CTI-Unet: Cascaded Threshold Integration for Improved U-Net Segmentation of Pathology Images

• URL: http://arxiv.org/abs/2504.05640v1
• Date: Tue, 08 Apr 2025 03:35:09 GMT
• Title: CTI-Unet: Cascaded Threshold Integration for Improved U-Net Segmentation of Pathology Images
• Authors: Mingyang Zhu, Yuqiu Liang, Jiacheng Wang,
• Abstract summary: Chronic kidney disease (CKD) is a growing global health concern, necessitating precise and efficient image analysis to aid diagnosis and treatment planning.<n>This paper proposes a novel textitd Threshold-Integrated U-Net (CTI-Unet) to overcome the limitations of single-threshold segmentation.
• Score: 4.223102602534721
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chronic kidney disease (CKD) is a growing global health concern, necessitating precise and efficient image analysis to aid diagnosis and treatment planning. Automated segmentation of kidney pathology images plays a central role in facilitating clinical workflows, yet conventional segmentation models often require delicate threshold tuning. This paper proposes a novel \textit{Cascaded Threshold-Integrated U-Net (CTI-Unet)} to overcome the limitations of single-threshold segmentation. By sequentially integrating multiple thresholded outputs, our approach can reconcile noise suppression with the preservation of finer structural details. Experiments on the challenging KPIs2024 dataset demonstrate that CTI-Unet outperforms state-of-the-art architectures such as nnU-Net, Swin-Unet, and CE-Net, offering a robust and flexible framework for kidney pathology image segmentation.

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