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Article

Combined Mass Spectrometry and Histopathology Imaging for Perioperative Tissue Assessment in Cancer Surgery

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School of Computing, Queen’s University, Kingston, ON K7L 3N6, Canada
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Department of Surgery, Queen’s University, Kingston, ON K7L 3N6, Canada
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Department of Pathology and Molecular Medicine, Queen’s University, Kingston, ON K7L 3N6, Canada
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Author to whom correspondence should be addressed.
Academic Editors: Terry Peters and Elvis C. S. Chen
J. Imaging 2021, 7(10), 203; https://doi.org/10.3390/jimaging7100203
Received: 9 July 2021 / Revised: 28 September 2021 / Accepted: 30 September 2021 / Published: 4 October 2021
Mass spectrometry is an effective imaging tool for evaluating biological tissue to detect cancer. With the assistance of deep learning, this technology can be used as a perioperative tissue assessment tool that will facilitate informed surgical decisions. To achieve such a system requires the development of a database of mass spectrometry signals and their corresponding pathology labels. Assigning correct labels, in turn, necessitates precise spatial registration of histopathology and mass spectrometry data. This is a challenging task due to the domain differences and noisy nature of images. In this study, we create a registration framework for mass spectrometry and pathology images as a contribution to the development of perioperative tissue assessment. In doing so, we explore two opportunities in deep learning for medical image registration, namely, unsupervised, multi-modal deformable image registration and evaluation of the registration. We test this system on prostate needle biopsy cores that were imaged with desorption electrospray ionization mass spectrometry (DESI) and show that we can successfully register DESI and histology images to achieve accurate alignment and, consequently, labelling for future training. This automation is expected to improve the efficiency and development of a deep learning architecture that will benefit the use of mass spectrometry imaging for cancer diagnosis. View Full-Text
Keywords: perioperative imaging; DESI; deep learning; deformable image registration; prostate cancer; diagnosis perioperative imaging; DESI; deep learning; deformable image registration; prostate cancer; diagnosis
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MDPI and ACS Style

Connolly, L.; Jamzad, A.; Kaufmann, M.; Farquharson, C.E.; Ren, K.; Rudan, J.F.; Fichtinger, G.; Mousavi, P. Combined Mass Spectrometry and Histopathology Imaging for Perioperative Tissue Assessment in Cancer Surgery. J. Imaging 2021, 7, 203. https://doi.org/10.3390/jimaging7100203

AMA Style

Connolly L, Jamzad A, Kaufmann M, Farquharson CE, Ren K, Rudan JF, Fichtinger G, Mousavi P. Combined Mass Spectrometry and Histopathology Imaging for Perioperative Tissue Assessment in Cancer Surgery. Journal of Imaging. 2021; 7(10):203. https://doi.org/10.3390/jimaging7100203

Chicago/Turabian Style

Connolly, Laura, Amoon Jamzad, Martin Kaufmann, Catriona E. Farquharson, Kevin Ren, John F. Rudan, Gabor Fichtinger, and Parvin Mousavi. 2021. "Combined Mass Spectrometry and Histopathology Imaging for Perioperative Tissue Assessment in Cancer Surgery" Journal of Imaging 7, no. 10: 203. https://doi.org/10.3390/jimaging7100203

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