A feature-enhanced network for stroke lesion segmentation from brain MRI images.

Accurate and expeditious segmentation of stroke lesions can greatly assist physicians in making accurate medical diagnoses and administering timely treatments. However, there are two limitations to the current deep learning methods. On the one hand, the attention structure utilizes only local features, which misleads the subsequent segmentation; on the other hand, simple downsampling compromises task-relevant detailed semantic information. To address these challenges, we propose a novel feature refinement and protection network (FRPNet) for stroke lesion segmentation. FRPNet employs a symmetric encoding-decoding structure and incorporates twin attention gate (TAG) and multi-dimension attention pooling (MAP) modules. The TAG module leverages the self-attention mechanism and bi-directional attention to extract both global and local features of the lesion. On the other hand, the MAP module establishes multidimensional pooling attention to effectively mitigate the loss of features during the encoding process. Extensive comparative experiments show that, our method significantly outperforms the state-of-the-art approaches with 60.16% DSC, 36.20px HD and 85.72% DSC, 27.02px HD on two ischemic stroke datasets that contain all stroke stages and several sequences of stroke images. The excellent results that exceed those of existing methods illustrate the efficacy and generalizability of the proposed method. The source code is released on https://github.com/wu2ze2lin2/FRPNet.

TransRender: a transformer-based boundary rendering segmentation network for stroke lesions.

Vision transformer architectures attract widespread interest due to their robust representation capabilities of global features. Transformer-based methods as the encoder achieve superior performance compared to convolutional neural networks and other popular networks in many segmentation tasks for medical images. Due to the complex structure of the brain and the approximate grayscale of healthy tissue and lesions, lesion segmentation suffers from over-smooth boundaries or inaccurate segmentation. Existing methods, including the transformer, utilize stacked convolutional layers as the decoder to uniformly treat each pixel as a grid, which is convenient for feature computation. However, they often neglect the high-frequency features of the boundary and focus excessively on the region features. We propose an effective method for lesion boundary rendering called TransRender, which adaptively selects a series of important points to compute the boundary features in a point-based rendering way. The transformer-based method is selected to capture global information during the encoding stage. Several renders efficiently map the encoded features of different levels to the original spatial resolution by combining global and local features. Furthermore, the point-based function is employed to supervise the render module generating points, so that TransRender can continuously refine the uncertainty region. We conducted substantial experiments on different stroke lesion segmentation datasets to prove the efficiency of TransRender. Several evaluation metrics illustrate that our method can automatically segment the stroke lesion with relatively high accuracy and low calculation complexity.