Department of Applied Mathematics and Statistics, Stony Brook University, NY, 11794, United States.
Department of Applied Mathematics and Statistics, Stony Brook University, NY, 11794, United States; Department of Biomedical Engineering, Stony Brook University, NY, 11794, United States.
Comput Med Imaging Graph. 2021 Apr;89:101895. doi: 10.1016/j.compmedimag.2021.101895. Epub 2021 Mar 11.
We developed a fast and accurate deep learning approach employing a semi-unsupervised learning system (SULS) for capturing the real-time noisy, sparse, and ambiguous images of platelet activation. Outperforming several leading supervised learning methods when applied to segment various platelet morphologies, the SULS detects their complex boundaries at submicron resolutions and it massively decreases to only a few hours for segmenting streaming images of 45 million platelets that would have taken 40 years to annotate manually. For the first time, the fast dynamics of pseudopod formation and platelet morphological changes including membrane tethers and transient tethering to vessels are accurately captured.
我们开发了一种快速而准确的深度学习方法，采用半监督学习系统 (SULS) 来捕捉血小板激活的实时嘈杂、稀疏和模糊图像。当应用于分割各种血小板形态时，SULS 优于几种领先的监督学习方法，它可以以亚微米分辨率检测其复杂边界，并且将分割 4500 万个血小板的流式图像所需的时间从 40 年减少到仅几个小时。这是第一次准确地捕捉到伪足形成和血小板形态变化的快速动态，包括膜系绳和与血管的短暂系绳。
