This is the official code and pre-trained weights for paper "Swin SMT: Global Sequential Modeling for Enhancing 3D Medical Image Segmentation" early accepted (top 11%) at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2024.
To the best of our knowledge, this is the first benchmark of TotalSegmentator-V2. The dataset consists od 1228 CT scans and 117 major anatomical structure segmentations. Following the original split, we train, validate and test model on 1082, 57, and 89 cases, respectively.
- [02.09.2024] The Swin SMT was selected for a Spotlight oral presentation 🎉
Recent advances in Vision Transformers (ViTs) have significantly enhanced medical image segmentation by facilitating the learning of global relationships. However, these methods face a notable challenge in capturing diverse local and global long-range sequential feature representations, particularly evident in whole-body CT (WBCT) scans. To overcome this limitation, we introduce Swin Soft Mixture Transformer (Swin SMT), a novel architecture based on Swin UNETR. This model incorporates a Soft Mixture-of-Experts (Soft MoE) to effectively handle complex and diverse long-range dependencies. The use of Soft MoE allows for scaling up model parameters maintaining a balance between computational complexity and segmentation performance in both training and inference modes. We evaluate Swin SMT on the publicly available TotalSegmentator-V2 dataset, which includes 117 major anatomical structures in WBCT images. Comprehensive experimental results demonstrate that Swin SMT outperforms several state-of-the-art methods in 3D anatomical structure segmentation, achieving an average Dice Similarity Coefficient of 85.09%.
Swin SMT can be easily integrated and used as a network for 3D segmentation tasks in any PyTorch project, especially in medical image segmentation. Usage:
import torch
from src.models.swin_smt import SwinSMT
x_input = torch.randn(1, 1, 128, 128, 128)
model = SwinSMT(
in_channels=1,
out_channels=118,
img_size=(128, 128, 128),
spatial_dims=3,
use_v2=True,
feature_size=48,
use_moe=True,
num_experts=4,
num_layers_with_moe=3
)
model(x_input)| Method | Params (M) | Time (s) | Organs | Vertebrae | Muscles | Ribs | Vessels | Overall ⬆ |
|---|---|---|---|---|---|---|---|---|
| UNETR [3] | 102.02 | 35 | 73.84 | 60.70 | 82.35 | 69.27 | 61.49 | 70.88 (*) |
| SwinUNETR-S [2] | 18.34 | 15 | 78.21 | 63.43 | 85.02 | 69.98 | 62.23 | 73.90 (*) |
| nnFormer [4] | 149.30 | 99 | 79.26 | 73.87 | 74.97 | 74.03 | 74.97 | 75.48 (*) |
| DiNTS [5] | 147.00 | 150 | 80.05 | 71.42 | 85.32 | 73.71 | 70.13 | 77.64 (*) |
| UNesT [6] | 87.30 | 45 | 80.75 | 71.93 | 86.43 | 72.79 | 69.61 | 77.70 (*) |
| 3D UX-Net [7] | 53.00 | 74 | 83.03 | 79.54 | 86.99 | 82.54 | 75.01 | 82.53 (*) |
| SwinUNETR-B [2] | 72.76 | 37 | 83.46 | 79.76 | 87.57 | 82.61 | 75.23 | 82.81 (*) |
| nnU-Net [8] | 370.74 | 300 | 82.02 | 82.89 | 86.98 | 85.27 | 75.51 | 83.44 (*) |
| SwinUNETR-L [2] | 290.40 | 145 | 83.26 | 82.02 | 87.99 | 83.82 | 75.60 | 83.59 (*) |
| 3D RepUX-Net [9] | 65.80 | 80 | 80.85 | 84.00 | 87.63 | 84.22 | 75.91 | 83.81 (*) |
| Universal Model [10] | 62.25 | 39 | 82.25 | 84.46 | 87.58 | 86.49 | 76.11 | 84.02 (*) |
| Swin SMT (ours) | 170.78 | 60 | 83.70 | 83.03 | 88.70 | 86.60 | 77.54 | 85.09 |
Due to the high number of classes, we decided to show qualitative results for selected organs.
  
Ground truth |
  
Swin SMT |
We trained Swin SMT on NVIDIA DGX server, equipped with 8 × NVIDIA A100 40GB GPUs.
ArXiv preprint can be found here.
If you find this repository useful, please consider citing this paper:
@inproceedings{plotka2024swin,
title={Swin SMT: Global Sequential Modeling for Enhancing 3D Medical Image Segmentation},
author={P{\l}otka, Szymon and Chrabaszcz, Maciej and Biecek, Przemyslaw},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={689--698},
year={2024},
organization={Springer}
}
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[8] Isensee, Fabian, et al. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation." Nature methods 18.2 (2021): 203-211.
[9] Lee, Ho Hin, et al. "Scaling up 3d kernels with bayesian frequency re-parameterization for medical image segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2023.
[10] Liu, Jie, et al. "Clip-driven universal model for organ segmentation and tumor detection." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.
[11] Puigcerver, Joan, et al. "From Sparse to Soft Mixtures of Experts." The Twelfth International Conference on Learning Representations.
