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In recent years, the increasing incidence of morbidity of brain stroke has made fast and accurate segmentation of lesion areas from brain MRI images important. With the development of deep learning, segmentation methods based on the computer have become a solution to assist clinicians in early diagnosis and treatment planning. Nevertheless, the variety of lesion sizes in brain MRI images and the roughness of the boundary of the lesion pose challenges to the accuracy of the segmentation algorithm. Current mainstream medical segmentation models are not able to solve these challenges due to their insufficient use of image features and context information. This paper proposes a novel feature enhancement and context capture network (FECC-Net), which is mainly composed of an atrous spatial pyramid pooling (ASPP) module and an enhanced encoder. In particular, the ASPP model uses parallel convolution operations with different sampling rates to enrich multi-scale features and fully capture image context information in order to process lesions of different sizes. The enhanced encoder obtains deep semantic features and shallow boundary features in the feature extraction process to achieve image feature enhancement, which is helpful for restoration of the lesion boundaries. We divide the pathological image into three levels according to the number of pixels in the real mask area and evaluate FECC-Net on an open dataset called Anatomical Tracings of Lesions After Stroke (ATLAS). The experimental results show that our FECC-Net outperforms mainstream methods, such as DoubleU-Net and TransUNet. Especially in small target tasks, FECC-Net is 4.09% ahead of DoubleU-Net on the main indicator DSC. Therefore, FECC-Net is encouraging and can be relied upon for brain MRI image applications.

In medical image segmentation tasks, it is typical to adopt convolutional neural networks with a serial encoder-decoder structure. However, mainstream networks cannot simultaneously achieve sufficient extraction of global features and the fusion of multi-scale information, which may lead to unpromising results for the segmentation of pathological images. Therefore, this article proposed a novel multi-scale feature fusion and global self-attention network (MSSA-Net) for medical image segmentation. Specifically, we designed a parallel double-encoder network with a multi-scale feature fusion encoder (MS-Encoder) and a self-attention encoder (SA-Encoder). The SA-Encoder introduces the transformer's global self-attention mechanism to extract global features, and the MS-Encoder adopts atrous spatial pyramid pooling (ASPP) to realize multi-scale fusion. We have evaluated the proposed MSSA-Net using three medical segmentation datasets, covering various imaging modalities such as colonoscopy and magnetic resonance imaging. Experiments on the CVC-ClinicDC, the 2015 MICCAI subchallenge on automatic polyp detection dataset, and anatomical tracings of lesions after stroke (ATLAS) show that our MSSA-Net outperforms mainstream methods such as DoubleU-Net and TransUNet. Moreover, MSSA-Net can predict more accurate segmentation masks, especially in the case of ATLAS, which has challenging images such as multiple shadow areas and discrete lesions.

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