Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171790
Title: Chromosome detection in metaphase cell images using morphological priors
Authors: Wang, Jun
Zhou, Chengfeng
Chen, Songchang
Hu, Jianwu
Wu, Minghui
Jiang, Xudong
Xu, Chenming
Qian, Dahong
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2023
Source: Wang, J., Zhou, C., Chen, S., Hu, J., Wu, M., Jiang, X., Xu, C. & Qian, D. (2023). Chromosome detection in metaphase cell images using morphological priors. IEEE Journal of Biomedical and Health Informatics, 27(9), 4579-4590. https://dx.doi.org/10.1109/JBHI.2023.3286572
Journal: IEEE Journal of Biomedical and Health Informatics
Abstract: Reliable chromosome detection in metaphase cell (MC) images can greatly alleviate the workload of cytogeneticists for karyotype analysis and the diagnosis of chromosomal disorders. However, it is still an extremely challenging task due to the complicated characteristics of chromosomes, e.g., dense distributions, arbitrary orientations, and various morphologies. In this article, we propose a novel rotated-anchor-based detection framework, named DeepCHM, for fast and accurate chromosome detection in MC images. Our framework has three main innovations: 1) A deep saliency map representing chromosomal morphological features is learned end-to-end with semantic features. This not only enhances the feature representations for anchor classification and regression but also guides the anchor setting to significantly reduce redundant anchors. This accelerates the detection and improves the performance; 2) A hardness-aware loss weights the contribution of positive anchors, which effectively reinforces the model to identify hard chromosomes; 3) A model-driven sampling strategy addresses the anchor imbalance issue by adaptively selecting hard negative anchors for model training. In addition, a large-scale benchmark dataset with a total of 624 images and 27,763 chromosome instances was built for chromosome detection and segmentation. Extensive experimental results demonstrate that our method outperforms most state-of-the-art (SOTA) approaches and successfully handles chromosome detection, with an AP score of 93.53%.
URI: https://hdl.handle.net/10356/171790
ISSN: 2168-2194
DOI: 10.1109/JBHI.2023.3286572
Schools: School of Electrical and Electronic Engineering 
Rights: © 2023 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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