中线偏移
人工智能
分割
脑室出血
医学
深度学习
计算机科学
计算机视觉
计算机断层摄影术
放射科
遗传学
生物
胎龄
怀孕
作者
Dasheng Wu,Haoming Li,Jianbo Chang,Chenchen Qin,Yi‐Hao Chen,Yixun Liu,Qinghua Zhang,Bingsheng Huang,Ming Feng,Renzhi Wang,Jianhua Yao
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2022-09-01
卷期号:41 (9): 2217-2227
被引量:2
标识
DOI:10.1109/tmi.2022.3160184
摘要
Brain midline delineation plays an important role in guiding intracranial hemorrhage surgery, which still remains a challenging task since hemorrhage shifts the normal brain configuration. Most previous studies detected brain midline on 2D plane and did not handle hemorrhage cases well. We propose a novel and efficient hemisphere-segmentation framework (HSF) for 3D brain midline surface delineation. Specifically, we formulate the brain midline delineation as a 3D hemisphere segmentation task, and employ an edge detector and a smooth regularization loss to generate the midline surface. We also introduce a distance-weighted map to keep the attention on the midline. Furthermore, we adopt rectification learning to handle various head poses. Finally, considering the complex situation of ventricle break-in for hemorrhages in bilateral intraventricular (B-IVH) cases, we identify those cases via a classification model and design a midline correction strategy to locally adjust the midline. To our best knowledge, it is the first study focusing on delineating the brain midline surface on 3D CT images of hemorrhage patients and handling the situation of ventricle break-in. Extensive validation on our large in-house datasets (519 patients) and the public CQ500 dataset (491 patients), demonstrates that our method outperforms state-of-the-art methods on brain midline delineation.
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