Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network
This addresses segmentation challenges for pediatric musculoskeletal disorders, though it appears incremental as it builds on existing regularization techniques.
The authors tackled the problem of segmenting multiple bones in scarce pediatric MR images by proposing a pre-trained regularized convolutional encoder-decoder network with shape priors and adversarial regularization. Their method performed better or comparable to previous approaches on metrics like Dice, sensitivity, and surface distances across ankle and shoulder datasets.
Morphological and diagnostic evaluation of pediatric musculoskeletal system is crucial in clinical practice. However, most segmentation models do not perform well on scarce pediatric imaging data. We propose a new pre-trained regularized convolutional encoder-decoder network for the challenging task of segmenting heterogeneous pediatric magnetic resonance (MR) images. To this end, we have conceived a novel optimization scheme for the segmentation network which comprises additional regularization terms to the loss function. In order to obtain globally consistent predictions, we incorporate a shape priors based regularization, derived from a non-linear shape representation learnt by an auto-encoder. Additionally, an adversarial regularization computed by a discriminator is integrated to encourage precise delineations. The proposed method is evaluated for the task of multi-bone segmentation on two scarce pediatric imaging datasets from ankle and shoulder joints, comprising pathological as well as healthy examinations. The proposed method performed either better or at par with previously proposed approaches for Dice, sensitivity, specificity, maximum symmetric surface distance, average symmetric surface distance, and relative absolute volume difference metrics. We illustrate that the proposed approach can be easily integrated into various bone segmentation strategies and can improve the prediction accuracy of models pre-trained on large non-medical images databases. The obtained results bring new perspectives for the management of pediatric musculoskeletal disorders.