CTI-Unet: Cascaded Threshold Integration for Improved U-Net Segmentation of Pathology Images
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| Format: | Preprint |
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2025
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| _version_ | 1866913783010557952 |
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| author | Zhu, Mingyang Liang, Yuqiu Wang, Jiacheng |
| author_facet | Zhu, Mingyang Liang, Yuqiu Wang, Jiacheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05640 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CTI-Unet: Cascaded Threshold Integration for Improved U-Net Segmentation of Pathology Images Zhu, Mingyang Liang, Yuqiu Wang, Jiacheng Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | CTI-Unet: Cascaded Threshold Integration for Improved U-Net Segmentation of Pathology Images |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.05640 |