Classification of red cell dynamics with convolutional and recurrent neural networks: a sickle cell disease case study.
Classification of red cell dynamics with convolutional and recurrent neural networks: a sickle cell disease case study.
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DOI：
10.1038/s41598-023-27718-w
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发表时间：
2023-01-13
影响因子：
4.6
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中科院分区：
文献类型：
--
作者：
红细胞在低剪切流下采取特定运动的分数是用于监测镰状细胞病患者的临床状态的有前途的廉价标记物。它的高通量测量依赖于对每个血液样本的数千个细胞运动的视频分析，以消除绝大多数不可靠的样本（失焦或重叠的细胞），并区分坦克踩踏和翻转运动，分别表征高度和不良变形的细胞。此外，这些视频具有不同的持续时间（从6帧到100多帧）。我们提出了一个两阶段的端到端机器学习管道，能够自动分类视频中具有高类别不平衡的细胞运动。通过扩展、比较和结合两种最先进的方法，卷积神经网络（CNN）模型和递归CNN，我们能够自动丢弃97%的不可靠细胞序列（第一阶段），并以97%的准确率和0.94的F1分数（第二阶段）对高度和较差变形的红细胞序列进行分类。数据集和代码已向社区公开发布。
The fraction of red blood cells adopting a specific motion under low shear flow is a promising inexpensive marker for monitoring the clinical status of patients with sickle cell disease. Its high-throughput measurement relies on the video analysis of thousands of cell motions for each blood sample to eliminate a large majority of unreliable samples (out of focus or overlapping cells) and discriminate between tank-treading and flipping motion, characterizing highly and poorly deformable cells respectively. Moreover, these videos are of different durations (from 6 to more than 100 frames). We present a two-stage end-to-end machine learning pipeline able to automatically classify cell motions in videos with a high class imbalance. By extending, comparing, and combining two state-of-the-art methods, a convolutional neural network (CNN) model and a recurrent CNN, we are able to automatically discard 97% of the unreliable cell sequences (first stage) and classify highly and poorly deformable red cell sequences with 97% accuracy and an F1-score of 0.94 (second stage). Dataset and codes are publicly released for the community.
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影响因子：
4
作者：
Atwell S;Badens C;Charrier A;Helfer E;Viallat A
通讯作者：
Viallat A
影响因子：
4
作者：
Faivre, Magalie;Renoux, Celine;Connes, Philippe
通讯作者：
Connes, Philippe
影响因子：
4.6
作者：
Cordasco, Daniel;Bagchi, Prosenjit
通讯作者：
Bagchi, Prosenjit
影响因子：
4.6
作者：
Chen CL;Mahjoubfar A;Tai LC;Blaby IK;Huang A;Niazi KR;Jalali B
通讯作者：
Jalali B
影响因子：
12.8
作者：
Kucukal E;Man Y;Hill A;Liu S;Bode A;An R;Kadambi J;Little JA;Gurkan UA
通讯作者：
Gurkan UA