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.
Keywords: deep learning; image registration; semantic segmentation; video stabilization; white blood cell counting.