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Yang H, Yu J, Jin L, Zhao Y, Gao Q, Shi C, Ye L, Li D, Yu H, Xu Y. A deep learning based method for automatic analysis of high-throughput droplet digital PCR images. Analyst 2023; 148:239-247. [PMID: 36511172 DOI: 10.1039/d2an01631a] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Droplet digital PCR (ddPCR) is a technique for absolute quantification of nucleic acid molecules and is widely used in biomedical research and clinical diagnosis. ddPCR partitions the reaction solution containing target molecules into a large number of independent microdroplets for amplification and performs quantitative analysis of target molecules by calculating the proportion of positive droplets by the principle of Poisson distribution. Accurate recognition of positive droplets in ddPCR images is of great importance to guarantee the accuracy of target nucleic acid quantitative analysis. However, hand-designed operators are sensitive to interference and have disadvantages such as low contrast, uneven illumination, low sample copy number, and noise, and their accuracy and robustness still need to be improved. Herein, we developed a deep learning-based high-throughput ddPCR droplet detection framework for robust and accurate ddPCR image analysis, and the experimental results show that our method achieves excellent performance in the recognition of positive droplets (99.71%) within a limited time. By combining the Hough transform and a convolutional neural network (CNN), our novel method can automatically filter out invalid droplets that are difficult to be identified by local or global encoding methods and realize high-precision localization and classification of droplets in ddPCR images under variable exposure, contrast, and uneven illumination conditions without the need for image pre-processing and normalization processes.
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Affiliation(s)
- Haixu Yang
- Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027, China. .,Binjiang Institute of Zhejiang University, Hangzhou, 310053, China
| | - Jiahui Yu
- Binjiang Institute of Zhejiang University, Hangzhou, 310053, China
| | - Luhong Jin
- Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027, China.
| | - Yunpeng Zhao
- ZJU-Bioer Technology Research & Development Center, Hangzhou Bioer Technology, Hangzhou, 310053, China
| | - Qi Gao
- ZJU-Bioer Technology Research & Development Center, Hangzhou Bioer Technology, Hangzhou, 310053, China
| | - Changrong Shi
- ZJU-Bioer Technology Research & Development Center, Hangzhou Bioer Technology, Hangzhou, 310053, China
| | - Lei Ye
- ZJU-Bioer Technology Research & Development Center, Hangzhou Bioer Technology, Hangzhou, 310053, China
| | - Dong Li
- ZJU-Bioer Technology Research & Development Center, Hangzhou Bioer Technology, Hangzhou, 310053, China
| | - Hai Yu
- ZJU-Bioer Technology Research & Development Center, Hangzhou Bioer Technology, Hangzhou, 310053, China
| | - Yingke Xu
- Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027, China. .,Binjiang Institute of Zhejiang University, Hangzhou, 310053, China.,Department of Endocrinology, Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Children's Health, Hangzhou, 310051, China
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Datta A, Wai-Kin Kong A, Yow KC. A Fully Automatic Method for Gridding Bright Field Images of Bead-Based Microarrays. IEEE J Biomed Health Inform 2015; 20:1148-59. [PMID: 26011899 DOI: 10.1109/jbhi.2015.2436928] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In this paper, a fully automatic method for gridding bright field images of bead-based microarrays is proposed. There have been numerous techniques developed for gridding fluorescence images of traditional spotted microarrays but to our best knowledge, no algorithm has yet been developed for gridding bright field images of bead-based microarrays. The proposed gridding method is designed for automatic quality control during fabrication and assembly of bead-based microarrays. The method begins by estimating the grid parameters using an evolutionary algorithm. This is followed by a grid-fitting step that rigidly aligns an ideal grid with the image. Finally, a grid refinement step deforms the ideal grid to better fit the image. The grid fitting and refinement are performed locally and the final grid is a nonlinear (piecewise affine) grid. To deal with extreme corruptions in the image, the initial grid parameter estimation and grid-fitting steps employ robust search techniques. The proposed method does not have any free parameters that need tuning. The method is capable of identifying the grid structure even in the presence of extreme amounts of artifacts and distortions. Evaluation results on a variety of images are presented.
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