Fully automated intracranial aneurysm detection and segmentation from digital subtraction angiography series using an end-to-end spatiotemporal deep neural network
Fully automated intracranial aneurysm detection and segmentation from digital subtraction angiography series using an end-to-end spatiotemporal deep neural network
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DOI：
10.1136/neurintsurg-2020-015824
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发表时间：
2020-10-01
影响因子：
4.8
通讯作者：
Zhang, Hongqi
中科院分区：
文献类型：
--
作者：
Jin, Hailan;Geng, Jiewen;Zhang, Hongqi
背景：颅内动脉瘤(IAS)在人群中很常见，可导致死亡。目的开发一种新的基于深度神经网络的全自动检测和分割框架，以帮助神经科医生在诊断过程中从2D+时间数字减影血管造影(DSA)序列中评估和轮廓颅内动脉瘤。方法基于通用U型设计的网络结构用于医学图像的分割和检测。该网络包括一种完全卷积技术，可以在高分辨率DSA帧中检测动脉瘤。此外，在网络的每一级都引入了双向卷积长期短期记忆模块，以捕捉穿过2D DSA帧的对比剂流动的变化。所得到的网络结合了来自DSA序列的空间和时间信息，并且可以端到端地进行训练。此外，还实施了深度监管，帮助网络融合。所提出的网络结构用来自347名IAS患者的2269个DSA序列进行训练。之后，在一个盲测试集上对该系统进行了评估，该测试集包含来自146名患者的947个DSA序列。结果354个动脉瘤中，316个(89.3%)检出成功，患者层面敏感性为97.7%，平均每个序列的假阳性数为3.77个。该系统每个序列的运行时间不到1秒，平均骰子系数得分为0.533。结论该深度神经网络有助于从2D DSA序列中成功地检测和分割动脉瘤，具有临床应用价值。
Background Intracranial aneurysms (IAs) are common in the population and may cause death. Objective To develop a new fully automated detection and segmentation deep neural network based framework to assist neurologists in evaluating and contouring intracranial aneurysms from 2D+time digital subtraction angiography (DSA) sequences during diagnosis. Methods The network structure is based on a general U-shaped design for medical image segmentation and detection. The network includes a fully convolutional technique to detect aneurysms in high-resolution DSA frames. In addition, a bidirectional convolutional long short-term memory module is introduced at each level of the network to capture the change in contrast medium flow across the 2D DSA frames. The resulting network incorporates both spatial and temporal information from DSA sequences and can be trained end-to-end. Furthermore, deep supervision was implemented to help the network converge. The proposed network structure was trained with 2269 DSA sequences from 347 patients with IAs. After that, the system was evaluated on a blind test set with 947 DSA sequences from 146 patients. Results Of the 354 aneurysms, 316 (89.3%) were successfully detected, corresponding to a patient level sensitivity of 97.7% at an average false positive number of 3.77 per sequence. The system runs for less than one second per sequence with an average dice coefficient score of 0.533. Conclusions This deep neural network assists in successfully detecting and segmenting aneurysms from 2D DSA sequences, and can be used in clinical practice.