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Weng X, Song F, Tang M, Wang K, Zhang Y, Miao Y, Chan LWC, Lei P, Hu Z, Yang F. MDM-U-Net: A novel network for renal cancer structure segmentation. Comput Med Imaging Graph 2023; 109:102301. [PMID: 37738774 DOI: 10.1016/j.compmedimag.2023.102301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 07/27/2023] [Accepted: 09/08/2023] [Indexed: 09/24/2023]
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.
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Affiliation(s)
- Xin Weng
- School of Biology & Engineering (School of Modern Industry for Health and Medicine), Guizhou Medical University, Guiyang, Guizhou, China
| | - Fasong Song
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China
| | - Maowen Tang
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China
| | - Kansui Wang
- Department of Radiology, The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China
| | - Yusui Zhang
- Department of Radiology, The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China
| | - Yuehong Miao
- School of Biology & Engineering (School of Modern Industry for Health and Medicine), Guizhou Medical University, Guiyang, Guizhou, China
| | - Lawrence Wing-Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
| | - Zuquan Hu
- School of Biology & Engineering (School of Modern Industry for Health and Medicine), Guizhou Medical University, Guiyang, Guizhou, China; Immune Cells and Antibody Engineering Research Center in University of Guizhou Province, Key Laboratory of Biology and Medical Engineering, Guizhou Medical University, Guiyang, Guizhou, China.
| | - Fan Yang
- School of Biology & Engineering (School of Modern Industry for Health and Medicine), Guizhou Medical University, Guiyang, Guizhou, China.
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