Accurate segmentation of the fetal brain from Magnetic Resonance Image (MRI) is important for prenatal assessment of fetal development. Although deep learning has shown the potential to achieve this task, it requires a large fine annotated dataset that is difficult to collect. To address this issue, weakly-supervised segmentation methods with image-level labels have gained attention, which are commonly based on class activation maps from a classification network trained with image tags. However, most of these methods suffer from incomplete activation regions, due to the low-resolution localization without detailed boundary cues. To this end, we propose a novel weakly-supervised method with image-level labels based on semantic features and context information exploration. We first propose an Uncertainty-weighted Multi-resolution Class Activation Map (UM-CAM) to generate high-quality pixel-level supervision. Then, we design a Geodesic distance-based Seed Expansion (GSE) method to provide context information for rectifying the ambiguous boundaries of UM-CAM. Extensive experiments on a fetal brain dataset show that our UM-CAM can provide more accurate activation regions with fewer false positive regions than existing CAM variants, and our proposed method outperforms state-of-the-art weakly-supervised methods with image-level labels.

翻译：从磁共振图像中准确分割胎儿脑部对于产前发育评估具有重要意义。尽管深度学习展现出实现该任务的潜力，但仍需大量难以采集的精细标注数据集。为解决此问题，基于图像级标签的弱监督分割方法——通常源于通过图像标签训练的分类网络生成的类激活图——已受到广泛关注。然而，由于低分辨率定位缺乏详细边界线索，大多数此类方法存在激活区域不完整的问题。为此，我们提出一种新颖的基于图像级标签的弱监督方法，该方法基于语义特征与上下文信息探索。首先，我们提出基于不确定性加权的多分辨率类激活图（UM-CAM）以生成高质量像素级监督信号。其次，设计基于测地距离的种子扩展（GSE）方法提供上下文信息以修正UM-CAM的模糊边界。在胎儿脑部数据集上的大量实验表明，与现有CAM变体相比，我们的UM-CAM能够提供更精确的激活区域且具有更少的假阳性区域。此外，所提方法在图像级标签的弱监督方法中优于现有最先进方案。