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A Lightweight and Robust Framework for Circulating Genetically Abnormal Cells (CACs) Identification Using 4-Color Fluorescence In Situ Hybridization (FISH) Image and Deep Refined Learning

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Abstract

Circulating genetically abnormal cells (CACs) constitute an important biomarker for cancer diagnosis and prognosis. This biomarker offers high safety, low cost, and high repeatability, which can serve as a key reference in clinical diagnosis. These cells are identified by counting fluorescence signals using 4-color fluorescence in situ hybridization (FISH) technology, which has a high level of stability, sensitivity, and specificity. However, there are some challenges in CACs identification, due to the difference in the morphology and intensity of staining signals. In this concern, we developed a deep learning network (FISH-Net) based on 4-color FISH image for CACs identification. Firstly, a lightweight object detection network based on the statistical information of signal size was designed to improve the clinical detection rate. Secondly, the rotated Gaussian heatmap with a covariance matrix was defined to standardize the staining signals with different morphologies. Then, the heatmap refinement model was proposed to solve the fluorescent noise interference of 4-color FISH image. Finally, an online repetitive training strategy was used to improve the model’s feature extraction ability for hard samples (i.e., fracture signal, weak signal, and adjacent signals). The results showed that the precision was superior to 96%, and the sensitivity was higher than 98%, for fluorescent signal detection. Additionally, validation was performed using the clinical samples of 853 patients from 10 centers. The sensitivity was 97.18% (CI 96.72–97.64%) for CACs identification. The number of parameters of FISH-Net was 2.24 M, compared to 36.9 M for the popularly used lightweight network (YOLO-V7s). The detection speed was about 800 times greater than that of a pathologist. In summary, the proposed network was lightweight and robust for CACs identification. It could greatly increase the review accuracy, enhance the efficiency of reviewers, and reduce the review turnaround time during CACs identification.

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Data Availability

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

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Funding

This work was supported by the National Natural Science Foundation of China [grant number 61971445 & 62271508].

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Authors and Affiliations

  1. China Academy of Information and Communications Technology, No.52, Huayuan bei Road, 100191, Beijing, China

    Xu Xu, Congsheng Li & Tongning Wu

  2. Zhuhai Sanmed Biotech Ltd, Zhuhai, 519060, Guangdong, China

    Xingjie Lan, Xianjun Fan, Xing Lv & Xin Ye

Authors
  1. Xu Xu
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  2. Congsheng Li
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  3. Xingjie Lan
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  4. Xianjun Fan
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  7. Tongning Wu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Xianjun Fan, Xinjie Lan, Xin Ye, and Xing Lu. Methodology and validation were performed by Xu Xu and Congsheng Li. Review and editing were performed by Tongning Wu. The first draft of the manuscript was written by Xu Xu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Tongning Wu.

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Xu, X., Li, C., Lan, X. et al. A Lightweight and Robust Framework for Circulating Genetically Abnormal Cells (CACs) Identification Using 4-Color Fluorescence In Situ Hybridization (FISH) Image and Deep Refined Learning. J Digit Imaging 36, 1687–1700 (2023). https://doi.org/10.1007/s10278-023-00843-8

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  • DOI: https://doi.org/10.1007/s10278-023-00843-8

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