Aggregate-aware model with bidirectional edge generation for medical image segmentation

Abstract

Accurate segmentation of lesion areas plays an important role in medical imaging-assisted diagnosis and treatment. Accurate boundary information can help doctors develop precise surgical plans and improve patient prognosis. However, automatic segmentation methods often struggle to accurately segment edges due to the random shape, size, and location of regions of interest (ROI). This problem is compounded in medical images, where the difference in pixel intensity between foreground and background is significantly smaller than in natural images. In this study, we propose an aggregate-aware model with bidirectional edge generation (Ambeg) for medical image segmentation. To overcome the problem of blurred edges between foreground and background in medical images, we design a deep learning model via a multi-task learning strategy and obtain richer visual features to guide segmentation. Furthermore, an Edge Feature Fusion (EFF) module is developed to combine spatial correlation information of lesion edges between adjacent images for more accurate edge segmentation. Finally, we design a new evaluation metric, the Boundary DSC segmentation consistency measure, to evaluate the edge segmentation accuracy of medical image segmentation methods. We utilize dilation and erosion operations in morphological methods to construct lesion edge labels. In addition, we use expansion and erosion rates to regulate the dimensions of the edge region to assess the requirements of different diseases for edge segmentation accuracy. The proposed approach is particularly noteworthy for achieving state-of-the-art results on medical image segmentation datasets, including BraTS 2022 (MRI), BraTS 2020 (MRI) and COVID-19–20 (CT), which have different modalities of datasets. It has an impressive Hausdorff distance of 4.62 mm and a sensitivity score of 92.45 % on BraTS 2020. Compared with existing assessment methods such as Dice score, Boundary DSC segmentation consistency measure focuses on the hard-to-segment lesion edge region rather than the easy-to-segment lesion center region, which provides a more comprehensive reference for physicians to choose automatic segmentation methods. In addition, since the boundary shapes of medical images are complex and diverse, we utilize morphological methods to obtain the boundary labels to ensure the smoothness of the boundary. Moreover, the approach is easy to implement and has a low computational cost, making it an attractive option for practical medical imaging applications.