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MuscleView is used in adults and pediatics aged 18 and older to automatically segment muscle and bone structures of the lower extremities from magnetic resonance imaging using a machine learning-based approach. After segmentation, it can provide derived metrics including muscle volume, intramuscular fat percentage, and left/right asymmetry. It is intended to be used by physicians who are trained to interpret MRI images, and serves as an initial method to segment muscle and bone structures from one or more study series. The segmentation results need to be reviewed and edited using appropriate software. It is intended to only provide the segmentation and derived metrics for muscle and bone structures and cannot serve as direct guidance for dagnosis of any diseases. This device is not intents who have tumors in lower limb.

MuscleView is a software only product that uses a machine learning-based approach for the automatic segmentation of musculoskeletal structures from MRI. Based on the segmentation, metrics such as volume and length of the segmented structures are calculated. The software has the following modules: user management, data management, image processing, Al segmentation & 3D model viewer and metrics calculation. User management involves authentication and access to the software and its results. Data management involves medical image data and its interactions with the system workflow. Image processing involves Preprocessing the DICOM data to create a combined continuous 3D volume(s) of series with similar settings for use in Al segmentation & 3D model viewer module handles training data and algorithms to obtain the pre-trained models and algorithms to update models. Metric calculation module handles the final calculation of relevant metrics. Input data is preprocessed and prepared for 3D volume segmentation of the musculoskeletal structures. A library of already contoured expert cases is utilized to train the machine learning algorithms, specifically convolutional networks (CNNs) perform automated segmentation. This process is in an auxiliary module for AI training. MuscleView is intended to be used by physicians who are trained to interpret MRI images, and serves as an initial method to segment muscle and bone structures from one or more study series. The segmentation results need to be reviewed and edited using appropriate software. This device is not intended for use with patients who have tumors in lower limb. The currently supported anatomical regions for automatic segmentation are 80 different muscles and bones of the lower extremity. Upon segmentation, a suite of metrics regarding the segmented 3D volumes is provided. It is intended to only provide the segmentation and derived metrics for muscle and bone structures and cannot serve as direct guidance for diagnosis of any diseases. These metrics include segmentation volume, fat infiltration (if applicable), and limb side asymmetry. The metrics are provided in conjunction with an interactive visualization of the 3D segmentation results. The software is deployed within a private network on a workstation with an advanced graphic processing unit (GPU) and runs as a service. A web-based interface is used to access the service and manage the data transfer, automatic segmentation, and visualization.