Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Tracking

TL;DR

Semantic-SuPer is a comprehensive framework that combines geometric and semantic information from endoscopic videos to improve 3D perception, reconstruction, and tracking in robotic surgery, enhancing surgical navigation capabilities.

Contribution

It introduces a novel integration of semantic segmentation with geometric perception for endoscopic scene understanding, advancing beyond existing geometric-only methods.

Findings

Outperforms baseline and state-of-the-art methods on challenging endoscopic data.

Effectively handles deforming tissue during 3D reconstruction and tracking.

Demonstrates robustness and accuracy in surgical scene perception.

Abstract

Accurate and robust tracking and reconstruction of the surgical scene is a critical enabling technology toward autonomous robotic surgery. Existing algorithms for 3D perception in surgery mainly rely on geometric information, while we propose to also leverage semantic information inferred from the endoscopic video using image segmentation algorithms. In this paper, we present a novel, comprehensive surgical perception framework, Semantic-SuPer, that integrates geometric and semantic information to facilitate data association, 3D reconstruction, and tracking of endoscopic scenes, benefiting downstream tasks like surgical navigation. The proposed framework is demonstrated on challenging endoscopic data with deforming tissue, showing its advantages over our baseline and several other state-of the-art approaches. Our code and dataset are available at https://github.com/ucsdarclab/Python-SuPer.