CTI-Unet：用于改进病理图像U-Net分割的级联阈值集成
CTI-Unet: Cascaded Threshold Integration for Improved U-Net Segmentation of Pathology Images
AI总结：
本文提出CTI-Unet，通过级联集成多个阈值化输出克服单阈值分割局限，在KPIs2024数据集上优于nnU-Net、Swin-Unet和CE-Net，实现肾脏病理图像的稳健分割。
摘要：
慢性肾脏病（CKD）是一个日益严重的全球健康问题，需要精确高效的图像分析来辅助诊断和治疗规划。肾脏病理图像的自动分割在促进临床工作流程中发挥着核心作用，然而传统分割模型通常需要精细的阈值调节。本文提出了一种新颖的级联阈值集成U-Net（CTI-Unet），以克服单阈值分割的局限性。通过顺序集成多个阈值化输出，我们的方法能够在噪声抑制与保留更精细结构细节之间取得平衡。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等最先进的架构，为肾脏病理图像分割提供了一个稳健且灵活的框架。
英文摘要：
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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