P_Mask_RCNN

.gitignore

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+.git/
+logs/
+datasets/
+results/
+temp/
+test/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# VS Studio Code
+.vscode
+
+# PyCharm
+.idea/
+
+# Dropbox
+.dropbox.attr
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/

README.md

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+# P_mask_RCNN for pulmonary embolism(PE) detection and segmentation
+
+This is an implementation of P_Mask RCNN
+on Python 3, Keras, and TensorFlow>=1.10, <2.0. The code inherits from [Mask RCNN]
+(https://github.com/matterport/Mask_RCNN). We mainly modified its RPN part. 
+Our method has a better performance on small object detection. 
+
+![Instance Segmentation Sample](images/p2.jpg)
+
+The repository includes:
+* the source code of Mask RCNN
+* the source code of constructing Guassian mixture model(GMM) and the implements of 
+the Expectation-Maximization(EM) algorithm
+* the source code of sample from GMM
+* the source code of the RPN model of P_mask_RCNN
+
+#Data Description
+to reproduce the results of the experiment in our paper, you should download the PE dataset
+from [here](https://figshare:com/authors/MojtabaMasoudi/5215238).
+
+The dataset is provided in dicom form, you should change it to jpg form, first. After that,
+you should generate a label file for the coco dataset using the mask label provided by the dataset, 
+and replace the file in the [annotations](annotations) directory.
+you can also use the label file provided by us. If you use our label files, 
+You need to rename each image in the following format: "%2d%3d.jpg" % (patient_id, slice_index),
+and divide the data set to 3 parts, including: train, test and val, with the image name contained in each label file. 
+
+The dataset includes 35 patients. A total of 8,792 CTPA images with
+the size of 512×512 pixels are included, of which 2,304
+contained lesion areas with 3781 PE regions of interest(PE-ROIs) altogether.More than 85% of these PE-ROIs are small
+objects with the square root of the area≤32 pixels which only
+occupy on average 0.3% of the image area.
+![objects square distribution](images/area.png)
+
+#Getting started
+Install the required package:
+```
+pip install -r requirements.txt
+```
+
+* [sampling.sample.py](sampling/sample.py) is the module to build GMM and sample points from GMM and save it to the 
+[locations](locations) directory. you can run it and plot the GMM in a 3D Coordinate System.
+```
+python sampling/sample.py
+```
+the result are as follows:
+<figure class="half">
+    <img src="images/1.png" alt="134">
+    <img src="images/mixture_gauss.png" alt="234">
+</figure>
+
+* [pulmonary_embolism.py](pulmonary_embolism.py) is the main file to train and validate P_Mask_RCNN.
+You can download pre-trained COCO weights (mask_rcnn_coco.h5) from the [releases page](https://github.com/matterport/Mask_RCNN/releases)
+to init the weight of the model.You can also use the [model](model/mask_rcnn_pulmonary.h5) we have pre-train to evaluate P_Mask_RCNN directly.
+
+To train your own model, run:
+```
+python pulmonary_embolism.py training --dataset="your dataset path" --model="your model weight file.h5"
+```
+To evaluate the model, run:
+```
+python pulmonary_embolism.py inference --dataset="your dataset path" --model="your model weight file.h5"
+```
+
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