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An evaluation of U-Net in Renal Structure Segmentation

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arxiv 2209.02247 v1 pith:YS5VZLRP submitted 2022-09-06 eess.IV cs.CVcs.LG

Haoyu Wang , Ziyan Huang , Jin Ye , Can Tu , Yuncheng Yang , Shiyi Du , Zhongying Deng , Chenglong Ma
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Jingqi Niu Junjun He
This is my paper · ORCID
classification eess.IVcs.CVcs.LG
keywords renalsegmentationu-netchallengekipastructureaimsangiography
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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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  1. Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg

    cs.CV 2025-02 conditional novelty 4.0 of 10

    FH-Seg, a U-Net variant with full-scale skip connections and hierarchical soft attention, reports higher Dice and F1 scores than prior methods on a new 16,212-image renal vasculature dataset.

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