Automatic segmentation of stroke lesions in T1-weighted magnetic resonance images with convolutional neural networks

Abstract

Stroke is a major cause of death and permanent disability. Magnetic resonance imaging (MRI) is often the modality for evaluating lesion extension, affected brain, area and classification between hemorrhagic or ischemic, which are critical for treatment and rehabilitation decisions. MRI manual evaluation and lesion delineation is time-consuming and subject to inter-and intra-observer variation. Although promising, convolutional neural network (CNN) approaches face challenges in dealing with small-size lesions, irregular morphology, and idiosyncrasy. The best-published segmentation approaches based on the ATLAS dataset and CNNs are either fully 3D, or use up to eight anatomically specific 2D CNNs, incurring high computation costs and limited deployment feasibility. We developed a more straightforward segmentation method using only three CNNs. First, an Attention UNet and an Attention ResUNet are trained only on a lesion-wise balanced sample of slice patches. By working on the patch level, these networks learn to segment lesions' texture and shape irregularities. Then, another Attention ResUNet uses the previous two CNNs output patches reassembled and stacked along with the original slice. By having the broader slice context available, this second step combines the first step segmentations, handling disagreements while keeping the segmentation coherence on the slice level. We validated the method on 239 exams from the multicenter ATLAS dataset, using a 5-fold cross-validation. In the test set of 45,171 slices, we observed a mean slice Dice coefficient of 0.8070±0.05, a state-of-the-art result in this dataset, showing generalization capacity on different centers and acquisition conditions.