Abstract
Prostate MRI segmentation is critical for accurate diagnosis and treatment planning but remains challenging due to the complex interplay between the peripheral zone’s thin, irregular boundaries and the central gland’s homogeneous textures, compounded by variability across imaging protocols. To address these challenges, we propose ProSeg, a novel deep learning framework featuring a specialized ProSeg block that integrates dual complementary processes: (1) anisotropic convolutions for precise peripheral zone boundary delineation and (2) cross-slice attention mechanisms for robust central gland texture modeling. Extensive evaluations on the Promise12 and Promise158 datasets demonstrate ProSeg’s state-of-the-art performance, achieving Dice scores of 84.31% (peripheral zone) and 57.92% (central gland) on Promise12, and 83.15% (peripheral zone) and 56.38% (central gland) on Promise158, significantly outperforming existing methods. ProSeg’s consistent accuracy across diverse protocols highlights its clinical potential for reliable prostate zonal segmentation in real-world settings.
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Data availability
The data supporting the findings of this study are publicly available. The Promise12 dataset is available at: https://promise12.grand-challenge.org/. The Prostate158 dataset is available at:https://github.com/kbressem/prostate158.
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J.Q. and Y.Y. conceived the study. J.Q. developed the methodology, implemented the software, performed validation and formal analysis, conducted the investigation, curated the data, and prepared the original draft. Y.Y. supervised the project, administered the research, and edited the manuscript. All authors reviewed and approved the final manuscript.
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Qin, J., Yang, Y. ProSeg: multi-scale context fusion for high-precision prostate segmentation in MRI. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43589-3
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DOI: https://doi.org/10.1038/s41598-026-43589-3
