De Backer, Pieter ORCID: 0000-0002-9375-2353, Eckhoff, Jennifer A., Simoens, Jente, Mueller, Dolores T., Allaeys, Charlotte, Creemers, Heleen, Hallemeesch, Amelie, Mestdagh, Kenzo, Van Praet, Charles, Debbaut, Charlotte ORCID: 0000-0003-1962-238X, Decaestecker, Karel, Bruns, Christiane J., Meireles, Ozanan, Mottrie, Alexandre and Fuchs, Hans F. (2022). Multicentric exploration of tool annotation in robotic surgery: lessons learned when starting a surgical artificial intelligence project. Surg. Endosc., 36 (11). S. 8533 - 8549. NEW YORK: SPRINGER. ISSN 1432-2218

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Abstract

Background Artificial intelligence (AI) holds tremendous potential to reduce surgical risks and improve surgical assessment. Machine learning, a subfield of AI, can be used to analyze surgical video and imaging data. Manual annotations provide veracity about the desired target features. Yet, methodological annotation explorations are limited to date. Here, we provide an exploratory analysis of the requirements and methods of instrument annotation in a multi-institutional team from two specialized AI centers and compile our lessons learned. Methods We developed a bottom-up approach for team annotation of robotic instruments in robot-assisted partial nephrectomy (RAPN), which was subsequently validated in robot-assisted minimally invasive esophagectomy (RAMIE). Furthermore, instrument annotation methods were evaluated for their use in Machine Learning algorithms. Overall, we evaluated the efficiency and transferability of the proposed team approach and quantified performance metrics (e.g., time per frame required for each annotation modality) between RAPN and RAMIE. Results We found a 0.05 Hz image sampling frequency to be adequate for instrument annotation. The bottom-up approach in annotation training and management resulted in accurate annotations and demonstrated efficiency in annotating large datasets. The proposed annotation methodology was transferrable between both RAPN and RAMIE. The average annotation time for RAPN pixel annotation ranged from 4.49 to 12.6 min per image; for vector annotation, we denote 2.92 min per image. Similar annotation times were found for RAMIE. Lastly, we elaborate on common pitfalls encountered throughout the annotation process. Conclusions We propose a successful bottom-up approach for annotator team composition, applicable to any surgical annotation project. Our results set the foundation to start AI projects for instrument detection, segmentation, and pose estimation. Due to the immense annotation burden resulting from spatial instrumental annotation, further analysis into sampling frequency and annotation detail needs to be conducted.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
De Backer, PieterUNSPECIFIEDorcid.org/0000-0002-9375-2353UNSPECIFIED
Eckhoff, Jennifer A.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Simoens, JenteUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Mueller, Dolores T.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Allaeys, CharlotteUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Creemers, HeleenUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Hallemeesch, AmelieUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Mestdagh, KenzoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Van Praet, CharlesUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Debbaut, CharlotteUNSPECIFIEDorcid.org/0000-0003-1962-238XUNSPECIFIED
Decaestecker, KarelUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Bruns, Christiane J.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Meireles, OzananUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Mottrie, AlexandreUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Fuchs, Hans F.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-683516
DOI: 10.1007/s00464-022-09487-1
Journal or Publication Title: Surg. Endosc.
Volume: 36
Number: 11
Page Range: S. 8533 - 8549
Date: 2022
Publisher: SPRINGER
Place of Publication: NEW YORK
ISSN: 1432-2218
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
SurgeryMultiple languages
URI: http://kups.ub.uni-koeln.de/id/eprint/68351

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