The success of Convolutional Neural Networks (CNNs) in 3D medical image segmentation relies on massive fully annotated 3D volumes for training that are time-consuming and labor-intensive to acquire. In this paper, we propose to annotate a segmentation target with only seven points in 3D medical images, and design a two-stage weakly supervised learning framework PA-Seg. In the first stage, we employ geodesic distance transform to expand the seed points to provide more supervision signal. To further deal with unannotated image regions during training, we propose two contextual regularization strategies, i.e., multi-view Conditional Random Field (mCRF) loss and Variance Minimization (VM) loss, where the first one encourages pixels with similar features to have consistent labels, and the second one minimizes the intensity variance for the segmented foreground and background, respectively. In the second stage, we use predictions obtained by the model pre-trained in the first stage as pseudo labels. To overcome noises in the pseudo labels, we introduce a Self and Cross Monitoring (SCM) strategy, which combines self-training with Cross Knowledge Distillation (CKD) between a primary model and an auxiliary model that learn from soft labels generated by each other. Experiments on public datasets for Vestibular Schwannoma (VS) segmentation and Brain Tumor Segmentation (BraTS) demonstrated that our model trained in the first stage outperformed existing state-of-the-art weakly supervised approaches by a large margin, and after using SCM for additional training, the model's performance was close to its fully supervised counterpart on the BraTS dataset.

翻译：卷积神经网络（CNN）在三维医学图像分割任务中的成功依赖于大量全标注的3D体数据，但这类数据的获取耗时耗力。本文提出仅需在3D医学图像中对分割目标标注7个点，并设计了一个两阶段弱监督学习框架PA-Seg。第一阶段，采用测地距离变换扩展种子点以提供更多监督信号。针对训练中未标注的图像区域，提出两种上下文正则化策略：多视角条件随机场（mCRF）损失和方差最小化（VM）损失——前者促使特征相似的像素具有一致标签，后者分别最小化分割前景与背景的强度方差。第二阶段，使用第一阶段预训练模型的预测结果作为伪标签。为克服伪标签中的噪声，引入自监控与交叉监控（SCM）策略，该策略结合自训练与主模型、辅助模型之间的交叉知识蒸馏（CKD），两模型从彼此生成的软标签中学习。在用于前庭神经鞘瘤（VS）分割和脑肿瘤分割（BraTS）的公共数据集上的实验表明，第一阶段训练的模型在性能上大幅超越现有最先进的弱监督方法；而在使用SCM进行额外训练后，该模型在BraTS数据集上的表现已接近全监督模型。