AI RESEARCH
Improving Prostate Gland Segmentation Using Transformer based Architectures
arXiv CS.LG
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ArXi:2506.14844v2 Announce Type: replace-cross Inter reader variability and cross site domain shift challenge the automatic segmentation of prostate anatomy using T2 weighted MRI images. This study investigates whether transformer models can retain precision amid such heterogeneity. We compare the performance of UNETR and SwinUNETR in prostate gland segmentation against our previous 3D UNet model, based on 546 MRI (T2weighted) volumes annotated by two independent experts. Three