# Multi-dimension unified Swin Transformer for 3D Lesion Segmentation in Multiple Anatomical Locations

arXiv: https://arxiv.org/abs/2309.01823 · HTML: https://mlsanity.com/paper/2309.01823/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 4 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [A Progressively-trained Scale-invariant and Boundary-aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions](https://mlsanity.com/paper/1811.04437/) (2018)
- [A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials](https://mlsanity.com/paper/2003.02943/) (2020)

## Built on by (most cited first, 1 of 1)

- [GauSSmart: Enhanced 3D Reconstruction through 2D Foundation Models and Geometric Filtering](https://mlsanity.com/paper/2510.14270/) (2025)

## Abstract (arXiv)

In oncology research, accurate 3D segmentation of lesions from CT scans is essential for the modeling of lesion growth kinetics. However, following the RECIST criteria, radiologists routinely only delineate each lesion on the axial slice showing the largest transverse area, and delineate a small number of lesions in 3D for research purposes. As a result, we have plenty of unlabeled 3D volumes and labeled 2D images, and scarce labeled 3D volumes, which makes training a deep-learning 3D segmentation model a challenging task. In this work, we propose a novel model, denoted a multi-dimension unified Swin transformer (MDU-ST), for 3D lesion segmentation. The MDU-ST consists of a Shifted-window transformer (Swin-transformer) encoder and a convolutional neural network (CNN) decoder, allowing it to adapt to 2D and 3D inputs and learn the corresponding semantic information in the same encoder. Based on this model, we introduce a three-stage framework: 1) leveraging large amount of unlabeled 3D lesion volumes through self-supervised pretext tasks to learn the underlying pattern of lesion anatomy in the Swin-transformer encoder; 2) fine-tune the Swin-transformer encoder to perform 2D lesion segmentation with 2D RECIST slices to learn slice-level segmentation information; 3) further fine-tune the Swin-transformer encoder to perform 3D lesion segmentation with labeled 3D volumes. The network's performance is evaluated by the Dice similarity coefficient (DSC) and Hausdorff distance (HD) using an internal 3D lesion dataset with 593 lesions extracted from multiple anatomical locations. The proposed MDU-ST demonstrates significant improvement over the competing models. The proposed method can be used to conduct automated 3D lesion segmentation to assist radiomics and tumor growth modeling studies. This paper has been accepted by the IEEE International Symposium on Biomedical Imaging (ISBI) 2023.