A Bayesian Approach to Weakly-supervised Laparoscopic Image Segmentation
This addresses the problem of reducing annotation effort for medical image segmentation, particularly in laparoscopic and cardiac contexts, with an incremental improvement through a novel Bayesian framework.
The paper tackles weakly-supervised laparoscopic image segmentation with sparse annotations by introducing a Bayesian deep learning approach, resulting in consistent outperformance over existing methods on two public datasets and competitive performance in a cardiac segmentation adaptation.
In this paper, we study weakly-supervised laparoscopic image segmentation with sparse annotations. We introduce a novel Bayesian deep learning approach designed to enhance both the accuracy and interpretability of the model's segmentation, founded upon a comprehensive Bayesian framework, ensuring a robust and theoretically validated method. Our approach diverges from conventional methods that directly train using observed images and their corresponding weak annotations. Instead, we estimate the joint distribution of both images and labels given the acquired data. This facilitates the sampling of images and their high-quality pseudo-labels, enabling the training of a generalizable segmentation model. Each component of our model is expressed through probabilistic formulations, providing a coherent and interpretable structure. This probabilistic nature benefits accurate and practical learning from sparse annotations and equips our model with the ability to quantify uncertainty. Extensive evaluations with two public laparoscopic datasets demonstrated the efficacy of our method, which consistently outperformed existing methods. Furthermore, our method was adapted for scribble-supervised cardiac multi-structure segmentation, presenting competitive performance compared to previous methods. The code is available at https://github.com/MoriLabNU/Bayesian_WSS.