Bone skeleton segmentation is a fundamental step in medical image analysis applications, such as computer-aided orthopedic surgery, fracture detection, detecting and diagnosing bone pathology and degenerative diseases. The extraction of bones in CT scans is a challenging task, which done manually by experts is a time-consuming process. In this work, a deep learning (DL) based solution for automatic segmentation of skeletal structure in conventional CT images is presented. To address the task of creating a diverse, high quality training dataset, an iterative data annotation process is utilized. A small training dataset is created using human annotation effort and used to train a segmentation model. The model is then inferred to initialize the ground truths for new cases. The new ground truths are reviewed and edited as necessary by the human annotators and added to the training dataset. The process is repeated until the model performance no longer improves on a held-out validation dataset. Within a few iterations the model generalization and prediction performance are observed to improve as a function of training dataset size and variety. Human effort in the dataset labeling process is also noted to reduce significantly for every interaction. The final DL segmentation models perform well across anatomy, scan and reconstruction settings and achieve a mean dice score of 0.988 on a held out, independent validation dataset.
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