Instance Segmentation for Whole Slide Imaging: End-to-End or Detect-Then-Segment

This repository contains both code and data relating to the experimentation performed to better understand effective segmentation methodologies utilizing deep learning techniques that improve glomeruli characterization on high-resolution Whole Slide Imaging.

To Apply Our Model

1. Download the model and a test image from: Google Drive
2. Download the test image, provided in Google Drive.
3. Navigate to segment/automatic-detection/deep-prediction
4. Set up the enviornment as described below
5. !python train.py
6. Evaluate results in ../dsc-evaluation via !python train.py

Please let me know if you have any questions @ [email protected]

Summary:

This research project comprehensively analyzes several factors relating to semantic segmentation (image resolution, color space, and segmentation backbones), as well as proposes and compares a "detect-then-segment" framework against current conventional end-to-end segmentation ("Mask-RCNN") methods utilized in high-resolution WSI.

General Methodology

Our project may be considered in two different phases:

1. Segmentation on Manual Detection Results

• In this phase, we comprehensively analyze conventional semantic segmentation upon manually detected glomeruli images. Namely, we discover the effect of six distinct image resolutions, two segmentation backbones, and 2 color spaces.

2. Segmentation on Automatic Detection Results

• In this phase, we directly evaluate the performance of our detect-then-segment approach relative to a standard Mask-RCNN implementation.

Usage and Data:

Project Structure:

.
├── README.md
├── automatic-detection
│   ├── data
│   ├── deep-prediction
│   └── dsc-evaluation
│       ├── deeplab-evaluation
│       └── mask-rcnn-evaluation
└── manual-detection
    ├── data
    ├── dsc-evaluation
    ├── prediction-pipeline
    │   ├── deep-prediction
    │   └── u-net-prediction
    └── segmentation-pipeline
        ├── deeplab-segmentation
        └── u-net-segmentation

Data

1. Manual Detection Phase

   • For this phase, manually detected glomeruli in 512x512 is provided, as well as a U-Net model (.pth) file for the 128x128 resolution. Further, ground truth masks are provided for DSC evaluation.

2. Automatic Detection Phase

   • The data provided includes the original resolution glomeruli data (> 1000x1000), manually traced corresponding masks, and a DeepLab_v3 model file.

Usage

Detailed Instructors are also located in each subfolder of this repository.

1. Manual Detection Phase

   • We begin by:
     • cd into ./manual-detection
     • The segmentation-pipeline/ folder contains both U-Net and DeepLab_v3 code to produce respective model (.pth) files to perform and remember segmentation results upon a given input set. You may use this to perform your own segmentation.
     • The prediction-pipeline folder takes a set of input images and a model file to produce predicted images.
       • With the images provided in the data/ folder, as well as the model file, you may use this prediction pipeline (namely, the u-net-prediction/ folder) to reproduce the images used in the experiment.
     • Finally, to evaluate the performance of any segmentation model, simply use the dsc-evaluation/general-evaluation/ folder and input both ground truth and predicted masks to obtain a CSV file of data. Average the DSC values for each photo to obtain the results presented in this experiment (a value of 0.940 should be obtained).

2. Automatic Detection Phase

   • We begin by:
     • cd into ./automatic-detection
     • Then, the deep-prediction folder contains code that will allow for the production of predicted images based on input images and a model file You may use the input images and the model file provided.
     • With the model file, cd to dsc-evaluation/. Two subfolders are present here:
       • deeplab-evaluation/ will be used to evaluate DeepLab_v3 predicted images against the ground truth masks.
       • mask-rcnn-evaluation/ will be used to evaluate MaskRCNN predicted images against the ground truth masks.
       • We draw a distinction between these two types of evaluation due to differing image representation (e.g., .png vs .jpg).
     • You should be able to obtain a DSC of 0.953 from DeepLab_v3, and 0.902 from Mask-RCNN.

General Notes

• If using Colab, run the following set-up script: !pip uninstall imgaug && pip install git+https://github.com/aleju/imgaug.git && pip install -U PyYAML && pip install tensorboardx
• Use yaml config files to properly alter the variables in the experiment: color space and resolution.

Results:

Our findings show that our detect-then-segment pipeline, with the DeepLab_v3 segmentation framework operating on a previously detected glomeruli of 512x512 resolution, achieved a 0.953 dice similarity coefficient (DSC), compared with a 0.902 DSC from the end-to-end Mask-RCNN pipeline.

See Also:

Mask-RCNN Implementation
U-Net Implementation
DeepLab_v3 Implementation