论文标题
带有3D U-NET和COX比例危害神经网络的PET/CT体积中头颈癌的分割和风险评分预测
Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks
论文作者
论文摘要
我们利用了一个3D NNU-NEN模型，其残留层补充了挤压和激发（SE）归一化（SE）归一化，以从由头部和颈部肿瘤分割Chal-Lenge（Hecktor）提供的PET/CT图像中进行肿瘤分割。我们提出的损失函数结合了统一的FO-CAL和MUMFORD-SHAH损失，以利用分布，区域和基于边界的损失函数。在不同中心进行的一对中心 - 交叉验证的结果表明，分段性能为0.82平均骰子得分（DSC）和3.16 Hausdorff距离（HD），并且在测试集中获得的结果为0.77 DSC和3.01 HD。病变细分后，我们提出了一种具有MLP神经净主链的病例对照比例危害COX模型，以预测每个离散病变的危险风险评分。该危险风险预测模型（COXCC）应接受从分段病变，患者和病变人口统计学中提取的许多PET/CT放射线特征，以及从多输入2D PET/CT卷积/CT卷积神经网络的倒数第二层提供的编码器特征，该特征是预测每种Lesion的时间到现场的任务。 10倍的交叉验证的COXCC模型导致C-Index验证得分为0.89，而Hecktor挑战测试数据集的C-指数得分为0.61。
We utilized a 3D nnU-Net model with residual layers supplemented by squeeze and excitation (SE) normalization for tumor segmentation from PET/CT images provided by the Head and Neck Tumor segmentation chal-lenge (HECKTOR). Our proposed loss function incorporates the Unified Fo-cal and Mumford-Shah losses to take the advantage of distribution, region, and boundary-based loss functions. The results of leave-one-out-center-cross-validation performed on different centers showed a segmentation performance of 0.82 average Dice score (DSC) and 3.16 median Hausdorff Distance (HD), and our results on the test set achieved 0.77 DSC and 3.01 HD. Following lesion segmentation, we proposed training a case-control proportional hazard Cox model with an MLP neural net backbone to predict the hazard risk score for each discrete lesion. This hazard risk prediction model (CoxCC) was to be trained on a number of PET/CT radiomic features extracted from the segmented lesions, patient and lesion demographics, and encoder features provided from the penultimate layer of a multi-input 2D PET/CT convolutional neural network tasked with predicting time-to-event for each lesion. A 10-fold cross-validated CoxCC model resulted in a c-index validation score of 0.89, and a c-index score of 0.61 on the HECKTOR challenge test dataset.
