An evaluation of U-Net in Renal Structure Segmentation

2022/09/06 by Haoyu Wang, Ziyan Huang, Wang, Haoyu +17
Engineering · Medicine · #Advanced X-ray and CT Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Renal and Vascular Pathologies #Renal cell carcinoma treatment #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2209.02247

openalex publication_date 2022/09/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Renal structure segmentation from computed tomography angiography~(CTA) is essential for many computer-assisted renal cancer treatment applications. Kidney PArsing~(KiPA 2022) Challenge aims to build a fine-grained multi-structure dataset and improve the segmentation of multiple renal structures. Recently, U-Net has dominated the medical image segmentation. In the KiPA challenge, we evaluated several U-Net variants and selected the best models for the final submission.

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