A Bayesian Approach to Weakly-supervised Laparoscopic Image Segmentation

TL;DR

This paper introduces a Bayesian deep learning framework for weakly-supervised laparoscopic image segmentation, improving accuracy, interpretability, and uncertainty quantification by modeling joint distributions and sampling pseudo-labels.

Contribution

It presents a novel Bayesian approach that estimates joint distributions of images and labels, enabling effective training with sparse annotations and extending to scribble-supervised cardiac segmentation.

Findings

Outperformed existing methods on two laparoscopic datasets.

Achieved competitive results in scribble-supervised cardiac segmentation.

Provided a probabilistic, interpretable model with uncertainty quantification.

Abstract

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.