Objective: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images. Materials and Methods: A fully convolutional network-based segmentation system was developed using a training dataset of 883 CT scans from 467 subjects. Axial CT images obtained at the inferior endplate level of the 3rd lumbar vertebra were used for the analysis. Manually drawn segmentation maps of the skeletal muscle, visceral fat, and subcutaneous fat were created to serve as ground truth data. The performance of the fully convolutional network-based segmentation system was evaluated using the Dice similarity coefficient and cross-sectional area error, for both a separate internal validation dataset (426 CT scans from 308 subjects) and an external validation dataset (171 CT scans from 171 subjects from two outside hospitals). Results: The mean Dice similarity coefficients for muscle, subcutaneous fat, and visceral fat were high for both the internal (0.96, 0.97, and 0.97, respectively) and external (0.97, 0.97, and 0.97, respectively) validation datasets, while the mean cross-sectional area errors for muscle, subcutaneous fat, and visceral fat were low for both internal (2.1%, 3.8%, and 1.8%, respectively) and external (2.7%, 4.6%, and 2.3%, respectively) validation datasets. Conclusion: The fully convolutional network-based segmentation system exhibited high performance and accuracy in the automatic segmentation of abdominal muscle and fat on CT images.

It has been suggested that endoplasmic reticulum (ER) stress facilitates fibrotic remodeling. Therefore, modulation of ER stress may serve as one of the possible therapeutic approaches to renal fibrosis. We examined whether and how activation of AMP-activated protein kinase (AMPK) suppressed ER stress induced by chemical ER stress inducers [tunicamycin (TM) and thapsigargin (TG)] and also nonchemical inducers in tubular HK-2 cells. We further investigated the in vivo effects of AMPK on ER stress and renal fibrosis. Western blot analysis, immunofluorescence, small interfering (si)RNA experiments, and immunohistochemical staining were performed. Metformin (the best known clinical activator of AMPK) suppressed TM- or TG-induced ER stress, as shown by the inhibition of TM- or TG-induced upregulation of glucose-related protein (GRP)78 and phosphorylated eukaryotic initiation factor-2α through induction of heme oxygenase-1. Metformin inhibited TM- or TG-induced epithelial-mesenchymal transitions as well. Compound C (AMPK inhibitor) blocked the effect of metformin, and 5-aminoimidazole-4-carboxamide-1β riboside (another AMPK activator) exerted the same effects as metformin. Transfection with siRNA targeting AMPK blocked the effect of metformin. Consistent with the results of cell culture experiments, metformin reduced renal cortical GRP78 expression and increased heme oxygenase-1 expression in a mouse model of ER stress-induced acute kidney injury by TM. Activation of AMPK also suppressed ER stress by transforming growth factor-β, ANG II, aldosterone, and high glucose. Furthermore, metformin reduced GRP78 expression and renal fibrosis in a mouse model of unilateral ureteral obstruction. In conclusion, AMPK may serve as a promising therapeutic target through reducing ER stress and renal fibrosis.