IEEE J Biomed Health Inform. 2022 Mar;26(3):1318-1328. doi: 10.1109/JBHI.2021.3104650. Epub 2022 Mar 7.
This study presents a novel approach to automatically perform instant phenotypic assessment of red blood cell (RBC) storage lesion in phase images obtained by digital holographic microscopy. The proposed model combines a generative adversarial network (GAN) with marker-controlled watershed segmentation scheme. The GAN model performed RBC segmentations and classifications to develop ageing markers, and the watershed segmentation was used to completely separate overlapping RBCs. Our approach achieved good segmentation and classification accuracy with a Dice's coefficient of 0.94 at a high throughput rate of about 152 cells per second. These results were compared with other deep neural network architectures. Moreover, our image-based deep learning models recognized the morphological changes that occur in RBCs during storage. Our deep learning-based classification results were in good agreement with previous findings on the changes in RBC markers (dominant shapes) affected by storage duration. We believe that our image-based deep learning models can be useful for automated assessment of RBC quality, storage lesions for safe transfusions, and diagnosis of RBC-related diseases.
本研究提出了一种新颖的方法，可通过数字全息显微镜获得的相图像自动执行对红细胞（RBC）储存损伤的即时表型评估。所提出的模型将生成对抗网络（GAN）与标记控制分水岭分割方案相结合。GAN 模型执行 RBC 分割和分类以开发老化标记，分水岭分割用于完全分离重叠的 RBC。我们的方法以约每秒 152 个细胞的高通量实现了良好的分割和分类准确性，Dice 系数为 0.94。这些结果与其他深度神经网络架构进行了比较。此外，我们基于图像的深度学习模型识别了 RBC 在储存过程中发生的形态变化。我们基于深度学习的分类结果与先前关于受储存时间影响的 RBC 标记（主导形状）变化的发现吻合较好。我们相信，我们基于图像的深度学习模型可用于自动评估 RBC 质量、储存损伤以确保输血安全以及诊断与 RBC 相关的疾病。
