Automatic segmentation of magnetic resonance (MR) images is crucial for morphological evaluation of the pediatric musculoskeletal system in clinical practice. However, the accuracy and generalization performance of individual segmentation models are limited due to the restricted amount of annotated pediatric data. Hence, we propose to train a segmentation model on multiple datasets, arising from different parts of the anatomy, in a multi-task and multi-domain learning framework. This approach allows to overcome the inherent scarcity of pediatric data while benefiting from a more robust shared representation. The proposed segmentation network comprises shared convolutional filters, domain-specific batch normalization parameters that compute the respective dataset statistics and a domain-specific segmentation layer. Furthermore, a supervised contrastive regularization is integrated to further improve generalization capabilities, by promoting intra-domain similarity and impose inter-domain margins in embedded space. We evaluate our contributions on two pediatric imaging datasets of the ankle and shoulder joints for bone segmentation. Results demonstrate that the proposed model outperforms state-of-the-art approaches.
翻译:在临床实践中,磁共振图像的自动分解对于对儿科肌肉骨骼系统的形态评估至关重要。然而,个别分解模型的准确性和一般化性能有限,因为附有注释的儿科数据数量有限。因此,我们提议在一个多任务和多领域学习的框架内,对来自解剖不同部分的多个数据集进行分解模型的培训。这一方法能够克服儿科数据固有的稀缺,同时受益于更强有力的共同代表制。拟议的分解网络由共同的转动过滤器、具体领域分批标准化参数组成,这些参数可计算出各自的数据集统计数据和特定领域分层。此外,监督的对比性规范化是一体化的,以便通过促进内部的相似性并在嵌入空间中强加内部的跨部空间。我们评估了我们对两套用于骨骼分解的脚踝和肩部合的儿成型成型成型成型成型成型数据集的贡献。结果显示,拟议的模型优于状态。