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Random forests in medical image computing
The Random Forests algorithm had a substantial impact on medical image computing over the last decade. This chapter presents basic algorithmic details, some variations proposed in the recent years and applications in medical image computing. Arguably, Random Forests' main impact was on the analysis tasks that required understanding spatial context within the images.
Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain
Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a fully automated approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study.