卷积神经网络
计算机科学
人工智能
判别式
掷骰子
边距(机器学习)
模式识别(心理学)
一般化
深度学习
冲程(发动机)
数据集
Sørensen–骰子系数
分割
保险丝(电气)
缺血性中风
相似性(几何)
机器学习
图像分割
医学
图像(数学)
缺血
心脏病学
数学
统计
工程类
数学分析
电气工程
机械工程
作者
Rongzhao Zhang,Lei Zhao,Wutao Lou,Jill Abrigo,Vincent Mok,Chiu‐Wing Winnie Chu,Defeng Wang,Lin Shi
标识
DOI:10.1109/tmi.2018.2821244
摘要
Acute ischemic stroke is recognized as a common cerebral vascular disease in aging people. Accurate diagnosis and timely treatment can effectively improve the blood supply of the ischemic area and reduce the risk of disability or even death. Understanding the location and size of infarcts plays a critical role in the diagnosis decision. However, manual localization and quantification of stroke lesions are laborious and time-consuming. In this paper, we propose a novel automatic method to segment acute ischemic stroke from diffusion weighted images (DWIs) using deep 3-D convolutional neural networks (CNNs). Our method can efficiently utilize 3-D contextual information and automatically learn very discriminative features in an end-to-end and data-driven way. To relieve the difficulty of training very deep 3-D CNN, we equip our network with dense connectivity to enable the unimpeded propagation of information and gradients throughout the network. We train our model with Dice objective function to combat the severe class imbalance problem in data. A DWI data set containing 242 subjects (90 for training, 62 for validation, and 90 for testing) with various types of acute ischemic stroke was constructed to evaluate our method. Our model achieved high performance on various metrics (Dice similarity coefficient: 79.13%, lesionwise precision: 92.67%, and lesionwise F1 score: 89.25%), outperforming the other state-of-the-art CNN methods by a large margin. We also evaluated the model on ISLES2015-SSIS data set and achieved very competitive performance, which further demonstrated its generalization capacity. The proposed method is fast and accurate, demonstrating a good potential in clinical routines.
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