🔥 NEWS 🔥 The full paper is available: CaraNet

We use Res2Net as our backbone.

We choose our CFP module as context module, and choose the dilation rate is 8. For the details of CFP module you can find here: CFPNet. The architecture of CFP module as shown in following figure:

As shown in architecture of CaraNet, the Axial Reverse Attention (A-RA) module contains two routes: 1) Reverse attention; 2) Axial-attention (The code of axial attention is applied from UACANET)

• Enviroment: Python 3.6;
• Install some packages:

conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch

conda install opencv-python pillow numpy matplotlib

• Clone this repository

git clone https://github.com/AngeLouCN/CaraNet

• Download the training and testing dataset from this link: Experiment Dataset
• Change the --train_path & --test_path in Train.py
• Run Train.py
• Testing dataset is ordered as follow:

|-- TestDataset
|   |-- CVC-300
|   |   |-- images
|   |   |-- masks
|   |-- CVC-ClinicDB
|   |   |-- images
|   |   |-- masks
|   |-- CVC-ColonDB
|   |   |-- images
|   |   |-- masks
|   |-- ETIS-LaribPolypDB
|   |   |-- images
|   |   |-- masks
|   |-- Kvasir
|       |-- images
|       |-- masks

• Change the data_path in Test.py

• Change the image_root and gt_root in eval_Kvasir.py
• You can also run the matlab code in eval fold, it contains other four measurement metrics results.
• You can download the segmentation maps of CaraNet from this link: CaraNet
• dice_average.m is to compute the averaged dice values according to sizes of objects, for small area analysis.

• Polyp Segmentation Results

• Conditions of test datasets:

• Small polyp analysis

The x-axis is the proportion size (%) of polyp; y-axis is the average mean dice coefficient.

• Dataset

BraTS input	Segmentation truth

• Results

• Small tumor analysis

For very small areas (<1%):

The difference between results of CaraNet and PraNet:

@inproceedings{lou2021caranet,
author = {Ange Lou and Shuyue Guan and Hanseok Ko and Murray H. Loew},
title = {{CaraNet: context axial reverse attention network for segmentation of small medical objects}},
volume = {12032},
booktitle = {Medical Imaging 2022: Image Processing},
organization = {International Society for Optics and Photonics},
publisher = {SPIE},
pages = {81 -- 92},
year = {2022},
doi = {10.1117/12.2611802}}

@inproceedings{9506485,
  author={Lou, Ange and Loew, Murray},
  booktitle={2021 IEEE International Conference on Image Processing (ICIP)}, 
  title={CFPNET: Channel-Wise Feature Pyramid For Real-Time Semantic Segmentation}, 
  year={2021},
  volume={},
  number={},
  pages={1894-1898},
  doi={10.1109/ICIP42928.2021.9506485}}