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动脉瘤性颅内出血的自动检测和血肿分割方法

周海柱 张东 胡平 祝新根

周海柱, 张东, 胡平, 祝新根. 动脉瘤性颅内出血的自动检测和血肿分割方法[J]. 分子影像学杂志, 2023, 46(3): 389-397. doi: 10.12122/j.issn.1674-4500.2023.03.02
引用本文: 周海柱, 张东, 胡平, 祝新根. 动脉瘤性颅内出血的自动检测和血肿分割方法[J]. 分子影像学杂志, 2023, 46(3): 389-397. doi: 10.12122/j.issn.1674-4500.2023.03.02
ZHOU Haizhu, ZHANG Dong, HU Ping, ZHU Xingen. Automated detection and segmentation method for aneurysmal intracranial hemorrhage[J]. Journal of Molecular Imaging, 2023, 46(3): 389-397. doi: 10.12122/j.issn.1674-4500.2023.03.02
Citation: ZHOU Haizhu, ZHANG Dong, HU Ping, ZHU Xingen. Automated detection and segmentation method for aneurysmal intracranial hemorrhage[J]. Journal of Molecular Imaging, 2023, 46(3): 389-397. doi: 10.12122/j.issn.1674-4500.2023.03.02

动脉瘤性颅内出血的自动检测和血肿分割方法

doi: 10.12122/j.issn.1674-4500.2023.03.02
基金项目: 国家自然科学基金(82172989);江西省重点研发计划项目(20212BBG71012)
详细信息
    作者简介:

    周海柱,在读硕士研究生,E-mail: haizhuzhou@whu.edu.cn

Automated detection and segmentation method for aneurysmal intracranial hemorrhage

Funds: Supported by National Natural Science Foundation of China (82172989)
  • 摘要:   目的  提出一种端到端的颅内动脉瘤破裂引起的颅内出血多类型血肿全自动分割方法。  方法  选择颅内动脉瘤破裂引起的颅内出血644例CT影像数据,按8:2的比例分为训练集和测试集。首先通过区域生长的方式获取脑组织区域,然后利用深度学习对出血区域进行多类型血肿分割。  结果  测试集上的结果表明,蛛网膜下腔出血、脑实质出血、脑室内出血和颅内出血的Dice系数分别为62.13%、68.64%、50.08%、71.10%。  结论  本文提出的分割网络可以有效地对颅内动脉瘤破裂引起的颅内出血完成多类型血肿分割,配合脑组织提取算法可以在未处理的临床数据上自动完成端到端的处理流程。该方法有效提升颅内动脉瘤破裂引起的颅内出血的诊治效率,有较好的临床应用价值。

     

  • 图  1  脑组织提取算法流程

    Figure  1.  The flow of brain extraction.

    图  2  脑干识别示意图

    Figure  2.  Diagram for brainstem recognition. Image B could be done from image A, which performed tissue extraction, erosion and removal of too-large or too-small ar-eas. The IOU of brainstem position identification was 0.91.

    图  3  脑组织提取结果

    Figure  3.  Brain extraction results. The area under the red masked is the brain tissue. There were 28 slices in this case, and the currently selected slice was 2, 6, 11, 18, 23, 26. The 6th slice was the last brainstem position after identification.

    图  4  基于椭圆拟合的旋转矫正

    Figure  4.  Rotation correction. A: Result of ellipse fitting; B: Result of rotation.

    图  5  混合2D/3D特征融合分割网络框架图

    Figure  5.  The framework of 2D/3D segmentation network with feature fusion.

    图  6  混合2D/3D特征融合分割网络设计图

    Figure  6.  The design of 2D/3D segmentation network with feature fusion.

    图  7  基本模块

    Figure  7.  Basic module.

    图  8  空间注意力模块

    Figure  8.  Spatial attention module.

    图  9  脑组织提取结果盒子图表示

    Figure  9.  Box plots for the result of brain extraction (Dice).

    图  10  分割结果图

    Figure  10.  Segmentation result. Red, yellow and blue represent the hematoma of SAH, IPH and IVH. The images from left to right are brain tissue, ground truths, prediction of 2D-UNet, prediction of 3D-UNet and prediction of the model in this article.

    表  1  血肿分割的表现

    Table  1.   The results of hematoma segmentation (%, Dice)

    Model ICH SAH IPH IVH
    2D-UNet 70.54 62.21 66.44 45.91
    3D-UNet 68.09 60.83 72.90 49.73
    Ours 71.10 62.13 68.64 50.08
    ICH: Intracranial hemorrhage; SAH: Subarachnoid hemorrhage; IPH: Intracerebral parenchymal hemorrhage; IVH: Intraventricu-lar hemorrhage.
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-01-22
  • 网络出版日期:  2023-06-15
  • 刊出日期:  2023-05-20

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