MDM-U-Net: A novel network for renal cancer structure segmentation

Xin Weng, Fasong Song, Maowen Tang, Kansui Wang, Yusui Zhang, Yuehong Miao, Lawrence Wing Chi Chan, Pinggui Lei, Zuquan Hu, Fan Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Accurate segmentation of the renal cancer structure, including the kidney, renal tumors, veins, and arteries, has great clinical significance, which can assist clinicians in diagnosing and treating renal cancer. For accurate segmentation of the renal cancer structure in contrast-enhanced computed tomography (CT) images, we proposed a novel encoder-decoder structure segmentation network named MDM-U-Net comprising a multi-scale anisotropic convolution block, dual activation attention block, and multi-scale deep supervision mechanism. The multi-scale anisotropic convolution block was used to improve the feature extraction ability of the network, the dual activation attention block as a channel-wise mechanism was used to guide the network to exploit important information, and the multi-scale deep supervision mechanism was used to supervise the layers of the decoder part for improving segmentation performance. In this study, we developed a feasible and generalizable MDM-U-Net model for renal cancer structure segmentation, trained the model from the public KiPA22 dataset, and tested it on the KiPA22 dataset and an in-house dataset. For the KiPA22 dataset, our method ranked first in renal cancer structure segmentation, achieving state-of-the-art (SOTA) performance in terms of 6 of 12 evaluation metrics (3 metrics per structure). For the in-house dataset, our method achieves SOTA performance in terms of 9 of 12 evaluation metrics (3 metrics per structure), demonstrating its superiority and generalization ability over the compared networks in renal structure segmentation from contrast-enhanced CT scans.

Original languageEnglish
Article number102301
JournalComputerized Medical Imaging and Graphics
Volume109
DOIs
Publication statusPublished - Oct 2023

Keywords

  • Contrast-enhanced CT
  • Dual activation attention block
  • Multi-scale anisotropic convolution block
  • Multi-scale deep supervision mechanism
  • Renal cancer structure segmentation

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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