Figure 2

Residual two-dimensional convolutional long short-term memory (ConvLSTM) U-net. The stack of high b-value diffusion-weighted images and corresponding apparent diffusion coefficient maps are fed into the network per-patient. Two important modifications are made to the 2D U-net. First, residual layers are utilized for each convolutional block, which allows unimpeded propagation of information throughout the network and mitigates the vanishing gradient problem. Second, bi-directional ConvLSTM layers are implemented on top of each convolutional block of the encoder network to allow communication of the feature maps. Consequently, it enables the network to consider all of the slices of an examination before delineating an ischemic lesion’s borders. Therefore, we suggest that this architecture, to some extent, mimics how radiologists assess images, which involves sequential assessment of all slices of an examination before making the final diagnosis or, in this context, performing segmentation.