Muscle-tendon paths vary between individuals, and musculoskeletal models show sensitivity to these variations when estimating muscle and joint forces. Accurate estimations of these internal forces allow for a more comprehensive approach to researching debilitating musculoskeletal pathologies such as osteoarthritis. However, defining subject-specific muscle-tendon paths is labour-intensive and requires expert knowledge, which is not as repeatable. Therefore, in this study, the accuracy of determining subject-specific muscle points and volumes based on lower limb Magnetic Resonance (MR) scans with the nnU-net is evaluated. Two models are trained using the open-source pipeline, referred to as the point and volume model. The volume model aims to segment muscle-tendon volumes and is trained on the open-source augmented dataset of Henson et al. (2023). The point model localises attachment and via points describing muscle action lines for a subset of relevant muscles of the volume model. Since U-net is not designed for predicting points, a workaround is introduced by creating cubes around the points as labels. The 3D volume model scored a median Dice Similarity Coecient of 92.7 % and shows some generalisation capability. The 3D point model scored a median Euclidean error of 5.1 mm. Compared to intra-operator variability for attachment points, this approach yields lower and more repeatable accuracy without required manual intervention. The nnU-net pipeline is capable of producing accurate models that can define subject-specific muscle-tendon paths based on MR scans.