DeepACEv2: Automated Chromosome Enumeration in Metaphase Cell Images Using Deep Convolutional Neural Networks
| Autor: | Yinhao Li, Li Xiao, Chan Tian, Jie Qiao, Chunlong Luo, Yufan Luo, Tianqi Yu, Fuhai Yu, Manqing Wang |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science Computer Vision and Pattern Recognition (cs.CV) Feature extraction Computer Science - Computer Vision and Pattern Recognition Convolutional neural network Chromosomes 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Enumeration Pyramid (image processing) Electrical and Electronic Engineering Metaphase Radiological and Ultrasound Technology business.industry Deep learning Chromosome Pattern recognition Karyotype Function (mathematics) Object detection Computer Science Applications Feature (computer vision) Karyotyping Neural Networks Computer Artificial intelligence business Software |
| Zdroj: | IEEE Transactions on Medical Imaging. 39:3920-3932 |
| ISSN: | 1558-254X 0278-0062 |
| DOI: | 10.1109/tmi.2020.3007642 |
| Popis: | Chromosome enumeration is an essential but tedious procedure in karyotyping analysis. To automate the enumeration process, we develop a chromosome enumeration framework, DeepACEv2, based on the region based object detection scheme. The framework is developed following three steps. Firstly, we take the classical ResNet-101 as the backbone and attach the Feature Pyramid Network (FPN) to the backbone. The FPN takes full advantage of the multiple level features, and we only output the level of feature map that most of the chromosomes are assigned to. Secondly, we enhance the region proposal network's ability by adding a newly proposed Hard Negative Anchors Sampling to extract unapparent but essential information about highly confusing partial chromosomes. Next, to alleviate serious occlusion problems, besides the traditional detection branch, we novelly introduce an isolated Template Module branch to extract unique embeddings of each proposal by utilizing the chromosome's geometric information. The embeddings are further incorporated into the No Maximum Suppression (NMS) procedure to improve the detection of overlapping chromosomes. Finally, we design a Truncated Normalized Repulsion Loss and add it to the loss function to avoid inaccurate localization caused by occlusion. In the newly collected 1375 metaphase images that came from a clinical laboratory, a series of ablation studies validate the effectiveness of each proposed module. Combining them, the proposed DeepACEv2 outperforms all the previous methods, yielding the Whole Correct Ratio(WCR)(%) with respect to images as 71.39, and the Average Error Ratio(AER)(%) with respect to chromosomes as about 1.17. |
| Databáze: | OpenAIRE |
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