论文标题
通过从解剖本地区域学习来改善骨骼年龄评估
Improve bone age assessment by learning from anatomical local regions
论文作者
论文摘要
作为基本成像检查，骨骼骨骼年龄评估（BAA）旨在评估人骨骼的生物学和结构成熟。在临床实践中，坦纳（Tanner）和怀特豪斯（TW2）方法是放射科医生进行BAA的广泛使用方法。 TW2方法将手分解为感兴趣的区域（ROI），并分别分析解剖学ROI以估计骨骼年龄。由于考虑对本地信息的分析，TW2方法在实践中显示出准确的结果。遵循TW2的精神，我们提出了一种新型模型，称为解剖局部感知网络（ALA-NET），以进行自动骨时代评估。在ALA-NET中，引入了解剖局部提取模块以学习手动结构并提取本地信息。此外，我们设计了一种解剖补丁培训策略，以在培训过程中提供额外的正规化。我们的模型可以以端到端的方式共同检测解剖学ROI并估算骨骼年龄。实验结果表明，我们的ALA-NET在公众可用的RSNA数据集中实现了3.91平均绝对误差（MAE）的新最新单个模型性能。由于我们的模型的设计与公认的TW2方法非常一致，因此它可以解释且可靠地用于临床使用。
Skeletal bone age assessment (BAA), as an essential imaging examination, aims at evaluating the biological and structural maturation of human bones. In the clinical practice, Tanner and Whitehouse (TW2) method is a widely-used method for radiologists to perform BAA. The TW2 method splits the hands into Region Of Interests (ROI) and analyzes each of the anatomical ROI separately to estimate the bone age. Because of considering the analysis of local information, the TW2 method shows accurate results in practice. Following the spirit of TW2, we propose a novel model called Anatomical Local-Aware Network (ALA-Net) for automatic bone age assessment. In ALA-Net, anatomical local extraction module is introduced to learn the hand structure and extract local information. Moreover, we design an anatomical patch training strategy to provide extra regularization during the training process. Our model can detect the anatomical ROIs and estimate bone age jointly in an end-to-end manner. The experimental results show that our ALA-Net achieves a new state-of-the-art single model performance of 3.91 mean absolute error (MAE) on the public available RSNA dataset. Since the design of our model is well consistent with the well recognized TW2 method, it is interpretable and reliable for clinical usage.
