This system is implemented using a data-centric abstraction model. There is a set of classes responsible for the analysis of data, a set of classes responsible for input & output of progress and data, and a pair of classes responsible for managing the overall workflow. The following classes are implemented, with the indicated dependencies on one another:
DishScorer executes the scoring of DISH in given DEXA images. It applies ML models using stored instances of TfObjectDetector and TfClassifier, and it interprets the results of those analyses, executing all augmentation options.
TfObjectDetector applies a stored tensorflow object-detection model to an image, returning a series of confidence-scored Box instances.
Box records the edges off a box on an x-y plane. Instances can also carry score values for themselves, as well as other arbitrary data ('labels').
TfClassifier applies a stored tensorflow image-classification model to an image, returning an instance of TfClassResult.
TfClassResult records the confidence scores assigned to each of a set of competing classes, as is output by an image-classification ML model.
ImgLister defines an interface for reading from a set of images (in this case, DEXA spines) to be analyzed. Two implementing classes are provided: ImgDirLister iterates through all image files in a given directory, while ImgFileLister iterates through a set of image files listed in a text file (allowing the image files to be distributed across many directories).
ProgressWriter defines a listener interface for reporting progress of the DISH scoring tool across a data set. Two implementing classes are provided: DotWriter prints dots to the shell as progress is made, while NullDotWriter does nothing (this allows results printed to stdout to be uncluttered by progress reports).
ImgDirScorer executes DISH scoring across a series of DEXA images, defined by its stored ImgLister instance.
PerformanceAnalyzer executes DISH scoring across a series of images stored in an annotation file (listing a score for each image). Rather than output those scores, it prints the results of a statistical analysis of the correlative relationship between the given & determined values.
Additional documentation is found within the code itself.
In addition to the source code, this pipeline requires two tensorflow
saved-model files (.pb) and accompanying
label files. These represent the ML models that are described in
the methods documentation.
In the table below, for each ML model, the model & corresponding label file are indicated, with a brief description of the model's purpose:
| Model File | Label File | Purpose |
|---|---|---|
| bridgeDetectorModel.pb | bridgeDetectorLabels.pbtxt | Object detector of anterior side of gaps between adjacent vertebrae. |
| bridgeScoreModel.pb | bridgeScoreLabels.txt | Image classification model for identifying the extent of hyperostosis for a given gap between vertebrae. |
See input/output descriptions in the usage instructions.