SSIS-Seg: Simulation-Supervised Image Synthesis for Surgical Instrument Segmentation.

Area of Science:

• Medical Imaging
• Robotics
• Computer Vision

Background:

• Deep learning enhances surgical instrument segmentation for computer-assisted interventions and robotic automation.
• Current segmentation models require substantial labeled data, limiting their widespread application.

Purpose of the Study:

• To develop a novel method for generating synthetic surgical images to train segmentation models.
• To reduce the dependency on large, manually annotated datasets in surgical image analysis.

Main Methods:

• Fusing robotic instrument simulation with domain adaptation techniques to create artificial surgical images.
• Integrating attention modules and a novel cost function into image generation pipelines.
• Utilizing simulation frames for model supervision during training.

Main Results:

• The proposed method successfully generates synthetic images capable of training effective surgical instrument segmentation models.
• Extensive evaluations demonstrate competitive segmentation performance compared to state-of-the-art methods.
• A new dataset from real surgeries is released for research purposes.

Conclusions:

• Synthetic surgical images generated through this novel approach can significantly improve the training of segmentation models.
• This method offers a viable solution to the data scarcity problem in surgical robotics and computer-assisted interventions.