AGMR-Net: Attention-guided multiscale recovery framework for stroke segmentation.

Abstract

Automatic and accurate lesion segmentation is critical to the clinical estimation of the lesion status of stroke diseases and appropriate diagnostic systems. Although existing methods have achieved remarkable results, their further adoption is hindered by: (1) intraclass inconsistency, i.e., large variability between different areas of the lesion; and (2) interclass indistinction, in which normal brain tissue resembles the lesion in appearance. To meet 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 (CPA) module in the encoding is adopted to obtain a patch-based coarse-grained attention map in a multistage, explicitly supervised way, enabling target spatial context saliency representation with a patch-based weighting technique that eliminates the effect of intraclass inconsistency. Secondly, to obtain more detailed boundary partitioning to meet the challenge of interclass indistinction, a newly designed cross-dimensional feature fusion (CFF) 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 multiscale deconvolution upsampling (MDU) is used for enhanced recovery of target spatial and boundary information. AGMR-Net is evaluated on the open-source dataset Anatomical Tracings of Lesions After Stroke, achieving the highest Dice similarity coefficient of 0.594, Hausdorff distance of 27.005mm, and average symmetry surface distance of 7.137mm, which demonstrates that our proposed method outperforms state-of-the-art methods and has great potential for stroke diagnosis.