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ParaTransCNN

This repo holds code for ParaTransCNN: Parallelized TransCNN Encoder for Medical Image Segmentation

  • Overview image

Requirements

We trained on NVIDIA RTX 3090, where python 3.9.10 and torch 1.12.1 on ubuntu 22.04.

We use the libraries of these versions:

  • Python 3.9.10
  • Torch 1.12.1+cu113
  • torchvision 0.13.1+cu113
  • numpy 1.21.5

You can pip the same experimental environment as us through requirements

pip install -r requirements.txt

Dataset preparation

  • Synapse Dataset: please go to "./datasets/README.md" for the details about preparing preprocessed Synapse dataset or download the Synapse Dataset from here.
  • AVT Dataset: please go to "./datasets/README.md" for the details about preparing preprocessed AVT dataset. The preprocessed dataset is here.

Train

  • Run the following code to train ParaTransCNN on the Synapse Dataset:
python train.py --dataset Synapse --train_path <your path to Synapse train dataset> --model_name ParaTransCNN --max_epochs 150 --batch_size 4 --base_lr 0.01
  • Run the following code to train ParaTransCNN on the AVT Dataset:
python train.py --dataset AVT --train_path <your path to AVT train dataset> --model_name ParaTransCNN --max_epochs 150 --batch_size 4 --base_lr 0.01

Test

  • Run the following code to test the trained ParaTransCNN on the Synapse Dataset:
python test.py --dataset Synapse --volume_path <your path to Synapse test dataset> --model_name ParaTransCNN --max_epochs 150 --batch_size 4 --base_lr 0.01
  • Run the following code to test the trained ParaTransCNN on the AVT Dataset:
python test.py --dataset AVT --volume_path <your path to AVT test dataset> --model_name ParaTransCNN --max_epochs 150 --batch_size 4 --base_lr 0.01

Results

  • Synapse

Evaluation metrics

Methods

DSC(%)

HD

Aorta

Gallbladder

Kidney(L)

Kidney(R)

Liver

Pancreas

Spleen

Stomach
DARR 69.77 - 74.74 53.77 72.31 73.24 94.08 54.18 89.90 45.96
R50 U-Net 74.68 36.87 87.74 63.66 80.60 78.19 93.74 56.90 85.87 74.16
U-Net 76.85 39.70 89.07 69.72 77.77 68.60 93.43 53.98 86.67 75.58
R50 Att-UNet 75.57 36.97 55.92 63.91 79.20 72.71 93.56 49.37 87.19 74.95
Att-UNet 77.77 36.02 89.55 68.88 77.98 71.11 93.57 58.04 87.30 75.75
R50 ViT 71.29 32.87 73.73 55.13 75.80 72.20 91.51 45.99 81.99 73.95
TransUnet 77.48 31.69 87.23 63.13 81.87 77.02 94.08 55.86 85.08 75.62
SwinUnet 79.13 21.55 85.47 66.53 83.28 79.61 94.29 56.58 90.66 76.60
TransDeepLab 80.16 21.25 86.04 69.16 84.08 79.88 93.53 61.19 89.00 78.40
HiFormer 80.39 14.70 86.21 65.69 85.23 79.77 94.61 59.52 90.99 81.08
MISSFormer 81.96 18.20 86.99 68.65 85.21 82.00 94.41 65.67 91.92 80.81
TransCeption 82.24 20.89 87.60 71.82 86.23 80.29 95.01 65.27 91.68 80.02
DAE-Former 82.43 17.46 88.96 72.30 86.08 80.88 94.98 65.12 91.94 79.19
ParaTransCNN 83.86 15.86 88.12 68.97 87.99 83.84 95.01 69.79 92.71 84.43

Visualization on Synapse

image

  • AVT

Visualization on AVT

image

  • ACDC

Visualization on ACDC

image

  • Kvasir_SEG

Evaluation metrics

Methods

Dice

Jaccard

Precision

Recall

U-Net 0.830530 0.748300 0.860328 0.858857
UNet++ 0.795231 0.705255 0.825769 0.840401
Att-UNet 0.828564 0.748071 0.848016 0.863609
TransUnet 0.869120 0.799637 0.895035 0.886673
SwinUnet 0.854450 0.777262 0.890461 0.862594
TransDeepLab 0.859171 0.779644 0.881949 0.883266
HiFormer 0.859615 0.786705 0.879861 0.884120
MISSFormer 0.715535 0.611769 0.760764 0.765871
TransCeption 0.773330 0.676522 0.801368 0.813837
DAE-Former 0.779659 0.680100 0.806010 0.807978
ParaTransCNN 0.882230 0.819137 0.895940 0.900321

Visualization on Kvasir_SEG

image

  • BUSI(benign & malignant)

Evaluation metrics

Methods

Dice

Jaccard

Precision

Recall

U-Net 0.779577 0.698415 0.795101 0.811817
UNet++ 0.751396 0.665028 0.774793 0.780807
Att-UNet 0.784430 0.701824 0.820558 0.792074
TransUnet 0.791364 0.711470 0.810834 0.822213
SwinUnet 0.781966 0.694188 0.822793 0.782219
TransDeepLab 0.778597 0.693664 0.799186 0.791463
HiFormer 0.779438 0.699652 0.795937 0.803998
MISSFormer 0.731416 0.633783 0.766812 0.754716
TransCeption 0.758622 0.660874 0.803228 0.765918
DAE-Former 0.733205 0.634020 0.775104 0.745468
ParaTransCNN 0.809358 0.729952 0.850570 0.804386

Visualization on BUSI

image

Reference