论文标题
先前引导的深度差异元学习者，以快速适应对程式化的细分
Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation
论文作者
论文摘要
当在新机构中部署预先培训的一般自动分割模型时，提议的先前引入的DDL网络中的支持框架将了解模型预测与最初的最初患者临床医生修订和批准的最终轮廓之间的系统差异。博学的样式特征差异与新患者（查询）功能串联，然后解码以获取样式适应的细分。该模型独立于实践风格和解剖结构。它具有模拟样式差异的元学习，并且在训练过程中无需暴露于任何真实的临床风格结构。一旦对模拟数据进行培训，就可以将其用于临床用途，以适应新的实践样式和新的解剖结构，而无需进一步培训。 为了显示概念证明，我们针对三种不同的解剖结构测试了六种不同实践样式变化的先前引导的DDL网络。从术后临床目标体积（CTV）进行调整到细分CTVStyle1，ctvStyle2和CTVStyle3，从羊毛膜细分到段性小parotidsuprings pertipums ectemumposterior和Rectumposterior。用骰子相似性系数（DSC）量化模式性能。 With adaptation based on only the first three patients, the average DSCs were improved from 78.6, 71.9, 63.0, 52.2, 46.3 and 69.6 to 84.4, 77.8, 73.0, 77.8, 70.5, 68.1, for CTVstyle1, CTVstyle2, and CTVstyle3, Parotidsuperficial, Rectumsuperior,分别显示了直立级的，显示了先验指导的DDL网络的巨大潜力，以快速而轻松地适应新的实践风格
When a pre-trained general auto-segmentation model is deployed at a new institution, a support framework in the proposed Prior-guided DDL network will learn the systematic difference between the model predictions and the final contours revised and approved by clinicians for an initial group of patients. The learned style feature differences are concatenated with the new patients (query) features and then decoded to get the style-adapted segmentations. The model is independent of practice styles and anatomical structures. It meta-learns with simulated style differences and does not need to be exposed to any real clinical stylized structures during training. Once trained on the simulated data, it can be deployed for clinical use to adapt to new practice styles and new anatomical structures without further training. To show the proof of concept, we tested the Prior-guided DDL network on six different practice style variations for three different anatomical structures. Pre-trained segmentation models were adapted from post-operative clinical target volume (CTV) segmentation to segment CTVstyle1, CTVstyle2, and CTVstyle3, from parotid gland segmentation to segment Parotidsuperficial, and from rectum segmentation to segment Rectumsuperior and Rectumposterior. The mode performance was quantified with Dice Similarity Coefficient (DSC). With adaptation based on only the first three patients, the average DSCs were improved from 78.6, 71.9, 63.0, 52.2, 46.3 and 69.6 to 84.4, 77.8, 73.0, 77.8, 70.5, 68.1, for CTVstyle1, CTVstyle2, and CTVstyle3, Parotidsuperficial, Rectumsuperior, and Rectumposterior, respectively, showing the great potential of the Priorguided DDL network for a fast and effortless adaptation to new practice styles
