Edge-enhanced semi-supervised vertical convolutional neural network for tubular structure segmentation: Application to medical images

Abstract

Accurate segmentation of tubular structures in the human body is crucial for disease diagnosis and preoperative planning in clinical practice. However, achieving precision in segmenting tubular structures in medical images proves challenging due to their highly intricate, furcated, and slender nature. This complexity also challenges obtaining a substantial amount of labeled data necessary for training deep learning models. To address these challenges, we propose ESVC-Net, a novel Edge-enhanced Semi-supervised Vertical Convolutional neural network designed to produce accurate tubular structure segmentation. Unlike traditional convolution approaches at a single scale, we propose a cross-scale vertical convolution module, enabling the learning of abundant multi-scale features for furcated and slender structures in the encoder. To enhance discriminability around the boundary, we introduce an edge spatially adaptive enhancement module. This module integrates edge features learned from the auxiliary edge detection task into the segmentation process. Furthermore, we employ a semi-supervised learning method, leveraging a significant amount of unlabeled data to enhance segmentation performance. We validate the effectiveness of ESVC-Net on two types of tubular structures: the lumbosacral plexus using MR images and the airway using CT images. Experimental results show that the superiority of ESVC-Net over state-of-the-art methods.