Deep Learning-Based Phenotypic Assessment of Red Cell Storage Lesions for Safe Transfusions

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.

Automated summary

This study presents a novel approach using digital holographic microscopy to automatically assess the storage lesion of red blood cells (RBCs). The proposed model combines generative adversarial networks (GANs) with marker-controlled watershed segmentation, achieving accurate segmentation and classification of RBCs and complete separation of overlapping cells. The high-throughput method achieved a Dice's coefficient of 0.94 and recognized morphological changes in RBCs during storage.