Semantic Segmentation in Surgery

Being able to identify different semantic regions, such as various types of tissues, organs, and instruments, in surgical scenes is critical for the robot to understand the current progress of the surgery, assess potential risks, and plan the next steps. Surgical scenes present many challenges, including blood, smoke, motion blur, and varying lighting conditions.


Reducing Annotating Load: Active Learning with Synthetic Images in Surgical Instrument Segmentation

Medical Image Analysis 2024


HemoSet: The First Blood Segmentation Dataset for Automation of Hemostasis Management

ISMR 2024


Multi-Frame Feature Aggregation for Real-Time Instrument Segmentation in Endoscopic Video

IEEE Robotics and Automation Letters 2021


LC-GAN: Image-to-Image Translation Based on Generative Adversarial Network for Endoscopic Images

IROS 2020


Towards Better Surgical Instrument Segmentation in Endoscopic Vision: Multi-Angle Feature Aggregation and Contour Supervision

IEEE Robotics and Automation Letters 2020