Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder
This model represents a Medical Image Segmentation Transformer (MIST) with a Convolutional Attention Mixing (CAM) decoder for medical image segmentation. MIST has two parts - a pre-trained multi-axis vision transformer (MaxViT) is used as an encoder (left side of the network), and the decoder that generates the segmentation maps (right side). Each block of the decoder includes an attention-mixing strategy where attentions computed at different stages are aggregated.
- Convolutional projected multi-head self-attention (MSA) are used instead of linear MSA to reduce computational cost and capture more salient features.
- Depth-wise (deep and shallow) convolutions (DWC and SWC) are incorporated to extract relevant semantic features and to increase kernel receptive field for better long-range dependency.
- loguru
- tqdm
- pyyaml
- pandas
- matplotlib
- scikit-learn
- scikit-image
- scipy
- opencv-python
- seaborn
- albumentations
- tabulate
- warmup-scheduler
- torch==1.11.0+cu113
- torchvision==0.12.0+cu113
- mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html
- timm
- einops
- pthflops
- torchsummary
- thop
This study uses the Automatic Cardiac Diagnosis Challenge (ACDC) and Synapse multi-organ datasets to evaluate the performance of MIST architecture. You can access ACDC dataset through https://www.creatis.insa-lyon.fr/Challenge/acdc/ and download Synapse dataset through https://www.synapse.org/\#!Synapse:syn3193805/wiki/217789.
Results on ACDC Dataset
| Models | Mean DICE | Right Ventricle | Myocardium | Left Ventricle |
|---|---|---|---|---|
| TransUNet | 89.71 | 88.86 | 84.53 | 95.73 |
| SwinUNet | 90.00 | 88.55 | 85.62 | 95.83 |
| MT-UNet | 90.43 | 86.64 | 89.04 | 95.62 |
| MISSFormer | 90.86 | 89.55 | 88.04 | 94.99 |
| PVT-CASCADE | 91.46 | 88.90 | 89.97 | 95.50 |
| nnUNet | 91.61 | 90.24 | 89.24 | 95.36 |
| TransCASCADE | 91.63 | 89.14 | 90.25 | 95.50 |
| nnFormer | 91.78 | 90.22 | 89.53 | 95.59 |
| Parallel MERIT | 92.32 | 90.87 | 90.00 | 96.08 |
| MIST (Proposed) | 92.56 | 91.23 | 90.31 | 96.14 |
Results on Synapse Dataset
| Models | Mean DICE | Mean HD95 | Aorta | GB | KL | KR | Liver | PC | SP | SM |
|---|---|---|---|---|---|---|---|---|---|---|
| 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 |
| MT-UNet | 78.59 | 26.59 | 87.92 | 64.99 | 81.47 | 77.29 | 93.06 | 59.46 | 87.75 | 76.81 |
| MISSFormer | 81.96 | 18.20 | 86.99 | 68.65 | 85.21 | 82.00 | 94.41 | 65.67 | 91.92 | 80.81 |
| PVT-CASCADE | 81.06 | 20.23 | 83.01 | 70.59 | 82.23 | 80.37 | 94.08 | 64.43 | 90.1 | 83.69 |
| CASTformer | 82.55 | 22.73 | 89.05 | 67.48 | 86.05 | 82.17 | 95.61 | 67.49 | 91.00 | 81.55 |
| TransCASCADE | 82.68 | 17.34 | 86.63 | 68.48 | 87.66 | 84.56 | 94.43 | 65.33 | 90.79 | 83.52 |
| Parallel MERIT | 84.22 | 16.51 | 88.38 | 73.48 | 87.21 | 84.31 | 95.06 | 69.97 | 91.21 | 84.15 |
| MIST (Proposed) | 86.92 | 11.07 | 89.15 | 74.58 | 93.28 | 92.54 | 94.94 | 72.43 | 92.83 | 87.23 |
The results for ACDC (upper row) and synapse dataset (lower row) are shown in the following image.
If this repository helped your works, please cite paper below:
Please contact Md Motiur Rahman at [email protected] for any query.
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