A multi-branch hierarchical attention network for medical target segmentation

Abstract

Medical imaging techniques have been widely used in modern clinical disease diagnosis and treatment programs. The captured medical images can well reflect the conditions of the human body tissues, which are significantly helpful to the doctors to determine the existence or the severity of the disease. In this paper, we develop a hierarchical attentive high-resolution convolutional network (AttHRNet) for segmenting targets of interest from medical images aiming to improve the automated processing standard and the intelligent interpretation quality of the medical images. The AttHRNet is an improved version of the high-resolution network (HRNet) structure with three novel modules. First, built with an improved HRNet structure assisted by a multiscale context augmentation (MSCA) module as the feature extraction backbone, the AttHRNet can produce a set of high-quality, strong-semantic feature maps at different resolutions. The MSCA module functions to reduce the information loss during feature downsampling. Second, designed with an effective feature attention principle, the feature encoding quality in each branch can be significantly promoted by concentrating on the informative and salient feature encodings across both channels and spatial locations. Furthermore, formulated with a hierarchical segmentation scheme, the output feature maps can be further augmented by including the semantic-level category exploitation (SLCE) module with a global perspective. The SLCE module allows the information from lower resolution segmentations to inform higher resolution segmentations. Through quantitative examinations, visual verifications, and comparative evaluations on four medical image datasets, we convince the promising applicability and competitive superiority of the AttHRNet in medical target segmentation issues.