🤖 AI Summary
This work addresses the common issue in medical image segmentation where insufficient geometric priors lead to poor boundary representation. To this end, it introduces mean curvature as a geometric constraint into deep active contour models for the first time, formulating a novel loss function to enhance geometric consistency of segmentation boundaries. A lightweight convolutional kernel is employed to approximate mean curvature efficiently, significantly reducing computational overhead. Furthermore, the method integrates the Chan–Vese model with convolutional neural networks to jointly optimize region and boundary information. Evaluated on liver and spleen datasets, the proposed approach achieves state-of-the-art performance, markedly improving both segmentation accuracy and geometric plausibility.
📝 Abstract
Medical image segmentation is a crucial task in the field of clinical analysis and applications. Though deep learning techniques recently play a crucial role in several scenarios, the training at the individual pixel level leads to a lack of geometric prior information. Scholars proposed to integrate the Chan-Vese model into the loss function for training which can take into account the region and length of the region inside and outside the segmentation process and then improve the performance in medical image segmentation. However, these methods still lack an effective characterization of the segmented region. To overcome this problem, we introduce the mean curvature as a geometric natural constraint and propose a Deep Active Contour and Mean Curvature (DACMC) loss function where the convolution kernel is used to approximate the mean curvature to save computational cost. We have validated the performance of our method on the liver and spleen dataset. Our proposed method demonstrates new state-of-the-art performance on several segmentation datasets.