HDoGReg: Segmentation and Grouping of Microcalcifications in 2D Mammography With Hessian Difference of Gaussians and Regression

This repository contains the code for our paper.

TODO:Paper in citation format

Abstract

Accurate microcalcification (MC) segmentation in 2D full-field digital mammography has shown to benefit computerized diagnosis of breast cancer. However, the automated identification and segmentation of microcalcifications remains a challenging task with high false positive rate. We present Hessian difference-of-Gaussians regression (HDoGReg), a two stage multi-scale approach to MC segmentation. Candidate high optical density objects are first delineated using blob detection and Hessian analysis. A regression convolutional network, trained to output a function with higher response near MCs, chooses the objects which constitute actual MCs. The method is trained and validated on 435 mammograms from two separate datasets. HDoGReg achieved a mean intersection over the union of 0.670$\pm$0.121 per image, intersection over the union per MC object of 0.607$\pm$0.250 and true positive rate of 0.75 at 0.4 false positive detections per $cm^2$. The results of HDoGReg perform better when compared to state-of-the-art MC segmentation and detection methods.

Approach

HDoGReg: Approach for segmenting individual MCs. Although the segmentation is performed on whole mammograms, for visualization purposes only a patch is being shown. In the upper branch, blob segmentation is per- formed to segment bright blob-like and tubular structures. In the lower branch, a regression convolutional neural network gives a continuous function with higher response close to MCs. A threshold is then applied to segment individ- ual MCs. The output from the two branches is combined based on an overlap criterion, resulting to the final segmentation mask.

Results

Here are some segmentation examples applying HDoGReg and the comparison method. For visualization purposes we present 256x256 sized patches with the single-pixel reference annotation objects dilated. From left to right: (a) Images; (b) Reference Annotation; (c) HDoGReg Predicted Segmentation; (d) Ciecholewski (2017) Segmentation. The three top patches are from INbreast data and the rest are our data.

Requirements & Containers

Follow the Docker Instructions to create and run a suitable container.

Preparing your data

The dataset should have the following directory structure. A specific format for the image file names does not apply.

dataset_directory/
├── images
│   └── png16
│       ├── image1.png
│       └── mask1.png
│           ...
└── other
    ├── test.csv
    ├── train.csv
    └── val.csv

The CSV files point to the files and should have the following structure:

train.csv

 , full_image, mask_image
0, image1, mask1
        ...

A sample dataset can be found in

Running the code

1. Follow the Docker Instructions to create and run a container.
2. Download Sample Data and Trained Models

python download_sample_data.py -target_dir './data/'
python models/download_trained_models.py

3. Run training

./pipeline_train.sh

4. Run inference (pretrained model can be used)

./pipeline_infer.sh

5. Run inference with other baseline methods

./pipeline_other_methods.sh

6. Run evaluation (should be run after inferance)

./pipeline_evaluate.sh

During model evaluation results are saved in MLflow under the directory mlruns/. To access results run

mlflow ui

and view at http://localhost:5000. For further instructions on how to use MLflow look at the official documentation mlflow.org/docs/latest/quickstart.html

Tutorials

• Tutorial: Individual components of HDoGReg are applied to produce MC segmentation