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

• Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 pp. 393 - 402
• Main Authors: Yoon, Jihun, Lee, Jiwon, Heo, Sunghwan, Yu, Hayeong, Lim, Jayeon, Song, Chi Hyun, Hong, SeulGi, Hong, Seungbum, Park, Bokyung, Park, SungHyun, Hyung, Woo Jin, Choi, Min-Kook
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Class imbalance; Domain randomization; Object detection; Surgical instrument localization
• ISBN: 9783030872014; 3030872017
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-87202-1_38

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 (The dataset, additional dataset statistics and several trained models are publicly available at https://hsdb-instrument.github.io/).