This repo holds code for Boundary Difference Over Union Loss For Medical Image Segmentation(MICCAI 2023).
- Get models in this link: R50-ViT-B_16, ViT-B_16, ViT-L_16...
wget https://storage.googleapis.com/vit_models/imagenet21k/{MODEL_NAME}.npz &&
mkdir ../model/vit_checkpoint/imagenet21k &&
mv {MODEL_NAME}.npz ../model/vit_checkpoint/imagenet21k/{MODEL_NAME}.npzYou can follow TransUnet to get and prepare the datasets.
- The directory structure of the whole project is as follows:
.
├── TransUNet
│ └──
├── model
│ └── vit_checkpoint
│ └── imagenet21k
│ ├── R50+ViT-B_16.npz
│ └── *.npz
├── Synapse
│ ├── test
│ │ ├── case0001.npy.h5
│ │ └── *.npy.h5
│ ├── train
│ │ ├── case0005_slice000.npz
│ │ └── *.npz
│ └── lists_Synapse
│ ├── all.lst
│ ├── test.txt
│ └── train.txt
└── ACDC
└── ...(same as Synapse)Please prepare an environment with python=3.7, and then use the command "pip install -r requirements.txt" for the dependencies.
- For Synapse dataset
- train
CUDA_VISIBLE_DEVICES=0 python train.py --dataset Synapse --vit_name R50-ViT-B_16- test
CUDA_VISIBLE_DEVICES=0 python test.py --dataset Synapse --vit_name R50-ViT-B_16 --is_savenii- For ACDC dataset
- train
CUDA_VISIBLE_DEVICES=0 python train.py --dataset ACDC --vit_name R50-ViT-B_16- test
CUDA_VISIBLE_DEVICES=0 python test.py --dataset ACDC --vit_name R50-ViT-B_16 --is_saveniiOur results were trained and tested using five different seeds, with the final results being the average of the five runs. The seed settings and results for each run are shown in the table below. For example, for the ACDC dataset, we have
| Seed | Loss | mean dice | mean hd95 | boundary IoU |
|---|---|---|---|---|
| 1234 | Boundary DoU | 91.40 | 2.20 | 78.71 |
| 1111 | Boundary DoU | 91.22 | 2.41 | 78.04 |
| 2222 | Boundary DoU | 91.16 | 2.08 | 78.75 |
| 3333 | Boundary DoU | 91.41 | 2.00 | 78.33 |
| 4444 | Boundary DoU | 91.30 | 2.16 | 78.47 |
| mean | Boundary DoU | 91.30 | 2.17 | 78.46 |
In the TransUNet model, the impact of seed selection on the results varies for different datasets, and different seeds can be tried for better results.