3D Swin transformer for partial medical auto segmentation Conference Paper
| Authors: | Rangnekar, A.; Jiang, J.; Veeraraghavan, H. |
| Title: | 3D Swin transformer for partial medical auto segmentation |
| Conference Title: | Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT, MICCAI Challenge (FLARE 2023) |
| Abstract: | Transformers are the highest accuracy segmentation frameworks in computer vision for natural imagery from the past few years. In contrast, medical imaging approaches, except a select few (for example, SwinUNETR and SMIT), are still dominated by the nnU-Net architecture family. In this paper, we investigate the application of a hierarchical vision transformer to the FLARE-23 challenge. Specifically, we benchmark our results using a relatively lightweight architecture, Swin-X Seg. We use multi-model self-training, wherein we use nnU-Net for predicting pseudo labels on partially labeled cases and then optimize the transformer architecture for memory requirements. Our network achieved the average DSC scores of 83.13 % and 35.19 % on the open validation set (50 cases) for organs and tumors, respectively, while staying under a max GPU memory utilization of 4GB at evaluation runtime. Our results show that there is potential for the transformer architecture to perform at par or better than conventional convolutional approaches, and we hope our findings encourage more research in the area. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. |
| Keywords: | medical imaging; memory requirements; network architecture; auto segmentation; high-accuracy; self-training; swin transformer; memory architecture; lightweight architecture; memory utilization; multi-modelling; net architecture; validation sets |
| Journal Title | Lecture Notes in Computer Science |
| Volume: | 14544 |
| Conference Dates: | 2023 Oct 8 |
| Conference Location: | Vancouver, Canada |
| ISBN: | 0302-9743 |
| Publisher: | Springer |
| Date Published: | 2024-01-01 |
| Start Page: | 222 |
| End Page: | 235 |
| Language: | English |
| DOI: | 10.1007/978-3-031-58776-4_18 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | This paper was published in a conference book with ISBN: 978-3-031-58775-7 -- Source: Scopus |
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