Automated White Blood Cell Counting in Nailfold Capillary Using Deep Learning Segmentation and Video Stabilization
1
Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea
2
School of Applied Science, Telkom University, Bandung 40257, Indonesia
3
Department of Electrical Communication, Daelim University, Anyang-Si 13916, Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sensors 2020, 20(24), 7101; https://doi.org/10.3390/s20247101
Received: 14 October 2020
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Revised: 30 November 2020
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Accepted: 9 December 2020
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Published: 11 December 2020
(This article belongs to the Special Issue Engineering Solutions for Digital Healthcare: From Health Monitoring to Health Enhancement)
White blood cells (WBCs) are essential components of the immune system in the human body. Various invasive and noninvasive methods to monitor the condition of the WBCs have been developed. Among them, a noninvasive method exploits an optical characteristic of WBCs in a nailfold capillary image, as they appear as visual gaps. This method is inexpensive and could possibly be implemented on a portable device. However, recent studies on this method use a manual or semimanual image segmentation, which depends on recognizable features and the intervention of experts, hindering its scalability and applicability. We address and solve this problem with proposing an automated method for detecting and counting WBCs that appear as visual gaps on nailfold capillary images. The proposed method consists of an automatic capillary segmentation method using deep learning, video stabilization, and WBC event detection algorithms. Performances of the three segmentation algorithms (manual, conventional, and deep learning) with/without video stabilization were benchmarks. Experimental results demonstrate that the proposed method improves the performance of the WBC event counting and outperforms conventional approaches.
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Keywords:
deep learning; image registration; semantic segmentation; video stabilization; white blood cell counting
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MDPI and ACS Style
Kim, B.; Hariyani, Y.-S.; Cho, Y.-H.; Park, C. Automated White Blood Cell Counting in Nailfold Capillary Using Deep Learning Segmentation and Video Stabilization. Sensors 2020, 20, 7101. https://doi.org/10.3390/s20247101
AMA Style
Kim B, Hariyani Y-S, Cho Y-H, Park C. Automated White Blood Cell Counting in Nailfold Capillary Using Deep Learning Segmentation and Video Stabilization. Sensors. 2020; 20(24):7101. https://doi.org/10.3390/s20247101
Chicago/Turabian StyleKim, Byeonghwi, Yuli-Sun Hariyani, Young-Ho Cho, and Cheolsoo Park. 2020. "Automated White Blood Cell Counting in Nailfold Capillary Using Deep Learning Segmentation and Video Stabilization" Sensors 20, no. 24: 7101. https://doi.org/10.3390/s20247101
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