将肺血管连接图作为解剖先验知识,用于基于深度学习的肺叶分割。

IF 1.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Journal of Medical Imaging Pub Date : 2024-07-01 Epub Date: 2024-07-09 DOI:10.1117/1.JMI.11.4.044001
Simone Bendazzoli, Emelie Bäcklin, Örjan Smedby, Birgitta Janerot-Sjoberg, Bryan Connolly, Chunliang Wang
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引用次数: 0

摘要

目的:我们的研究探讨了将先前的解剖学知识纳入深度学习(DL)方法的潜在益处,该方法旨在自动分割胸部 CT 扫描中的肺叶:我们介绍了一种基于深度学习的自动方法,该方法利用肺血管系统的解剖信息来指导和增强分割过程。这需要利用肺血管连接图(LVC),该图编码了相关的肺血管解剖数据。我们的研究探索了 nnU-Net 框架内三种不同神经网络架构的性能:独立 U-Net、多任务 U-Net 和级联 U-Net:实验结果表明,将 LVC 信息纳入 DL 模型可提高分割准确性,尤其是在具有挑战性的胸部 CT 容量边界区域。此外,我们的研究还证明了 LVC 增强模型泛化能力的潜力。最后,通过对 10 例 COVID-19 肺叶的分割评估了该方法的鲁棒性,证明了它在肺部疾病中的适用性:结论:将 LVC 等先验解剖信息纳入 DL 模型有望提高分割性能,尤其是在边界区域。结论:将 LVC 等先验解剖信息纳入 DL 模型有望提高分割性能,尤其是在边界区域。然而,这种提高的程度存在局限性,因此需要进一步探索其实际应用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lung vessel connectivity map as anatomical prior knowledge for deep learning-based lung lobe segmentation.

Purpose: Our study investigates the potential benefits of incorporating prior anatomical knowledge into a deep learning (DL) method designed for the automated segmentation of lung lobes in chest CT scans.

Approach: We introduce an automated DL-based approach that leverages anatomical information from the lung's vascular system to guide and enhance the segmentation process. This involves utilizing a lung vessel connectivity (LVC) map, which encodes relevant lung vessel anatomical data. Our study explores the performance of three different neural network architectures within the nnU-Net framework: a standalone U-Net, a multitasking U-Net, and a cascade U-Net.

Results: Experimental findings suggest that the inclusion of LVC information in the DL model can lead to improved segmentation accuracy, particularly, in the challenging boundary regions of expiration chest CT volumes. Furthermore, our study demonstrates the potential for LVC to enhance the model's generalization capabilities. Finally, the method's robustness is evaluated through the segmentation of lung lobes in 10 cases of COVID-19, demonstrating its applicability in the presence of pulmonary diseases.

Conclusions: Incorporating prior anatomical information, such as LVC, into the DL model shows promise for enhancing segmentation performance, particularly in the boundary regions. However, the extent of this improvement has limitations, prompting further exploration of its practical applicability.

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来源期刊
Journal of Medical Imaging
Journal of Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.10
自引率
4.20%
发文量
0
期刊介绍: JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.
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