[2208.05772] KiPA22 Report: U-Net with Contour Regularization for Renal Structures Segmentation
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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2208.05772 (eess)
[Submitted on 10 Aug 2022 (v1), last revised 6 Sep 2022 (this version, v2)]

Title:KiPA22 Report: U-Net with Contour Regularization for Renal Structures Segmentation

Authors:Kangqing Ye, Peng Liu, Xiaoyang Zou, Qin Zhou, Guoyan Zheng
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Abstract:Three-dimensional (3D) integrated renal structures (IRS) segmentation is important in clinical practice. With the advancement of deep learning techniques, many powerful frameworks focusing on medical image segmentation are proposed. In this challenge, we utilized the nnU-Net framework, which is the state-of-the-art method for medical image segmentation. To reduce the outlier prediction for the tumor label, we combine contour regularization (CR) loss of the tumor label with Dice loss and cross-entropy loss to improve this phenomenon.
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2208.05772 [eess.IV]
  (or arXiv:2208.05772v2 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2208.05772

Submission history

From: Kangqing Ye [view email]
[v1] Wed, 10 Aug 2022 15:07:20 UTC (3,677 KB)
[v2] Tue, 6 Sep 2022 09:08:01 UTC (3,653 KB)
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