CIS-UNet: Multi-class segmentation of the aorta in computed tomography angiography via context-aware shifted window self-attention.

Autor: Imran M; Department of Medicine, University of Florida, Gainesville, FL 32611, USA., Krebs JR; Department of Surgery, University of Florida, Gainesville, FL 32611, USA., Gopu VRR; Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA., Fazzone B; Department of Surgery, University of Florida, Gainesville, FL 32611, USA., Sivaraman VB; Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA., Kumar A; Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA., Viscardi C; Department of Surgery, University of Florida, Gainesville, FL 32611, USA., Heithaus RE; Department of Radiology, University of Florida, Gainesville, FL 32611, USA., Shickel B; Department of Medicine, University of Florida, Gainesville, FL 32611, USA., Zhou Y; Department of Computer Science and Engineering, University of California, Santa Cruz, CA 95064, USA., Cooper MA; Department of Surgery, University of Florida, Gainesville, FL 32611, USA., Shao W; Department of Medicine, University of Florida, Gainesville, FL 32611, USA. Electronic address: weishao@ufl.edu.
Jazyk: angličtina
Zdroj: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society [Comput Med Imaging Graph] 2024 Nov 19; Vol. 118, pp. 102470. Date of Electronic Publication: 2024 Nov 19.
DOI: 10.1016/j.compmedimag.2024.102470
Abstrakt: Advancements in medical imaging and endovascular grafting have facilitated minimally invasive treatments for aortic diseases. Accurate 3D segmentation of the aorta and its branches is crucial for interventions, as inaccurate segmentation can lead to erroneous surgical planning and endograft construction. Previous methods simplified aortic segmentation as a binary image segmentation problem, overlooking the necessity of distinguishing between individual aortic branches. In this paper, we introduce Context-Infused Swin-UNet (CIS-UNet), a deep learning model designed for multi-class segmentation of the aorta and thirteen aortic branches. Combining the strengths of Convolutional Neural Networks (CNNs) and Swin transformers, CIS-UNet adopts a hierarchical encoder-decoder structure comprising a CNN encoder, a symmetric decoder, skip connections, and a novel Context-aware Shifted Window Self-Attention (CSW-SA) module as the bottleneck block. Notably, CSW-SA introduces a unique adaptation of the patch merging layer, distinct from its traditional use in the Swin transformers. CSW-SA efficiently condenses the feature map, providing a global spatial context, and enhances performance when applied at the bottleneck layer, offering superior computational efficiency and segmentation accuracy compared to the Swin transformers. We evaluated our model on computed tomography (CT) scans from 59 patients through a 4-fold cross-validation. CIS-UNet outperformed the state-of-the-art Swin UNetR segmentation model by achieving a superior mean Dice coefficient of 0.732 compared to 0.717 and a mean surface distance of 2.40 mm compared to 2.75 mm. CIS-UNet's superior 3D aortic segmentation offers improved accuracy and optimization for planning endovascular treatments. Our dataset and code will be made publicly available at https://github.com/mirthAI/CIS-UNet.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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