Stroke, as a disease with high incidence and mortality rates, is increasingly receiving attention. Despite the rapid development of deep learning in the medical field providing excellent performance for AI-assisted diagnosis, automated segmentation of stroke still poses significant challenges. Issues such as the similarity between hemorrhagic regions and the background, the irregularity of hemorrhagic areas, and the vast variability in hemorrhage sizes persist. To address these segmentation challenges, this paper introduces a new network architecture that incorporates a multi-scale channel joint attention module and cascaded feature assisted enhancement module, taking into account the anisotropy and asymmetry of images. This method aims to accurately segment lesion tissues from chronic stroke brain images in T1- weighted MRI. Experimental results have demonstrated that the method proposed in this paper achieves superior performance outcomes compared to other methods. This study provides a promising solution for stroke segmentation.
In this paper, we introduce a new multi-center instance segmentation model based on the deep learning technique, as a generalization of the classical polarmask model. In contrast to the original polarmask model which imposes a star-convexity shape to approximate the target region, we propose to establish a multi-center model which allows representing the target region via multiple star convex shapes. For this purpose, we extract a set of points, each of which is taken as the centers of star convex shapes, to compute multiple star convex shapes. As a consequence, the final segmentation mask can be naturally generated using the union of all of the detected star convex shapes. Experimental results show that the multi-center polarmask model can achieve more advanced performance on the COCO dataset. In addition, the introduced model provides the possibility for real-time applications.
It is a popular way to incorporate the active contour evolution scheme into the multiscale image decomposition and reconstruction procedure, so as to enhance the image segmentation accuracy. However, most of these models are carried out by the level set formulation which cost much computation time. In this paper, we propose a new image segmentation model that combines the circular geodesic model with an adaptive cut and the multiscale image processing. As a consequence, the proposed model can blend the benefits from both of the geodesic models and the multiscale image analysis method. Experimental results show that the proposed multiscale geodesic model indeed outperforms the circular geodesic model with an adaptive cut in solving the image segmentation problem in the presence of strong noise.
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