用贝叶斯方法从稀疏标注中学习更准确的腹腔镜图像分割。
A Bayesian Approach to Weakly-supervised Laparoscopic Image Segmentation
- 构建概率框架，联合建模图像与标签分布，生成高质量伪标签。
- 在两个公开数据集上优于现有方法，提升分割精度与泛化能力。
- 可解释性强，适合医疗图像分析领域研究人员参考。
本文研究稀疏标注下的弱监督腹腔镜图像分割问题。提出一种新颖的贝叶斯深度学习方法，基于完整的贝叶斯框架，提升模型分割的准确性与可解释性，具备理论保障。不同于直接使用观测图像和弱标注进行训练的传统方法，本方法通过估计图像与标签的联合分布，实现高质伪标签采样，从而训练出具有强泛化能力的分割模型。模型各组件均以概率形式表达，结构统一且可解释。该特性使模型能有效利用稀疏标注，并量化预测不确定性。在两个公开腹腔镜数据集上的大量实验表明，本方法持续优于现有技术。此外，方法还成功拓展至涂鸦监督的心脏多结构分割任务，性能与先前方法相当。代码已开源：https://github.com/MoriLabNU/Bayesian_WSS。
原文摘要 · Abstract (English)
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.
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