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.
翻译:临床实践对小儿科肌肉骨骼系统进行临床临床实践至关重要。然而,大多数分解模型在缺乏的儿科成像数据方面表现不佳。我们建议建立一个新的先期训练的常规共振编码脱coder网络,以完成将各种小儿科磁共振(MR)图像分解的艰巨任务。为此,我们为分解网络设想了一个新型优化方案,其中包括对损失功能的额外正规化条件。为了获得全球一致的预测,我们采用了一种基于形状的先期正规化,由自动编码器所学的非线性形状代表制产生。此外,由歧视者计算的一种对抗性正规化的正轨调节,将鼓励精确的划界。为了完成从脚踝和肩膀连接中分解两种稀缺的松散的儿科成像数据集的多骨分解任务,拟议的方法包括病理学和健康检查。拟议的方法可以更好或更方便地采用先前提议的关于Dice、敏感性、特征、最高度表面距离、平均测度表面偏差、平均测程图理学分析结果,可以说明大型的骨质分层分析结果。我们提出的地平面和绝对体结构分析方法可以改进各种新的分析。