# Weakly Supervised Segmentation by A Deep Geodesic Prior

## TL;DR

This paper introduces a weakly supervised image segmentation method utilizing a deep geodesic prior, improving accuracy especially with noisy labels by incorporating shape information into the loss function.

## Contribution

The study proposes a novel segmentation approach that integrates a deep geodesic prior from an auto-encoder into the training process, enhancing robustness to weak and noisy labels.

## Key findings

- Boosted segmentation performance by 4.4-6.3% in dice score.
- Improved robustness to high-level noise in labels.
- Effective in segmenting cardiac substructures with noisy annotations.

## Abstract

The performance of the state-of-the-art image segmentation methods heavily relies on the high-quality annotations, which are not easily affordable, particularly for medical data. To alleviate this limitation, in this study, we propose a weakly supervised image segmentation method based on a deep geodesic prior. We hypothesize that integration of this prior information can reduce the adverse effects of weak labels in segmentation accuracy. Our proposed algorithm is based on a prior information, extracted from an auto-encoder, trained to map objects geodesic maps to their corresponding binary maps. The obtained information is then used as an extra term in the loss function of the segmentor. In order to show efficacy of the proposed strategy, we have experimented segmentation of cardiac substructures with clean and two levels of noisy labels (L1, L2). Our experiments showed that the proposed algorithm boosted the performance of baseline deep learning-based segmentation for both clean and noisy labels by 4.4%, 4.6%(L1), and 6.3%(L2) in dice score, respectively. We also showed that the proposed method was more robust in the presence of high-level noise due to the existence of shape priors.