ "source": [
+ "# We will now use the 2D MPR functionality to look at the lesion from different\n",
+ "# orientations.\n",
+ "#\n",
+ "# Hint: This works best in Google Chrome."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KLHO0i2n9uTj"
+ },
+ "source": [
+ "# Question 1): What is MPR?\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "AHKBMrIi94tX"
+ },
+ "source": [
+ "# TODO: YOUR ANSWER"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "MPR means multi-planer reformation or reconstruction. It is used to convert data from one domain to anbother. "
+ ],
+ "metadata": {
+ "id": "fPdl0hYDeypV"
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "OHuclxry96Th"
+ },
+ "source": [
+ "# Question 2): What is the difference between MIP, MinIP, AvgIP?\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tDwbU5ckPHnA"
+ },
+ "source": [
+ "# TODO: YOUR ANSWER"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "They mean Maximum Intesity Projection, Minimum Intensity Projection and Average Intensity Projection. So it is used for thresholding and get the images according to the required values"
+ ],
+ "metadata": {
+ "id": "Y95hz9U9fIsm"
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6aMkM41v-AHW"
+ },
+ "source": [
+ "# Now, please navigate to the lesion in all 3 orientations.\n",
+ "#\n",
+ "# TODO: Paste a screenshot below.\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "XSAGctHlvjds"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7_7qJ4Ng-O15"
+ },
+ "source": [
+ "TODO: Add screenshot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "el0CRGhL-A2o"
+ },
+ "source": [
+ "# Question 3): What does the Slab Thickness slider do?\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "OnF3VM8f-UZK"
+ },
+ "source": [
+ "# TODO: YOUR ANSWER"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "It goes through different regions of the area (in this case, lungs) and gives us the respective scan"
+ ],
+ "metadata": {
+ "id": "IpKOyRyyu1mn"
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XYtx1zEx-Qh2"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "R68B9XFl5Rgw"
+ },
+ "source": [
+ "**Task 4**: Let's access the pixel data! [20 Points]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nUY4a8v47wN6"
+ },
+ "source": [
+ "# Please EXIT THE 2D MPR.\n",
+ "#\n",
+ "# We will now grab the pixel data of the currently displayed slice\n",
+ "# using the Javascript console.\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "jNb5khgK-w0G"
+ },
+ "source": [
+ "# 1) Open the Developer Tools of your browser and access the JS console.\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "TkLwR8xAPI-e"
+ },
+ "source": [
+ "# 2) The following code allows to access the pixel data of the slice that\n",
+ "# is currently displayed.\n",
+ "#\n",
+ "# element = cornerstone.getEnabledElements()[0];\n",
+ "# pixels = element.image.getPixelData();\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "pQ_WuOSH_KXr"
+ },
+ "source": [
+ "# Question 4): What is the maximum pixel value of the current slice?\n",
+ "#\n",
+ "# Hint: There are multiple ways of doing this according to\n",
+ "# https://medium.com/coding-at-dawn/the-fastest-way-to-find-minimum-and-maximum-values-in-an-array-in-javascript-2511115f8621\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "u_swNiBV_xNK"
+ },
+ "source": [
+ "# TODO: YOUR ANSWER"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "1123"
+ ],
+ "metadata": {
+ "id": "_clwAh-Li9Ce"
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "19WZcZLj_xQ0"
+ },
+ "source": [
+ " "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1f72yBFNgonn"
+ },
+ "source": [
+ "**Bonus**: Apply a filter mask to the current slice! [33 Points]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "cEEzh8DcACOR"
+ },
+ "source": [
+ "# Here we will modify the pixel data!\n",
+ "#\n",
+ "# The following code allows you to set the pixels of the current slice.\n",
+ "#\n",
+ "# pixels.set(new_pixels);\n",
+ "#\n",
+ "# Write code to APPLY A FILTER MASK to the pixels.\n",
+ "#\n",
+ "# Hint: You can use the following code to get the dimensions of the current slice.\n",
+ "# w = element.image.width;\n",
+ "# h = element.image.height;\n",
+ "#\n",
+ "# You can decide which filter mask to apply (e.g., Gaussian blur, edge detection..).\n",
+ "#\n",
+ "# ** IMPORTANT **\n",
+ "# Hint 2: Triggering a REDRAW programmatically might be hard but you can use\n",
+ "# the LEVELS tool to manually trigger a redraw to see filtered pixels.\n",
+ "# ** IMPORTANT **"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "V3cGbWdB93om"
+ },
+ "source": [
+ "#\n",
+ "#\n",
+ "# TODO: YOUR CODE GOES HERE\n",
+ "#\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "u7ZiV_N5A9to"
+ },
+ "source": [
+ "# TODO: Please add a screenshot after filtering the current slice."
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ESU5G9cjBAmt"
+ },
+ "source": [
+ "TODO: ADD SCREENSHOT"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0ZtwkapEBVrx"
+ },
+ "source": [
+ "#\n",
+ "# SUPER BONUS (+33 extra): Can you trigger the redraw programmatically?\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "fjvuYHqdBdHU"
+ },
+ "source": [
+ "# TODO: YOUR CODE FOR REDRAW GOES HERE"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0PjWeCJ2825h"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kWb3h4MKm5t4"
+ },
+ "source": [
+ "#\n",
+ "# THANK YOU!!!\n",
+ "#\n",
+ "# .--..--..--..--..--..--.\n",
+ "# .' \\ (`._ (_) _ \\\n",
+ "# .' | '._) (_) |\n",
+ "# \\ _.')\\ .----..---. /\n",
+ "# |(_.' | / .-\\-. \\ |\n",
+ "# \\ 0| | ( O| O) | o|\n",
+ "# | _ | .--.____.'._.-. |\n",
+ "# \\ (_) | o -` .-` |\n",
+ "# | \\ |`-._ _ _ _ _\\ /\n",
+ "# \\ | | `. |_||_| |\n",
+ "# | o | \\_ \\ | -. .-.\n",
+ "# |.-. \\ `--..-' O | `.`-' .'\n",
+ "# _.' .' | `-.-' /-.__ ' .-'\n",
+ "# .' `-.` '.|='=.='=.='=.='=|._/_ `-'.'\n",
+ "# `-._ `. |________/\\_____| `-.'\n",
+ "# .' ).| '=' '='\\/ '=' |\n",
+ "# `._.` '---------------'\n",
+ "# //___\\ //___\\\n",
+ "# || ||\n",
+ "# LGB ||_.-. ||_.-.\n",
+ "# (_.--__) (_.--__)\n",
+ "#\n",
+ "#"
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file