Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in the screening and prognosis assessment of high-risk breast cancer. The segmentation of cancerous regions is essential useful for the subsequent analysis of breast MRI. To alleviate the annotation effort to train the segmentation networks, we propose a weakly-supervised strategy using extreme points as annotations for breast cancer segmentation. Without using any bells and whistles, our strategy focuses on fully exploiting the learning capability of the routine training procedure, i.e., the train - fine-tune - retrain process. The network first utilizes the pseudo-masks generated using the extreme points to train itself, by minimizing a contrastive loss, which encourages the network to learn more representative features for cancerous voxels. Then the trained network fine-tunes itself by using a similarity-aware propagation learning (SimPLe) strategy, which leverages feature similarity between unlabeled and positive voxels to propagate labels. Finally the network retrains itself by employing the pseudo-masks generated using previous fine-tuned network. The proposed method is evaluated on our collected DCE-MRI dataset containing 206 patients with biopsy-proven breast cancers. Experimental results demonstrate our method effectively fine-tunes the network by using the SimPLe strategy, and achieves a mean Dice value of 81%.

翻译：摘要：乳腺动态对比增强磁共振成像（DCE-MRI）在高风险乳腺癌的筛查和预后评估中具有重要作用。癌变区域的分割对于后续乳腺MRI分析至关重要。为了减轻训练分割网络时的标注负担，我们提出了一种利用极值点作为标注的弱监督乳腺癌分割策略。该方法无需任何复杂技巧，专注于充分发挥常规训练流程（即训练-微调-再训练过程）的学习能力。网络首先利用极值点生成的伪掩膜，通过最小化对比损失进行自训练，该损失函数促使网络学习更具代表性的癌变体素特征；随后，预训练网络通过相似性感知传播学习（SimPLe）策略进行微调，该策略利用未标记体素与阳性体素之间的特征相似性来传播标签；最后，网络利用前一步微调所得伪掩膜进行再训练。我们在包含206例经活检证实乳腺癌患者的DCE-MRI数据集上对所提方法进行了评估。实验结果表明，该方法通过SimPLe策略有效实现了网络微调，平均Dice值达到81%。