A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos

Oblique back-illumination capillaroscopy has recently been introduced as an efficient method for non-invasive blood cell imaging in human capillaries. To apply this technique to clinical blood cell counting, solutions for automatic processing of acquired videos are needed. Here, we take the first step towards this goal, by introducing a novel deep learning multi-cell tracking model, named CycleTrack, which achieves accurate blood cell counting from capillaroscopic videos. CycleTrack combines two simple online tracking models, SORT and CenterTrack, and is tailored to features of capillary blood cell flow. Blood cells are tracked by displacement vectors in two opposing temporal directions between consecutive frames. This approach yields accurate tracking despite rapidly moving and deforming blood cells. The proposed model outperforms other baseline trackers, achieving 66.3% MOTA and 75.1% ID Fl score on test videos. CycleTrack achieves an average cell counting error of 3.42% among 8 1000-frame test videos, compared to 6.55% and 22.98% from original CenterTrack and SORT, with negligible time expense. It takes 800s to track and count approximately 8000 blood cells from 9,600 frames captured in a typical one-minute video. Moreover, the blood cell velocity measured by CycleTrack demonstrates a consistent, pulsatile pattern within the physiological range of heart rate. The project code is accessible online at: https://github.com/DurrLab/CycleTrack.