Author:
Liu Hui,Wang Guangjie,Song Sifan,Huang Daiyun,Zhang Lin
Abstract
Precise segmentation of chromosome in the real image achieved by a microscope is significant for karyotype analysis. The segmentation of image is usually achieved by a pixel-level classification task, which considers different instances as different classes. Many instance segmentation methods predict the Intersection over Union (IoU) through the head branch to correct the classification confidence. Their effectiveness is based on the correlation between branch tasks. However, none of these methods consider the correlation between input and output in branch tasks. Herein, we propose a chromosome instance segmentation network based on regression correction. First, we adopt two head branches to predict two confidences that are more related to localization accuracy and segmentation accuracy to correct the classification confidence, which reduce the omission of predicted boxes in NMS. Furthermore, a NMS algorithm is further designed to screen the target segmentation mask with the IoU of the overlapping instance, which reduces the omission of predicted masks in NMS. Moreover, given the fact that the original IoU loss function is not sensitive to the wrong segmentation, K-IoU loss function is defined to strengthen the penalty of the wrong segmentation, which rationalizes the loss of mis-segmentation and effectively prevents wrong segmentation. Finally, an ablation experiment is designed to evaluate the effectiveness of the chromosome instance segmentation network based on regression correction, which shows that our proposed method can effectively enhance the performance in automatic chromosome segmentation tasks and provide a guarantee for end-to-end karyotype analysis.
Subject
Genetics (clinical),Genetics,Molecular Medicine
Reference43 articles.
1. A Survey of Neural Network Based Automated Systems for Human Chromosome Classification;Abid;Artif. Intell. Rev.,2018
2. Individual Chromosomes as Viscoelastic Copolymers;Almagro;Europhys. Lett.,2003
3. A Tool for the Analysis of Chromosomes: KaryoType;Altınordu;Taxon,2016
4. A Fuzzy-Adaptive Approach to Segment Metaphase Chromosome ImagesAutomatic Segmentation and Karyotyping of Chromosomes Using Bio-Metrics;Andrade,2018
5. A Review of Metaphase Chromosome Image Selection Techniques for Automatic Karyotype Generation;Arora;Med. Biol. Eng. Comput.,2016
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献