Re-Identification Based Automatic Matching and Annotation of Chromosome

Karyotyping of human chromosomes generally consists of three steps: pre-processing, segmentation and classification. By analyzing the number and structure of chromosomes, diseases such as cancers and genetic disorders can be diagnosed. Besides the traditional methods, The Convolutional Neural Network have improved the computer vision area dramatically. When it comes to chromosome karyotyping, few research methods have been proposed to solve the problem of segmentation and classification. This paper proposes an innovative automatic strategy named Chromosome-Automatic-Annotation (CAA) model, which labels the single chromosomes in microscopic images by: 1) applying a joint loss consists of softmax loss and center loss to enlarge the distance of features among the 24 classes; 2) employing the similarity matrix to annotate the single chromosome images in Query Queue with the single chromosome in Gallery Queue. With a dataset of 90624 single chromosome images, after 50 epoch training, the proposed model reached an accuracy of 98.75% for automatic annotation of the chromosome images on a test set of 644 images.