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The repository contains the PyTorch implementation of "Duplex Contextual Relations for PolypSegmentation"

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Duplex Contextual Relations for Polyp Segmentation

Introduction

The repository contains the PyTorch implementation of "Duplex Contextual Relations for PolypSegmentation"

Overview

1. Framework


Figure 1: Overview of our proposed DCRNet.

2. Quantitative Results


Figure 2: Qualitative results.

3. Qualitative Results


Figure 3: Quantitative results on EndoScene dataset.


Figure 4: Quantitative results on Kvasir-SEG dataset.


Figure 5: Quantitative results on PICCOLO dataset.

Usage

1. Prerequisite environment

  1. torch>=1.5.0

  2. torchvision>=0.6.0

  3. tqdm

  4. scipy

  5. scikit-image

  6. PIL

  7. numpy

  8. CUDA

2. Dataset downloading

  • Downloading the CVC-EndoSceneStill dataset, which can be found in this Google Drive link
  • Downloading the Kvasir-SEG dataset, which can be found in this Google Drive link
  • To access the PICCOLO dataset, please visit here

3. Train

  • Assign your customized path of --train_path ,--save_root and --gpu in Train.py.
  • Run python Train.py

4. Test

  • Assign the --pth_path , --data_root , --save_root and --gpu in Test.py.
  • Run python Test.py
  • The quantitative results will be displayed in your screen, and the qualitative results will be saved in your customized path.

5. Evaluate

  • The evaluation code is stored in ./utils/eval.py
  • You can replace it with your customized evaluation metrics.

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The repository contains the PyTorch implementation of "Duplex Contextual Relations for PolypSegmentation"

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