Morphology-based deep learning enables accurate detection of senescence in mesenchymal stem cell cultures.

BACKGROUND: Cell senescence is a sign of aging and plays a significant role in the pathogenesis of age-related disorders. For cell therapy, senescence may compromise the quality and efficacy of cells, posing potential safety risks. Mesenchymal stem cells (MSCs) are currently undergoing extensive research for cell therapy, thus necessitating the development of effective methods to evaluate senescence. Senescent MSCs exhibit distinctive morphology that can be used for detection. However, morphological assessment during MSC production is often subjective and uncertain. New tools are required for the reliable evaluation of senescent single cells on a large scale in live imaging of MSCs. RESULTS: We have developed a successful morphology-based Cascade region-based convolution neural network (Cascade R-CNN) system for detecting senescent MSCs, which can automatically locate single cells of different sizes and shapes in multicellular images and assess their senescence state. Additionally, we tested the applicability of the Cascade R-CNN system for MSC senescence and examined the correlation between morphological changes with other senescence indicators. CONCLUSIONS: This deep learning has been applied for the first time to detect senescent MSCs, showing promising performance in both chronic and acute MSC senescence. The system can be a labor-saving and cost-effective option for screening MSC culture conditions and anti-aging drugs, as well as providing a powerful tool for non-invasive and real-time morphological image analysis integrated into cell production.

Fast and low-dose medical imaging generation empowered by hybrid deep-learning and iterative reconstruction.

Fast and low-dose reconstructions of medical images are highly desired in clinical routines. We propose a hybrid deep-learning and iterative reconstruction (hybrid DL-IR) framework and apply it for fast magnetic resonance imaging (MRI), fast positron emission tomography (PET), and low-dose computed tomography (CT) image generation tasks. First, in a retrospective MRI study (6,066 cases), we demonstrate its capability of handling 3- to 10-fold under-sampled MR data, enabling organ-level coverage with only 10- to 100-s scan time; second, a low-dose CT study (142 cases) shows that our framework can successfully alleviate the noise and streak artifacts in scans performed with only 10% radiation dose (0.61 mGy); and last, a fast whole-body PET study (131 cases) allows us to faithfully reconstruct tumor-induced lesions, including small ones (<4 mm), from 2- to 4-fold-accelerated PET acquisition (30-60 s/bp). This study offers a promising avenue for accurate and high-quality image reconstruction with broad clinical value.

Induced Pluripotent Stem Cells Detection via Ensemble Yolo Network.

Induced pluripotent stem cells (iPSCs) have huge potential in regenerative medicine research and industrial applications. However, building automatic method without using cell staining technique for iPSCs identification is an important challenge. To improve the efficiency of producing iPSCs, we build an accurate and noninvasive iPSCs colonies detection method via ensemble Yolo network based on the self-collected bright-field microscopy images. Meanwhile, test-time augmentation (TTA) is leveraged to further improve the detection result of our iPSCs colonies detection method. Extensive experimental results on our dataset demonstrate that our method obtains quite favorable detection performance with the highest F1 score of 0.867 and the highest mean average precision score of 0.898, which outperforms most mainstream methods.