Segmentation of Chronic Subdural Hematomas Using 3D Convolutional Neural Networks
Segmentation of Chronic Subdural Hematomas Using 3D Convolutional Neural Networks
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DOI：
10.1016/j.wneu.2020.12.014
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发表时间：
2021-03-22
影响因子：
2
通讯作者：
Levitt, Michael
中科院分区：
文献类型：
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作者：
Kellogg, Ryan T.;Vargas, Jan;Levitt, Michael
目的：慢性硬膜下血肿（cSDH）是一种日益普遍的神经系统疾病，通常需要手术干预以减轻脑压迫。cSDH的管理严重依赖于计算机断层扫描（CT）成像，并且经常获得系列成像以帮助直接管理。血肿体积为指导治疗和评估新的治疗方法提供了重要信息。我们着手开发一个自动程序来计算CT扫描前和术后images.METHODS的血肿体积：共21,710图像（128 CT扫描）进行手动分割，并用于训练卷积神经网络自动分割cSDHs。我们纳入了接受cSDH手术治疗的患者的术前和术后冠状头CT。结果：我们的最佳模型在测试数据集上的DICE得分为0.8351，在验证集上的平均DICE得分为0.806 +/- 0.06。该模型在完整的数据集上进行训练，其中体积减小，网络深度为4，并且编码器通路的上下文模块内的激活后残留块。补丁训练的模型表现不佳，将网络深度从5降低到4似乎并没有显着提高性能。结论：我们成功地在包含cSDH的术前和术后头部CT数据集上训练了卷积神经网络。该工具可以帮助进行自动化，准确的测量，以评估治疗效果。
OBJECTIVE: Chronic subdural hematomas (cSDHs) are an increasingly prevalent neurologic disease that often requires surgical intervention to alleviate compression of the brain. Management of cSDHs relies heavily on computed tomography (CT) imaging, and serial imaging is frequently obtained to help direct management. The volume of hematoma provides critical information in guiding therapy and evaluating new methods of management. We set out to develop an automated program to compute the volume of hematoma on CT scans for both pre- and postoperative images.METHODS: A total of 21,710 images (128 CT scans) were manually segmented and used to train a convolutional neural network to automatically segment cSDHs. We included both pre- and postoperative coronal head CTs from patients undergoing surgical management of cSDHs. -RESULTS: Our best model achieved a DICE score of 0.8351 on the testing dataset, and an average DICE score of 0.806 +/- 0.06 on the validation set. This model was trained on the full dataset with reduced volumes, a network depth of 4, and postactivation residual blocks within the context modules of the encoder pathway. Patch trained models did not perform as well and decreasing the network depth from 5 to 4 did not appear to significantly improve performance. -CONCLUSIONS: We successfully trained a convolutional neural network on a dataset of pre- and postoperative head CTs containing cSDH. This tool could assist with automated, accurate measurements for evaluating treatment efficacy.