Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.
Department of Radiology, Guizhou Provincial People's Hospital, Guiyang 550002, People's Republic of China.
Phys Med Biol. 2024 Jul 26;69(15). doi: 10.1088/1361-6560/ad6372.
This study aimed to employ a two-stage deep learning method to accurately detect small aneurysms (4-10 mm in size) in computed tomography angiography images.This study included 956 patients from 6 hospitals and a public dataset obtained with 6 CT scanners from different manufacturers. The proposed method consists of two components: a lightweight and fast head region selection (HRS) algorithm and an adaptive 3D nnU-Net network, which is used as the main architecture for segmenting aneurysms. Segments generated by the deep neural network were compared with expert-generated manual segmentation results and assessed using Dice scores.The area under the curve (AUC) exceeded 79% across all datasets. In particular, the precision and AUC reached 85.2% and 87.6%, respectively, on certain datasets. The experimental results demonstrated the promising performance of this approach, which reduced the inference time by more than 50% compared to direct inference without HRS.Compared with a model without HRS, the deep learning approach we developed can accurately segment aneurysms by automatically localizing brain regions and can accelerate aneurysm inference by more than 50%.
本研究旨在采用两阶段深度学习方法，准确检测计算机断层血管造影图像中的小动脉瘤（4-10 毫米大小）。本研究纳入了来自 6 家医院的 956 名患者，以及来自不同制造商的 6 台 CT 扫描仪的公共数据集。所提出的方法包括两个组件：一个轻量级且快速的头部区域选择（HRS）算法和自适应 3D nnU-Net 网络，该网络用作分割动脉瘤的主要架构。深度神经网络生成的片段与专家生成的手动分割结果进行比较，并使用 Dice 分数进行评估。所有数据集的曲线下面积（AUC）均超过 79%。特别是，在某些数据集上，精度和 AUC 分别达到了 85.2%和 87.6%。实验结果表明，该方法具有很大的应用潜力，与没有 HRS 的直接推断相比，推断时间减少了 50%以上。与没有 HRS 的模型相比，我们开发的深度学习方法可以通过自动定位脑区来准确分割动脉瘤，并将动脉瘤推断速度提高 50%以上。
