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Proceedings Paper

Applying deep learning technology to automatically identify metaphase chromosomes using scanning microscopic images: an initial investigation
Author(s): Yuchen Qiu; Xianglan Lu; Shiju Yan; Maxine Tan; Samuel Cheng; Shibo Li; Hong Liu; Bin Zheng
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Paper Abstract

Automated high throughput scanning microscopy is a fast developing screening technology used in cytogenetic laboratories for the diagnosis of leukemia or other genetic diseases. However, one of the major challenges of using this new technology is how to efficiently detect the analyzable metaphase chromosomes during the scanning process. The purpose of this investigation is to develop a computer aided detection (CAD) scheme based on deep learning technology, which can identify the metaphase chromosomes with high accuracy. The CAD scheme includes an eight layer neural network. The first six layers compose of an automatic feature extraction module, which has an architecture of three convolution-max-pooling layer pairs. The 1st, 2nd and 3rd pair contains 30, 20, 20 feature maps, respectively. The seventh and eighth layers compose of a multiple layer perception (MLP) based classifier, which is used to identify the analyzable metaphase chromosomes. The performance of new CAD scheme was assessed by receiver operation characteristic (ROC) method. A number of 150 regions of interest (ROIs) were selected to test the performance of our new CAD scheme. Each ROI contains either interphase cell or metaphase chromosomes. The results indicate that new scheme is able to achieve an area under the ROC curve (AUC) of 0.886±0.043. This investigation demonstrates that applying a deep learning technique may enable to significantly improve the accuracy of the metaphase chromosome detection using a scanning microscopic imaging technology in the future.

Paper Details

Date Published: 9 March 2016
PDF: 6 pages
Proc. SPIE 9709, Biophotonics and Immune Responses XI, 97090K (9 March 2016); doi: 10.1117/12.2217418
Show Author Affiliations
Yuchen Qiu, Univ. of Oklahoma (United States)
Xianglan Lu, Univ. of Oklahoma Health Sciences Ctr. (United States)
Shiju Yan, Univ. of Oklahoma (United States)
Univ. of Shanghai for Sciences and Technology (China)
Maxine Tan, Univ. of Oklahoma (United States)
Samuel Cheng, Univ. of Oklahoma, Tulsa (United States)
Shibo Li, Univ. of Oklahoma Health Sciences Ctr. (United States)
Hong Liu, Univ. of Oklahoma (United States)
Bin Zheng, Univ. of Oklahoma (United States)


Published in SPIE Proceedings Vol. 9709:
Biophotonics and Immune Responses XI
Wei R. Chen, Editor(s)

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