Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function
Medical image segmentation is an important task in clinical analysis. Although deep learning techniques are widely used, training at the individual pixel level ignores geometric prior information about the region being segmented. Integrating the Chan-Vese model into the loss function is a well-established remedy that accounts for the regions inside and outside the segmentation and, through its length term, for boundary regularity. However, such losses still lack an effective characterisation of local boundary geometry. We introduce the mean curvature as a natural geometric constraint and propose a Deep Active Contour and Mean Curvature (DACMC) loss function, in which a fixed convolution k...
| Authors | Xiao-qiang Zhai, Zhi-feng Pang, Peng Zheng et al. |
|---|---|
| Primary category | eess.IV |
| Published | 14 July 2026 |
| Last updated at source | 18 September 2026 |
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