Understanding sickle cell dynamics requires accurate identification of morphological transitions under diverse biophysical conditions, particularly in densely packed and overlapping cell populations. Here, we present an automated deep learning framework that integrates AI-assisted annotation, segmentation, classification, and instance counting to quantify red blood cell (RBC) populations across varying density regimes in time-lapse microscopy data. Experimental images were annotated using the Roboflow platform to generate labeled dataset for training an nnU-Net segmentation model. The trained network enables prediction of the temporal evolution of the sickle cell fraction, while a watershed algorithm resolves overlapping cells to enhance quantification accuracy. Despite requiring only a limited amount of labeled data for training, the framework achieves high segmentation performance, effectively addressing challenges associated with scarce manual annotations and cell overlap. By quantitatively tracking dynamic changes in RBC morphology, this approach can more than double the experimental throughput via densely packed cell suspensions, capture drug-dependent sickling behavior, and reveal distinct mechanobiological signatures of cellular morphological evolution. Overall, this AI-driven framework establishes a scalable and reproducible computational platform for investigating cellular biomechanics and assessing therapeutic efficacy in microphysiological systems.

翻译：理解镰状细胞动态需要在不同生物物理条件下准确识别形态转变，特别是在密集堆积和重叠的细胞群体中。本文提出了一种自动化深度学习框架，该框架整合了AI辅助标注、分割、分类和实例计数，以量化延时显微数据中不同密度区域下的红细胞群体。实验图像使用Roboflow平台进行标注，生成用于训练nnU-Net分割模型的标记数据集。训练后的网络能够预测镰状细胞比例的时间演化，同时采用分水岭算法解析重叠细胞以提高量化准确性。尽管仅需少量标记数据进行训练，该框架仍实现了较高的分割性能，有效解决了与稀缺人工标注和细胞重叠相关的挑战。通过定量追踪红细胞形态的动态变化，该方法可通过密集细胞悬浮液将实验通量提高一倍以上，捕获药物依赖性的镰状化行为，并揭示细胞形态演化的独特力生物学特征。总体而言，这一AI驱动框架建立了一个可扩展且可重复的计算平台，用于研究细胞生物力学及评估微生理系统中的治疗效果。