Automatic Segmentation of Acute Ischemic Stroke From DWI Using 3-D Fully Convolutional DenseNets

• Published in: IEEE transactions on medical imaging Vol. 37; no. 9; pp. 2149 - 2160
• Main Authors: Zhang, Rongzhao, Zhao, Lei, Lou, Wutao, Abrigo, Jill M., Mok, Vincent C. T., Chu, Winnie C. W., Wang, Defeng, Shi, Lin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3D convolutional neural networks; Acute ischemic stroke segmentation; Aged; Aged, 80 and over; Aging; Algorithms; Artificial neural networks; Biomedical imaging; Brain - diagnostic imaging; Brain Ischemia - diagnostic imaging; Deep learning; Diagnosis; Diffusion Magnetic Resonance Imaging - methods; DWI; Female; Humans; Image processing; Image segmentation; Imaging, Three-Dimensional - methods; Information processing; Ischemia; Lesions; Localization; Male; Medical imaging; Middle Aged; Neural networks; Neural Networks (Computer); Objective function; Solid modeling; Stroke; Stroke - diagnostic imaging; Three-dimensional displays; Training; Two dimensional displays; Vascular diseases
• DOI: 10.1109/TMI.2018.2821244
• ISSN: 0278-0062; 1558-254X

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.