论文标题
深度学习双向时间跟踪算法，用于自动化血细胞的非侵入性毛细管镜检查视频
A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos
论文作者
论文摘要
最近引入了斜后刷毛细血管镜检查，作为人类毛细血管中高质量，非侵入性血细胞成像的一种方法。为了使这项技术用于临床血细胞计数，需要自动处理获得视频的解决方案。在这里，我们通过引入一个名为Cycletrack的深度学习多细胞跟踪模型迈出了这一目标的第一步，该模型可从毛细管视频中获得准确的血细胞计数。 Cycletrack结合了两个简单的在线跟踪模型，分类和中心轨道，并根据毛细血管流量的特征量身定制。连续帧之间的两个相反的时间方向（向前和向后跟踪）在两个相对的时间方向（向前和向后跟踪）中跟踪血细胞。尽管迅速移动并变形了血细胞，但这种方法仍会产生准确的跟踪。提出的模型优于其他基线跟踪器，在测试视频中获得了65.57％的多个对象跟踪准确性和73.95％ID F1得分。与手动血细胞计数相比，Cycletrack达到96.58 $ \ pm $ 2.43％的细胞计数精度，每个测试视频具有1000帧，而独立中心网格的精度为93.45％和77.02％，几乎没有额外的时间费用。从典型的一分钟视频中捕获的9,600帧跟踪和计算大约8000个血细胞需要800秒。此外，通过Cycletrack测得的血细胞速度在心率的生理范围内表现出一致的脉动模式。最后，我们讨论了Cycletrack框架的未来改进，该框架将使倾斜的背刷显微镜临床翻译向实时且无创的护理血细胞计数和分析技术。
Oblique back-illumination capillaroscopy has recently been introduced as a method for high-quality, non-invasive blood cell imaging in human capillaries. To make this technique practical for 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 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 (forward- and backward-tracking) between consecutive frames. This approach yields accurate tracking despite rapidly moving and deforming blood cells. The proposed model outperforms other baseline trackers, achieving 65.57% Multiple Object Tracking Accuracy and 73.95% ID F1 score on test videos. Compared to manual blood cell counting, CycleTrack achieves 96.58 $\pm$ 2.43% cell counting accuracy among 8 test videos with 1000 frames each compared to 93.45% and 77.02% accuracy for independent CenterTrack and SORT almost without additional 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. Lastly, we discuss future improvements for the CycleTrack framework, which would enable clinical translation of the oblique back-illumination microscope towards a real-time and non-invasive point-of-care blood cell counting and analyzing technology.
