Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network
arXiv:2009.07092 · doi:10.1016/j.artmed.2022.102364
Abstract
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.
21 pages, 11 figures, 7 tables, Accepted for publication in the Journal of Artificial Intelligence in Medicine
References in corpus (11)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
- Transfusion: Understanding Transfer Learning for Medical Imaging
- Regularization for Deep Learning: A Taxonomy
- Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation
- Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation
- Incorporating prior knowledge in medical image segmentation: a survey
- Combining Shape Priors with Conditional Adversarial Networks for Improved Scapula Segmentation in MR images
- Critical Assessment of Transfer Learning for Medical Image Segmentation with Fully Convolutional Neural Networks
- Effective 3D Humerus and Scapula Extraction using Low-contrast and High-shape-variability MR Data