Data Augmentation for Surgical Scene Segmentation with Anatomy-Aware Diffusion Models

TL;DR

This paper presents a diffusion model-based multi-stage framework for generating annotated multi-class surgical datasets, significantly improving segmentation performance by enhancing anatomy awareness and structural consistency.

Contribution

It introduces a novel diffusion model approach for creating anatomically accurate, multi-class surgical datasets with annotations, reducing reliance on extensive manual labeling.

Findings

Achieved 15% improvement in segmentation scores with synthetic data.

Generated high-quality, anatomically consistent multi-class surgical images.

Enhanced anatomy awareness in segmentation models through synthetic data augmentation.

Abstract

In computer-assisted surgery, automatically recognizing anatomical organs is crucial for understanding the surgical scene and providing intraoperative assistance. While machine learning models can identify such structures, their deployment is hindered by the need for labeled, diverse surgical datasets with anatomical annotations. Labeling multiple classes (i.e., organs) in a surgical scene is time-intensive, requiring medical experts. Although synthetically generated images can enhance segmentation performance, maintaining both organ structure and texture during generation is challenging. We introduce a multi-stage approach using diffusion models to generate multi-class surgical datasets with annotations. Our framework improves anatomy awareness by training organ specific models with an inpainting objective guided by binary segmentation masks. The organs are generated with an inference pipeline using pre-trained ControlNet to maintain the organ structure. The synthetic multi-class datasets are constructed through an image composition step, ensuring structural and textural consistency. This versatile approach allows the generation of multi-class datasets from real binary datasets and simulated surgical masks. We thoroughly evaluate the generated datasets on image quality and downstream segmentation, achieving a $15\%$ improvement in segmentation scores when combined with real images. The code is available at https://gitlab.com/nct_tso_public/muli-class-image-synthesis