Computer-vision based analysis of the neurosurgical scene – A systematic review
Introduction: With increasing use of robotic surgical adjuncts, artificial intelligence and augmented reality in neurosurgery, the automated analysis of digital images and videos acquired over various procedures becomes a subject of increased interest. While several computer vision (CV) methods have...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Brain and Spine |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772529423009943 |
| _version_ | 1797398891301175296 |
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| author | Félix Buyck Jef Vandemeulebroucke Jakub Ceranka Frederick Van Gestel Jan Frederick Cornelius Johnny Duerinck Michaël Bruneau |
| author_facet | Félix Buyck Jef Vandemeulebroucke Jakub Ceranka Frederick Van Gestel Jan Frederick Cornelius Johnny Duerinck Michaël Bruneau |
| author_sort | Félix Buyck |
| collection | DOAJ |
| description | Introduction: With increasing use of robotic surgical adjuncts, artificial intelligence and augmented reality in neurosurgery, the automated analysis of digital images and videos acquired over various procedures becomes a subject of increased interest. While several computer vision (CV) methods have been developed and implemented for analyzing surgical scenes, few studies have been dedicated to neurosurgery. Research question: In this work, we present a systematic literature review focusing on CV methodologies specifically applied to the analysis of neurosurgical procedures based on intra-operative images and videos. Additionally, we provide recommendations for the future developments of CV models in neurosurgery. Material and methods: We conducted a systematic literature search in multiple databases until January 17, 2023, including Web of Science, PubMed, IEEE Xplore, Embase, and SpringerLink. Results: We identified 17 studies employing CV algorithms on neurosurgical videos/images. The most common applications of CV were tool and neuroanatomical structure detection or characterization, and to a lesser extent, surgical workflow analysis. Convolutional neural networks (CNN) were the most frequently utilized architecture for CV models (65%), demonstrating superior performances in tool detection and segmentation. In particular, mask recurrent-CNN manifested most robust performance outcomes across different modalities. Discussion and conclusion: Our systematic review demonstrates that CV models have been reported that can effectively detect and differentiate tools, surgical phases, neuroanatomical structures, as well as critical events in complex neurosurgical scenes with accuracies above 95%. Automated tool recognition contributes to objective characterization and assessment of surgical performance, with potential applications in neurosurgical training and intra-operative safety management. |
| first_indexed | 2024-03-09T01:32:05Z |
| format | Article |
| id | doaj.art-38035d8f8a5044bab3e5e62b94d63d9d |
| institution | Directory Open Access Journal |
| issn | 2772-5294 |
| language | English |
| last_indexed | 2024-03-09T01:32:05Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Brain and Spine |
| spelling | doaj.art-38035d8f8a5044bab3e5e62b94d63d9d2023-12-09T06:09:18ZengElsevierBrain and Spine2772-52942023-01-013102706Computer-vision based analysis of the neurosurgical scene – A systematic reviewFélix Buyck0Jef Vandemeulebroucke1Jakub Ceranka2Frederick Van Gestel3Jan Frederick Cornelius4Johnny Duerinck5Michaël Bruneau6Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium; Vrije Universiteit Brussel (VUB), Research group Center For Neurosciences (C4N-NEUR), 1090, Brussels, Belgium; Corresponding author. Department of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), Laerbeeklaan 101, 1090, Brussels, Belgium.Vrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), 1050, Brussels, Belgium; Department of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium; imec, 3001, Leuven, BelgiumVrije Universiteit Brussel (VUB), Department of Electronics and Informatics (ETRO), 1050, Brussels, Belgium; imec, 3001, Leuven, BelgiumDepartment of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium; Vrije Universiteit Brussel (VUB), Research group Center For Neurosciences (C4N-NEUR), 1090, Brussels, BelgiumDepartment of Neurosurgery, Medical Faculty, Heinrich-Heine-University, 40225, Düsseldorf, GermanyDepartment of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium; Vrije Universiteit Brussel (VUB), Research group Center For Neurosciences (C4N-NEUR), 1090, Brussels, BelgiumDepartment of Neurosurgery, Universitair Ziekenhuis Brussel (UZ Brussel), 1090, Brussels, Belgium; Vrije Universiteit Brussel (VUB), Research group Center For Neurosciences (C4N-NEUR), 1090, Brussels, BelgiumIntroduction: With increasing use of robotic surgical adjuncts, artificial intelligence and augmented reality in neurosurgery, the automated analysis of digital images and videos acquired over various procedures becomes a subject of increased interest. While several computer vision (CV) methods have been developed and implemented for analyzing surgical scenes, few studies have been dedicated to neurosurgery. Research question: In this work, we present a systematic literature review focusing on CV methodologies specifically applied to the analysis of neurosurgical procedures based on intra-operative images and videos. Additionally, we provide recommendations for the future developments of CV models in neurosurgery. Material and methods: We conducted a systematic literature search in multiple databases until January 17, 2023, including Web of Science, PubMed, IEEE Xplore, Embase, and SpringerLink. Results: We identified 17 studies employing CV algorithms on neurosurgical videos/images. The most common applications of CV were tool and neuroanatomical structure detection or characterization, and to a lesser extent, surgical workflow analysis. Convolutional neural networks (CNN) were the most frequently utilized architecture for CV models (65%), demonstrating superior performances in tool detection and segmentation. In particular, mask recurrent-CNN manifested most robust performance outcomes across different modalities. Discussion and conclusion: Our systematic review demonstrates that CV models have been reported that can effectively detect and differentiate tools, surgical phases, neuroanatomical structures, as well as critical events in complex neurosurgical scenes with accuracies above 95%. Automated tool recognition contributes to objective characterization and assessment of surgical performance, with potential applications in neurosurgical training and intra-operative safety management.http://www.sciencedirect.com/science/article/pii/S2772529423009943computer visionSurgical videosAutomated detectionSurgical instrumentsSurgical phase recognitionNeuroanatomy |
| spellingShingle | Félix Buyck Jef Vandemeulebroucke Jakub Ceranka Frederick Van Gestel Jan Frederick Cornelius Johnny Duerinck Michaël Bruneau Computer-vision based analysis of the neurosurgical scene – A systematic review Brain and Spine computer vision Surgical videos Automated detection Surgical instruments Surgical phase recognition Neuroanatomy |
| title | Computer-vision based analysis of the neurosurgical scene – A systematic review |
| title_full | Computer-vision based analysis of the neurosurgical scene – A systematic review |
| title_fullStr | Computer-vision based analysis of the neurosurgical scene – A systematic review |
| title_full_unstemmed | Computer-vision based analysis of the neurosurgical scene – A systematic review |
| title_short | Computer-vision based analysis of the neurosurgical scene – A systematic review |
| title_sort | computer vision based analysis of the neurosurgical scene a systematic review |
| topic | computer vision Surgical videos Automated detection Surgical instruments Surgical phase recognition Neuroanatomy |
| url | http://www.sciencedirect.com/science/article/pii/S2772529423009943 |
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