This project aims at developing an artificial intelligence-based approach for the automated extraction of patient-specific information on spinal muscle morphology from medical images.
The successful treatment of spinal pathologies depends on well-founded knowledge of the disease mechanisms. Unfortunately, many spinal pathologies are not sufficiently well understood and treatment effects are controversial. This is partially due to the fact that relevant biomechanical parameters such as segmental compressive forces can only be measured in vivo using highly inva-sive approaches. Due to the technical advances in the past two decades though, this can now be achieved using complex digital technologies such as musculoskeletal (MSK) modeling. However, MSK modeling is only accurate if the models are patient-specific, particularly in terms of muscle properties. To adjust such parameters, we usually rely on manually segmented CT scans, but these processes are highly time-consuming and therefore not applicable for research on larger samples or for everyday clinical practice. A possible way to speed up these processes might be to use an artificial intelligence (AI) approach such as deep learning, which uses biologically-inspired neural networks that enable computers to learn from observational data. A well trained neural network could thereby reduce the time needed for image segmentation from several hours to a couple of seconds per patient. The overall aim of the project is to create, train and verify a neural network for the automated ex-traction of patient-specific paraspinal muscle morphology from CT scans to allow for appropriate simulation of spinal loading usin
To be able to train the neural network appropriately, we will establish a training dataset consisting of approximately 200 trunk CT/MRI scans of healthy children and adolescents. To extract the required training data, scans will be manually segmented, including the assessment of trunk muscle morphology by determining muscle cross-sectional area (CSA) and relative location for each horizontal vertebral mid-plane using a custom-built image processing software. Using our training dataset, we will then retrain a ready trained convolutional neural network (CNN) for the recognition of vertebral body as well as erector spinae and multifidi muscle contours. The process of retraining a ready trained network is called transfer learning and takes far fewer epochs and results in better accuracy. Finally, muscle morphology data will be implemented into our MSK models using a previously established MATLAB-pipeline.