SARA: Automated Anatomical Reference Assignment of Lower Limb on MRI scans via ANN-based Bone Segmentation

Abstract

<ns3:p>This study proposes a methodology for the automated localisation of lower limb bones on T1 weighted MRI scans, employing a deep learning (DL) approach. The primary objective is to facilitate precise identification of skeletal structures, thereby supporting radiomics based diagnostics of neuromuscular disorders. The developed framework is not confined to the recognition of lower limb bones. A dataset of 1,243 MRI scans was used, with a subset of 29 manually labelled bone segmentations of six key lower limb bone classes. Axial slices were divided into training (2,283), validation (300), and hold-out (378) sets. A two part segmentation pipeline was developed using a combination of U-Net and ResNet architectures, with a custom cost function to handle variable label presence across slices. A novel method for obtaining precise bone-related slice location on the MRI volume was developed. Segmentation quality for Tibia and Femur was high, achieving 86.04% and 86.97% Dice score on the hold-out subset. The ResNet classifier correctly identified the defined regions on the volume, achieving AUCs over 97% on the hold-out subset for most leg fragments except for the knee label. This work introduces a new method for anatomical spatial localisation estimation in MRI scans. Unlike previous studies, which could only recognise body parts, the proposed method also estimates the precise bone-related slice location within the scanned volume, providing an added layer of anatomical context. The developed pipeline can be retrained for other body fragments. This solution exhibits a strong potential for use in clinical workflows, especially for studies involving musculoskeletal diseases.</ns3:p>

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