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

• URL: http://arxiv.org/abs/2309.01823v1
• Date: Mon, 4 Sep 2023 21:24:00 GMT
• Title: Multi-dimension unified Swin Transformer for 3D Lesion Segmentation in Multiple Anatomical Locations
• Authors: Shaoyan Pan, Yiqiao Liu, Sarah Halek, Michal Tomaszewski, Shubing Wang, Richard Baumgartner, Jianda Yuan, Gregory Goldmacher, Antong Chen
• Abstract summary: We propose a novel model, denoted a multi-dimension unified Swin transformer (MDU-ST) for 3D lesion segmentation. The network's performance is evaluated by the Dice similarity coefficient (DSC) and Hausdorff distance (HD) using an internal 3D lesion dataset. The proposed method can be used to conduct automated 3D lesion segmentation to assist radiomics and tumor growth modeling studies.
• Score: 1.7413461132662074
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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