Radiotherapy has become a common treatment option for head and neck (H&N) cancer, and organs at risk (OARs) need to be delineated to implement a high conformal dose distribution. Manual drawing of OARs is time consuming and inaccurate, so automatic drawing based on deep learning models has been proposed to accurately delineate the OARs. However, state-of-the-art performance usually requires a decent amount of delineation, but collecting pixel-level manual delineations is labor intensive and may not be necessary for representation learning. Encouraged by the recent progress in self-supervised learning, this study proposes and evaluates a novel multiview contrastive representation learning to boost the models from unlabelled data. The proposed learning architecture leverages three views of CTs (coronal, sagittal, and transverse plane) to collect positive and negative training samples. Specifically, a CT in 3D is first projected into three 2D views (coronal, sagittal, and transverse planes), then a convolutional neural network takes 3 views as inputs and outputs three individual representations in latent space, and finally, a contrastive loss is used to pull representation of different views of the same image closer (“positive pairs”) and push representations of views from different images (“negative pairs”) apart. To evaluate performance, we collected 220 CT images in H&N cancer patients. The experiment demonstrates that our method significantly improves quantitative performance over the state-of-the-art (from 83% to 86% in absolute Dice scores). Thus, our method provides a powerful and principled means to deal with the label-scarce problem.

中文翻译：

---

放射治疗中OAR描绘的多视图自我监督分割

放射疗法已成为头颈（H＆N）癌症的常见治疗选择，并且需要对高危器官（OAR）进行描绘，以实现高保形剂量分布。手工绘制OAR既费时又不准确，因此提出了基于深度学习模型的自动绘制来准确地描述OAR。但是，最先进的性能通常需要相当大的轮廓，但是收集像素级手动轮廓是劳动密集型的，对于表示学习而言可能不是必需的。受自我监督学习的最新进展的鼓舞，本研究提出并评估了一种新颖的多视图对比表示学习，以从未标记的数据中增强模型。拟议的学习体系结构利用了CT的三种视图（冠状，矢状，和横向平面）以收集正面和负面的训练样本。具体来说，首先将3D CT投影到三个2D视图（冠状，矢状和横切面），然后卷积神经网络将3个视图作为输入，并在潜在空间中输出三个单独的表示，最后使用对比损失将同一图像的不同视图的表示（“正对”）拉近，并将不同图像的视图的表示（“负对”）推开。为了评估性能，我们在H＆N癌症患者中收集了220张CT图像。实验表明，我们的方法比最先进的方法显着提高了定量性能（绝对Dice分数从83％提高到86％）。因此，我们的方法为处理标签稀缺问题提供了强大而有原则的手段。