A spatiotemporal correlation deep learning network for brain penumbra disease

Brain penumbra is a critical condition that is closely related to stroke. Thus, there is high demand for fast and accurate segmentation of penumbra tissue in magnetic resonance images. However, most convolu-tional neural networks (CNNs) focus on learning contextual semantic information from two-dimensional imaging slides, ignoring the spatiotemporal correlations among adjacent slides. Here, we propose an encoder-decoder network (ConvLSTM-Net) with a specifically convolutional long short-term memory skip connection to extract the spatiotemporal correlations of features of adjacent slices in a non-linear manner. A mixed loss function is also used to improve the segmentation performance. We test the pro-posed method on the penumbra segmentation challenge and obtain an average Dice score over 80%, indi-cating that its performance is superior to or comparable with that of state-of-the-art segmentation methods. A mixed loss function provides positive support for the stability of model training. In addition, we visualize two representative samples to improve the interpretability of the results of ConvLSTM-Net.(c) 2022 Published by Elsevier B.V.