DES-SAM: Distillation-Enhanced Semantic SAM for Cervical Nuclear Segmentation with Box Annotation

Nuclei segmentation in cervical cell images is a crucial technique for the automatic diagnosis of cervical cell pathology. The current state-of-the-art (SOTA) nuclei segmentation methods often require significant time and resources to provide pixel-level annotations for training. To reduce the labor-intensive annotation costs, we propose DES-SAM, a box-supervised cervical nucleus segmentation network with strong generalization ability based on self-distillation prompting. We utilize Segment Anything Model (SAM) to generate high-quality pseudo-labels by integrating a lightweight detector. The main challenges lie in the poor generalization ability brought by small-scale training datasets and the large-scale training parameters of traditional knowledge distillation frameworks. To address these challenges, we propose leveraging the strong feature extraction ability of SAM and a self-distillation prompting strategy to maximize the performance of the downstream nuclear semantic segmentation task without compromising SAM’s generalization. Additionally, we propose an Edge-aware Enhanced Loss to improve the segmentation capability of DES-SAM. Various comparative and generalization experiments on public cervical cell nuclei datasets demonstrate the effectiveness of the proposed method. The code is available at https://github.com/CVIU-CSU/DES-SAM .