Multi-dimensional segmentation model and system development for recognizing small lesions after acute ischemic stroke

To develop a deep learning-based multi-dimensional segmentation system to recognize small lesions in magnetic resonance imaging（MRI）images in order to provide a decision-making basis for the diagnosis and treatment of acute ischemic stroke（AIS）.

We extracted and fused the features from 2D and 3D network, introduced the joint loss function, and then proposed a new 2.5D method, a multi-dimensional multi-scale attention enhanced network（MMAE-Net）. On AIS segmentation datasets（171 cases in training set and 43 cases in testing set）, the proposed method was trained and tested, and its performance was compared with other methods.

When compared to 2D and 3D networks, our 2.5D network（MMAE-Net）achieved the best segmentation performance in all evaluation indicators, with a dice similarity coefficient（DSC）of 81.25% and a sensitivity of 84.82%. MMAE-Net achieved better segmentation performance when compared to U-Net, ResU-Net, DenseU-Net, AttentionU-Net, Segmentation TRansformer（SETR）, and other classical methods and previous research. In addition, we also created a visual and automated clinical application system to improve the practical and promotive capability of methods.

Based on fusing the features from 2D and 3D network, a 2.5D multi-dimensional segmentation model MMAE-Net is developed, which has achieved excellent performance in the recognition of MRI small lesions and provides an effective solution for the diagnosis and treatment of AIS diseases.