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Michael Ebner, Nabavi E, Shapey J, Xie Y, Liebmann F, Spirig JM, Hoch A, Farshad M, Saeed SR, Bradford R, Yardley I, Ourselin S, Edwards AD, Führnstahl P, Vercauteren T. Intraoperative hyperspectral label-free imaging: from system design to first-in-patient translation. JOURNAL OF PHYSICS D: APPLIED PHYSICS 2021; 54:294003. [PMID: 34024940 PMCID: PMC8132621 DOI: 10.1088/1361-6463/abfbf6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 03/30/2021] [Accepted: 04/27/2021] [Indexed: 10/05/2023]
Abstract
Despite advances in intraoperative surgical imaging, reliable discrimination of critical tissue during surgery remains challenging. As a result, decisions with potentially life-changing consequences for patients are still based on the surgeon's subjective visual assessment. Hyperspectral imaging (HSI) provides a promising solution for objective intraoperative tissue characterisation, with the advantages of being non-contact, non-ionising and non-invasive. However, while its potential to aid surgical decision-making has been investigated for a range of applications, to date no real-time intraoperative HSI (iHSI) system has been presented that follows critical design considerations to ensure a satisfactory integration into the surgical workflow. By establishing functional and technical requirements of an intraoperative system for surgery, we present an iHSI system design that allows for real-time wide-field HSI and responsive surgical guidance in a highly constrained operating theatre. Two systems exploiting state-of-the-art industrial HSI cameras, respectively using linescan and snapshot imaging technology, were designed and investigated by performing assessments against established design criteria and ex vivo tissue experiments. Finally, we report the use of our real-time iHSI system in a clinical feasibility case study as part of a spinal fusion surgery. Our results demonstrate seamless integration into existing surgical workflows.
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Affiliation(s)
- Michael Ebner
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Eli Nabavi
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Jonathan Shapey
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, UCL, London, United Kingdom
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
| | - Yijing Xie
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Florentin Liebmann
- Research in Orthopedic Computer Science (ROCS), Balgrist University Hospital, University of Zurich, Balgrist CAMPUS, Zurich, Switzerland
- Laboratory for Orthopaedic Biomechanics, ETH Zurich, Zurich, Switzerland
| | - José Miguel Spirig
- Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Armando Hoch
- Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Mazda Farshad
- Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Shakeel R Saeed
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
- The Ear Institute, UCL, London, United Kingdom
- The Royal National Throat, Nose and Ear Hospital, London, United Kingdom
| | - Robert Bradford
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
| | - Iain Yardley
- Department of Paediatric Surgery, Evelina London Children’s Hospital, London, United Kingdom
| | - Sébastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - A David Edwards
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
- Department of Paediatric Surgery, Evelina London Children’s Hospital, London, United Kingdom
| | - Philipp Führnstahl
- Research in Orthopedic Computer Science (ROCS), Balgrist University Hospital, University of Zurich, Balgrist CAMPUS, Zurich, Switzerland
| | - Tom Vercauteren
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
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Wiesenfarth M, Reinke A, Landman BA, Eisenmann M, Saiz LA, Cardoso MJ, Maier-Hein L, Kopp-Schneider A. Methods and open-source toolkit for analyzing and visualizing challenge results. Sci Rep 2021; 11:2369. [PMID: 33504883 PMCID: PMC7841186 DOI: 10.1038/s41598-021-82017-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 01/11/2021] [Indexed: 01/12/2023] Open
Abstract
Grand challenges have become the de facto standard for benchmarking image analysis algorithms. While the number of these international competitions is steadily increasing, surprisingly little effort has been invested in ensuring high quality design, execution and reporting for these international competitions. Specifically, results analysis and visualization in the event of uncertainties have been given almost no attention in the literature. Given these shortcomings, the contribution of this paper is two-fold: (1) we present a set of methods to comprehensively analyze and visualize the results of single-task and multi-task challenges and apply them to a number of simulated and real-life challenges to demonstrate their specific strengths and weaknesses; (2) we release the open-source framework challengeR as part of this work to enable fast and wide adoption of the methodology proposed in this paper. Our approach offers an intuitive way to gain important insights into the relative and absolute performance of algorithms, which cannot be revealed by commonly applied visualization techniques. This is demonstrated by the experiments performed in the specific context of biomedical image analysis challenges. Our framework could thus become an important tool for analyzing and visualizing challenge results in the field of biomedical image analysis and beyond.
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Affiliation(s)
- Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, Heidelberg, 69120, Germany.
| | - Annika Reinke
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| | - Bennett A Landman
- Electrical Engineering, Vanderbilt University, Nashville, TN, 37235-1679, USA
| | - Matthias Eisenmann
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| | - Laura Aguilera Saiz
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, WC2R 2LS, UK
| | - Lena Maier-Hein
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, 69120, Heidelberg, Germany.
| | - Annette Kopp-Schneider
- Division of Biostatistics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, Heidelberg, 69120, Germany
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