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:在 3D 医学图像分割 3D 中, Convolutional NealNetwork (CNNs) 成功在 3D 医学图像分割中的成功依赖于大量全加注的 3D 数量的培训。 在本文中,我们建议对一个仅包含 3D 医学图像中七个点的分解目标进行注释,并设计一个两阶段监督薄弱的学习框架 PA-Seg。 在第一阶段,我们使用大地距离转换来扩大种子点以提供更多的监督信号。为了在培训中进一步处理未加注的图像区域,我们建议了两种背景规范化战略,即多视图随机场(MCF)损失和差异最小化(VMM) 损失。 在本文中,我们首先鼓励具有类似特性的像素分解目标(像素), 并设计一个两个阶段的强度差异化框架(PA-S-S-S-S-S-S-S-B) 。 我们使用模型在第一阶段中获得的预测作为假标签的状态。 为了克服贴近标签中的噪音,我们首先引入了一种自我和交叉监测(S-C-Dervial-S-C-C) 模拟的模型的模型, 和实验中, 将每个的模型的模型的模型的模型的模型和实验的模型的模型的模型的模型的模型和实验性分析,通过每个模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型和模拟的模型的模型的模型的模拟的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的