  Automatic and accurate lesion segmentation is critical for clinically
estimating the lesion statuses of stroke diseases and developing appropriate
diagnostic systems. Although existing methods have achieved remarkable results,
further adoption of the models is hindered by: (1) inter-class indistinction,
the normal brain tissue resembles the lesion in appearance. (2) intra-class
inconsistency, large variability exists between different areas of the lesion.
To solve these challenges in stroke segmentation, we propose a novel method,
namely Attention Guided Multiscale Recovery framework (AGMR-Net) in this paper.
Firstly, a coarse-grained patch attention module in the encoding is adopted to
get a patch-based coarse-grained attention map in a multi-stage explicitly
supervised way, enabling target spatial context saliency representation with a
patch-based weighting technique that eliminates the effect of intra-class
inconsistency. Secondly, to obtain a more detailed boundary partitioning to
solve the challenge of the inter-class indistinction, a newly designed
cross-dimensional feature fusion module is used to capture global contextual
information to further guide the selective aggregation of 2D and 3D features,
which can compensate for the lack of boundary learning capability of 2D
convolution. Lastly, in the decoding stage, an innovative designed multi-scale
deconvolution upsampling instead of linear interpolation enhances the recovery
of target space and boundary information. The AGMR-Net is evaluated on the open
dataset Anatomical Tracings of Lesions-After-Stroke (ATLAS), achieving the
highest dice similarity coefficient (DSC) score of 0.594, Hausdorff distance of
27.005 mm, and average symmetry surface distance of 7.137 mm, which demonstrate
that our proposed method outperforms other state-of-the-art methods and has
great potential in the diagnosis of stroke.
