  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.

Computer Vision and Pattern Recognition