Cervical cancer is one of the most prevalent and easily contracted diseases among women, significantly impacting their daily lives. Computer vision-based cervical cell morphology diagnosis technology can offer robust support for cervical cell analysis at a lower cost. However, the presence of a substantial number of overlapping cells in cervical images renders existing cell segmentation methods less accurate, thereby complicating the guidance of medical diagnosis. In this paper, we introduce a tristage Progressive Refinement method (PRefiner) for overlapping cell segmentation that decouples the traditional end-to-end pipeline, with the final stage specifically correcting anomalous results to enhance precision. We achieve separable overlapping cervical cell segmentation results through a cell nucleus locator, a single-cell segmenter, and a Segmentation Result Mask Refiner. Specifically, we employ a hybrid U-Net as the primary network for the cell nucleus locator and single-cell segmenter, which determines the position of the cell nucleus and procures the initial coarse segmentation result. In the mask refiner, we incorporate a conditional generation framework to address the perception decision problem and design a local–global dual-scale discriminator to ensure that the segmentation result aligns with the prior of a single-cell mask. Experimental results on CCEDD and ISBI2015 demonstrate that PRefiner achieves optimal performance by effectively resolving abnormal segmentations. Notably, our method improves the Dice coefficient of abnormal results from five different models by an average of 2.62% (ranging from 1.0% to 5.1%).
Zhu et al. (Sat,) studied this question.