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Fully-automatic deep learning-based analysis for determination of the invasiveness of breast cancer cells in an acoustic trap

Title
Fully-automatic deep learning-based analysis for determination of the invasiveness of breast cancer cells in an acoustic trap
Authors
Youn, SangyeonLee, KyungsuSon, JeehoonYang, In-HwanHwang, Jae Youn
DGIST Authors
Youn, Sangyeon; Lee, Kyungsu; Son, Jeehoon; Yang, In-Hwan; Hwang, Jae Youn
Issue Date
2020-06
Citation
Biomedical Optics Express, 11(6), 2976-2995
Type
Article
Article Type
Article
Keywords
TRACKINGMICROSCOPY
ISSN
2156-7085
Abstract
A single-beam acoustic trapping technique has been shown to be very useful for determining the invasiveness of suspended breast cancer cells in an acoustic trap with a manual calcium analysis method. However, for the rapid translation of the technology into the clinic, the development of an efficient/accurate analytical method is needed. We, therefore, develop a fully-automatic deep learning-based calcium image analysis algorithm for determining the invasiveness of suspended breast cancer cells using a single-beam acoustic trapping system. The algorithm allows to segment cells, find trapped cells, and quantify their calcium changes over time. For better segmentation of calcium fluorescent cells even with vague boundaries, a novel deep learning architecture with multi-scale/multi-channel convolution operations (MM-Net) is devised and constructed by a target inversion training method. The MM-Net outperforms other deep learning models in the cell segmentation. Also, a detection/quantification algorithm is developed and implemented to automatically determine the invasiveness of a trapped cell. For the evaluation of the algorithm, it is applied to quantify the invasiveness of breast cancer cells. The results show that the algorithm offers similar performance to the manual calcium analysis method for determining the invasiveness of cancer cells, suggesting that it may serve as a novel tool to automatically determine the invasiveness of cancer cells with high-efficiency. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.
URI
http://hdl.handle.net/20.500.11750/12857
DOI
10.1364/BOE.390558
Publisher
The Optical Society
Related Researcher
  • Author Hwang, Jae Youn MBIS(Multimodal Biomedical Imaging and System) Laboratory
  • Research Interests Multimodal Imaging; High-Frequency Ultrasound Microbeam; Ultrasound Imaging and Analysis; 스마트 헬스케어; Biomedical optical system
Files:
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Collection:
Department of Electrical Engineering and Computer ScienceMBIS(Multimodal Biomedical Imaging and System) Laboratory1. Journal Articles


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