ESNet: End-to-End Chromosome Instance Segmentation Method Based on Edge Supervised Network

Chromosomal numerical and structural abnormalities are common in newborn defects. Karyotype analysis, which can detect abnormalities based on chromosome microscope images, has emerged as one of the gold standards for the diagnosis of such diseases. Individual chromosomes are segmented, numbered and identified throughout this procedure. Thus, precise chromosomal segmentation, as the key of karyotyping, affects the success of subsequent missions. Most segmentation methods cannot identify a chromosome in case of overlapping since there is little distinction between inter and intra chromosomal classes. The loss and redundancy of the segmentation mask may be to blame for that. An end-to-end framework named Edge Supervised Network (ESNet) based on Mask RCNN is proposed in this paper. To incorporate edge prior knowledge, an edge supervised branch and feature fusion module are designed to help recognize individual chromosomes from clusters, and a spatial attention module to capture more contextual information for better edge identification. Balance loss weight is also proposed for edge loss in the training phase. Experimental results reveal that ESNet achieves better segmentation performance in comparison with other competing methods, which can be a potential baseline network for end-to-end chromosome instance segmentation.