Weakly Supervised Segmentation Improves the Estimate of the Choroid Plexus Volume: Application to Multiple Sclerosis

The Choroid Plexus (ChP) is a brain vascular tissue responsible for regulatory processes. Modifications in ChP Volume (ChPV) have been associated with neurodegenerative disorders, making ChPV a potential biomarker for monitoring disease progression and severity. The current gold-standard technique to quantify ChPV is manual segmentation on T1-weighted Magnetic Resonance Images. However, this method is time-consuming and prone to variability between different operators. Recently, deep learning methods have emerged as state-of-the-art systems for Magnetic Resonance image segmentation. In this study, we demonstrate that deep learning models can be effectively trained using weakly labeled data, specifically bounding box annotations that lack precise contour information. To explore this concept, we trained a series of models using various strategies that leverage bounding box annotations for segmentation tasks. A model trained on a dataset with manual segmentation masks achieved a lower performance compared with the same model trained with a weakly supervised strategy based on bounding box images. While acknowledging that the increase may not be substantial, it is essential to take into account our limited number of images. This limitation becomes intriguing when considering that compiling extensive datasets is considerably easier using rough ROIs rather than finely segmented ones.