hSDB-instrument: Instrument Localization Database for Laparoscopic and Robotic Surgeries

TL;DR

This paper introduces hSDB-instrument, a comprehensive dataset with detailed annotations for surgical instrument localization in laparoscopic and robotic surgeries, supporting improved detection, segmentation, and analysis of surgical tools.

Contribution

The paper presents a new dataset with detailed kinematic, bounding box, and segmentation annotations for surgical instruments, including synthesized data to address class imbalance, enabling advanced research in surgical tool localization.

Findings

Baseline object detection performance established using MMDetection.

Dataset includes detailed annotations for 2 surgical procedures.

Synthesized instrument data helps mitigate class imbalance.

Abstract

Automated surgical instrument localization is an important technology to understand the surgical process and in order to analyze them to provide meaningful guidance during surgery or surgical index after surgery to the surgeon. We introduce a new dataset that reflects the kinematic characteristics of surgical instruments for automated surgical instrument localization of surgical videos. The hSDB(hutom Surgery DataBase)-instrument dataset consists of instrument localization information from 24 cases of laparoscopic cholecystecomy and 24 cases of robotic gastrectomy. Localization information for all instruments is provided in the form of a bounding box for object detection. To handle class imbalance problem between instruments, synthesized instruments modeled in Unity for 3D models are included as training data. Besides, for 3D instrument data, a polygon annotation is provided to enable instance segmentation of the tool. To reflect the kinematic characteristics of all instruments, they are annotated with head and body parts for laparoscopic instruments, and with head, wrist, and body parts for robotic instruments separately. Annotation data of assistive tools (specimen bag, needle, etc.) that are frequently used for surgery are also included. Moreover, we provide statistical information on the hSDB-instrument dataset and the baseline localization performances of the object detection networks trained by the MMDetection library and resulting analyses.