  Segmenting stroke lesions from T1-weighted MR images is of great value for
large-scale stroke rehabilitation neuroimaging analyses. Nevertheless, there
are great challenges with this task, such as large range of stroke lesion
scales and the tissue intensity similarity. The famous encoder-decoder
convolutional neural network, which although has made great achievements in
medical image segmentation areas, may fail to address these challenges due to
the insufficient uses of multi-scale features and context information. To
address these challenges, this paper proposes a Cross-Level fusion and Context
Inference Network (CLCI-Net) for the chronic stroke lesion segmentation from
T1-weighted MR images. Specifically, a Cross-Level feature Fusion (CLF)
strategy was developed to make full use of different scale features across
different levels; Extending Atrous Spatial Pyramid Pooling (ASPP) with CLF, we
have enriched multi-scale features to handle the different lesion sizes; In
addition, convolutional long short-term memory (ConvLSTM) is employed to infer
context information and thus capture fine structures to address the intensity
similarity issue. The proposed approach was evaluated on an open-source
dataset, the Anatomical Tracings of Lesions After Stroke (ATLAS) with the
results showing that our network outperforms five state-of-the-art methods. We
make our code and models available at https://github.com/YH0517/CLCI_Net.
