Patient-specific cerebral 3D vessel model reconstruction using deep learning.

IF 2.6 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Medical & Biological Engineering & Computing Pub Date : 2024-10-01 Epub Date: 2024-05-28 DOI:10.1007/s11517-024-03136-6
Satoshi Koizumi, Taichi Kin, Naoyuki Shono, Satoshi Kiyofuji, Motoyuki Umekawa, Katsuya Sato, Nobuhito Saito
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Abstract

Three-dimensional vessel model reconstruction from patient-specific magnetic resonance angiography (MRA) images often requires some manual maneuvers. This study aimed to establish the deep learning (DL)-based method for vessel model reconstruction. Time of flight MRA of 40 patients with internal carotid artery aneurysms was prepared, and three-dimensional vessel models were constructed using the threshold and region-growing method. Using those datasets, supervised deep learning using 2D U-net was performed to reconstruct 3D vessel models. The accuracy of the DL-based vessel segmentations was assessed using 20 MRA images outside the training dataset. The dice coefficient was used as the indicator of the model accuracy, and the blood flow simulation was performed using the DL-based vessel model. The created DL model could successfully reconstruct a three-dimensional model in all 60 cases. The dice coefficient in the test dataset was 0.859. Of note, the DL-generated model proved its efficacy even for large aneurysms (> 10 mm in their diameter). The reconstructed model was feasible in performing blood flow simulation to assist clinical decision-making. Our DL-based method could successfully reconstruct a three-dimensional vessel model with moderate accuracy. Future studies are warranted to exhibit that DL-based technology can promote medical image processing.

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利用深度学习重建特定患者的三维脑血管模型
从患者特定的磁共振血管造影(MRA)图像重建三维血管模型通常需要一些手动操作。本研究旨在建立基于深度学习(DL)的血管模型重建方法。研究人员制作了40名颈内动脉瘤患者的飞行时间MRA图像,并使用阈值和区域生长法构建了三维血管模型。利用这些数据集,使用二维 U 网进行有监督的深度学习,重建三维血管模型。使用训练数据集之外的 20 张 MRA 图像评估了基于 DL 的血管分割的准确性。骰子系数被用作模型准确性的指标,并使用基于 DL 的血管模型进行了血流模拟。在所有 60 个病例中,创建的 DL 模型都能成功重建三维模型。测试数据集中的骰子系数为 0.859。值得注意的是,DL 生成的模型即使对大动脉瘤(直径大于 10 毫米)也证明了其有效性。重建的模型可用于血流模拟,辅助临床决策。我们基于 DL 的方法可以成功地重建三维血管模型,准确度适中。未来的研究将证明基于 DL 的技术可以促进医学图像处理。
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来源期刊
Medical & Biological Engineering & Computing
Medical & Biological Engineering & Computing 医学-工程:生物医学
CiteScore
6.00
自引率
3.10%
发文量
249
审稿时长
3.5 months
期刊介绍: Founded in 1963, Medical & Biological Engineering & Computing (MBEC) continues to serve the biomedical engineering community, covering the entire spectrum of biomedical and clinical engineering. The journal presents exciting and vital experimental and theoretical developments in biomedical science and technology, and reports on advances in computer-based methodologies in these multidisciplinary subjects. The journal also incorporates new and evolving technologies including cellular engineering and molecular imaging. MBEC publishes original research articles as well as reviews and technical notes. Its Rapid Communications category focuses on material of immediate value to the readership, while the Controversies section provides a forum to exchange views on selected issues, stimulating a vigorous and informed debate in this exciting and high profile field. MBEC is an official journal of the International Federation of Medical and Biological Engineering (IFMBE).
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