MitoCellPhe reveals mitochondrial morphologies in single fibroblasts and clustered stem cells.

Mitochondria are dynamic organelles that differ significantly in their morphologies across cell types, reflecting specific cellular needs and stages in development. Despite the wide biological significance in disease and in health, delineating mitochondrial morphologies in complex systems remains challenging. Here, we present the Mitochondrial Cellular Phenotype (MitoCellPhe) tool developed for quantifying mitochondrial morphologies and demonstrate its utility in delineating differences in mitochondrial morphologies in a human fibroblast and human induced pluripotent stem cell (hiPSC) line. MitoCellPhe generates 24 parameters, allowing for a comprehensive analysis of mitochondrial structures and importantly allows for quantification to be performed on mitochondria in images containing single cells or clusters of cells. With this tool, we were able to validate previous findings that show networks of mitochondria in healthy fibroblast cell lines and a more fragmented morphology in hiPSCs. Using images generated from control and diseased fibroblasts and hiPSCs, we also demonstrate the efficacy of the toolset in delineating differences in morphologies between healthy and the diseased state in both stem cell (hiPSC) and differentiated fibroblast cells. Our results demonstrate that MitoCellPhe enables high-throughput, sensitive, detailed, and quantitative mitochondrial morphological assessment and thus enables better biological insights into mitochondrial dynamics in health and disease.

Learning Medical Materials From Radiography Images.

Deep learning models have been shown to be effective for material analysis, a subfield of computer vision, on natural images. In medicine, deep learning systems have been shown to more accurately analyze radiography images than algorithmic approaches and even experts. However, one major roadblock to applying deep learning-based material analysis on radiography images is a lack of material annotations accompanying image sets. To solve this, we first introduce an automated procedure to augment annotated radiography images into a set of material samples. Next, using a novel Siamese neural network that compares material sample pairs, called D-CNN, we demonstrate how to learn a perceptual distance metric between material categories. This system replicates the actions of human annotators by discovering attributes that encode traits that distinguish materials in radiography images. Finally, we update and apply MAC-CNN, a material recognition neural network, to demonstrate this system on a dataset of knee X-rays and brain MRIs with tumors. Experiments show that this system has strong predictive power on these radiography images, achieving 92.8% accuracy at predicting the material present in a local region of an image. Our system also draws interesting parallels between human perception of natural materials and materials in radiography images.