Sparse Cascade: Multi-stage cascade fusion and shape dictionary guided network for segmentation of microscopic cytology images

Microscopic cytology image instance segmentation is critical for clinical diagnosis, treatment planning, and biomedical research. However, challenges such as cell overlapping, external interferences, and irregular shapes hinder accurate segmentation. In this paper, a microscopic cytology image instance segmentation algorithm based on a multi-stage cascaded fusion and shape-guided strategy is proposed to improve instance segmentation accuracy . To mitigate the issue of external interference and cell overlap, a feedforward fusion mechanism is introduced in the multi-stage cascade architecture to effectively integrate the mask features from multiple stages. To make the shape of the segmentation result more consistent with the shape of the cell, and to alleviate the issues of unclear boundaries and incomplete shape segmentation caused by cellular overlap and irregular shapes, the sparse code guidance branch based on the explicit shape guidance strategy is introduced. Experiments on the private embryonic cell dataset and the ISBI2014 dataset indicate that the network has achieved improvements over the Cascade Mask R-CNN, with enhancements of 2.51% and 3.92% in bounding boxes mean Average Precision (mAP), and 2.82% and 4.70% in segmentation mAP, respectively. The experimental results indicate that the network proposed in this paper can output more accurate bounding boxes and instance masks.