分割
人工智能
推论
深度学习
计算机科学
人工神经网络
血管造影
放射科
选择(遗传算法)
动脉瘤
计算机断层摄影术
模式识别(心理学)
计算机断层血管造影
医学
机器学习
作者
Huizhong Zheng,Xinfeng Liu,Zhenxing Huang,Yan Ren,Bin Fu,Tianliang Shi,Lu Liu,Qiping Guo,Chong Tian,Dong Liang,Rongpin Wang,Jie Chen,Zhanli Hu
标识
DOI:10.1088/1361-6560/ad6372
摘要
Abstract Objective. 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. Approach. 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. Main Results. 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. Significance. 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%.
科研通智能强力驱动
Strongly Powered by AbleSci AI