通过级联阈值融合提升病理图像分割精度，解决传统方法调参难问题。
CTI-Unet: Cascaded Threshold Integration for Improved U-Net Segmentation of Pathology Images
- 分步融合多阈值输出，平衡去噪与细节保留
- 在KPIs2024数据集上超越nnU-Net等主流模型
- 适合需要高精度病理分割的临床研究者
慢性肾病（CKD）是全球日益严峻的健康问题，亟需精准高效的图像分析以辅助诊断与治疗规划。自动化肾脏病理图像分割在推动临床流程中起核心作用，但传统分割模型常需精细调参。本文提出新型级联阈值集成U-Net（CTI-Unet），克服单阈值分割的局限性。通过顺序整合多个阈值输出，该方法可有效协调噪声抑制与细微结构保持。在具有挑战性的KPIs2024数据集上的实验表明，CTI-Unet优于nnU-Net、Swin-Unet和CE-Net等先进架构，为肾脏病理图像分割提供了一种鲁棒且灵活的解决方案。
原文摘要 · Abstract (English)
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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