Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
International Journal of Computer Assisted Radiology and Surgery

10-04-2024 | Basalioma | Original Article

ImSpect: Image-driven self-supervised learning for surgical margin evaluation with mass spectrometry

Authors: Laura Connolly, Fahimeh Fooladgar, Amoon Jamzad, Martin Kaufmann, Ayesha Syeda, Kevin Ren, Purang Abolmaesumi, John F. Rudan, Doug McKay, Gabor Fichtinger, Parvin Mousavi

Published in: International Journal of Computer Assisted Radiology and Surgery

Login to get access

Abstract

Purpose

Real-time assessment of surgical margins is critical for favorable outcomes in cancer patients. The iKnife is a mass spectrometry device that has demonstrated potential for margin detection in cancer surgery. Previous studies have shown that using deep learning on iKnife data can facilitate real-time tissue characterization. However, none of the existing literature on the iKnife facilitate the use of publicly available, state-of-the-art pretrained networks or datasets that have been used in computer vision and other domains.

Methods

In a new framework we call ImSpect, we convert 1D iKnife data, captured during basal cell carcinoma (BCC) surgery, into 2D images in order to capitalize on state-of-the-art image classification networks. We also use self-supervision to leverage large amounts of unlabeled, intraoperative data to accommodate the data requirements of these networks.

Results

Through extensive ablation studies, we show that we can surpass previous benchmarks of margin evaluation in BCC surgery using iKnife data, achieving an area under the receiver operating characteristic curve (AUC) of 81%. We also depict the attention maps of the developed DL models to evaluate the biological relevance of the embedding space

Conclusions

We propose a new method for characterizing tissue at the surgical margins, using mass spectrometry data from cancer surgery.
Literature
1.
Adib E, Fernandez A, Afghah F, Prevost JJ (2023) Synthetic ecg signal generation using probabilistic diffusion models. arXiv preprint arXiv:​2303.​02475
2.
Ahmad Z, Tabassum A, Guan L, Khan NM (2021) Ecg heartbeat classification using multimodal fusion. IEEE Access 9:100615–100626CrossRef
3.
Alzubaidi L, Fadhel MA, Al-Shamma O, Zhang J, Santamaría J, Duan YR, Oleiwi S (2020) Towards a better understanding of transfer learning for medical imaging: a case study. Appl Sci 10(13):4523CrossRef
4.
Ayyachamy S, Alex V, Khened M, Krishnamurthi G (2019) Medical image retrieval using resnet-18. In: Medical imaging 2019: imaging informatics for healthcare, research, and applications, pp 233–241
5.
Bai W, Chen C, Tarroni G, Duan J, Guitton F, Petersen SE, Guo Y, Matthews PM, Rueckert D (2019) Self-supervised learning for cardiac mr image segmentation by anatomical position prediction. In: International Conf. on Medical Image Computing and Computer-Assisted Intervention, pp 541–549
6.
Balog J, Sasi-Szabó L, Kinross J, Lewis MR, Muirhead LJ, Veselkov K, Mirnezami R, Dezső B, Damjanovich L, Darzi A, Nicholson TZ, Jeremy K (2013) Intraoperative tissue identification using rapid evaporative ionization mass spectrometry. Sci Transl Med 5(194):194ra93CrossRefPubMed
7.
Chen L, Bentley P, Mori K, Misawa K, Fujiwara M, Rueckert D (2019) Self-Supervised learning for medical image analysis using image context restoration. Med Image Anal 58:101539CrossRefPubMedPubMedCentral
8.
Fooladgar F, Jamzad A, Connolly L, Santilli A, Kaufmann M, Ren K, Abolmaesumi P, Rudan JF, McKay D, Fichtinger G, Mousavi P (2022) Uncertainty estimation for margin detection in cancer surgery using mass spectrometry. IJCARS 17(12):2305–2313
9.
Hatami N, Gavet Y, Debayle J (2018) Classification of time-series images using deep convolutional neural networks. In: Tenth international conference on machine vision (ICMV 2017), pp 242–249
10.
Jamzad A, Sedghi A, Santilli AM, Janssen NN, Kaufmann M, Ren KY, Vanderbeck K, Wang A, McKay D, Rudan JF, Fichtinger G, Mousavi P (2020) Improved resection margins in surgical oncology using intraoperative mass spectrometry. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp 44–53
11.
Jamzad A, Sedghi A, Santilli AM, Janssen NN, Kaufmann M, Ren KY, Vanderbeck K, Wang A, Mckay D, Rudan JF, Fichtinger G, Mousavi P (2020) Improved resection margins in surgical oncology using intraoperative mass spectrometry. Medical Image Computing and Computer Assisted Intervention, MICCAI Lecture Notes in Computer Science vol 12263 Springer, Cham
12.
Janssen NN, Kaufmann M, Santilli A, Jamzad A, Vanderbeck K, Ren KYM, Ungi T, Mousavi P, Rudan JF, McKay D, Wang A, Fichtinger G (2020) Navigated tissue characterization during skin cancer surgery. Int J Comput Assist Radiol Surg 15(10):1665–1672CrossRefPubMed
13.
Jiang W, Zhang D, Ling L, Lin R (2022) Time series classification based on image transformation using feature fusion strategy. Neural Process Lett 54(5):3727–3748CrossRef
14.
Khosla P, Teterwak P, Wang C, Sarna A, Tian Y, Isola P, Maschinot A, Liu C, Krishnan D (2020) Supervised contrastive learning. Adv Neural Inf Process Syst 33:18661–18673
15.
Koundouros N, Poulogiannis G (2020) Reprogramming of fatty acid metabolism in cancer. Br J Cancer 122(1):4–22CrossRefPubMed
16.
Lohuis PJ, Joshi A, Borggreven PA, Vermeeren L, Zupan-Kajcovski B, Al-Mamgani A, Balm AJ (2016) Aggressive basal cell carcinoma of the head and neck: challenges in surgical management. Eur Arch Otorhinolaryngol 273(11):3881–3889CrossRefPubMed
17.
Morid MA, Borjali A, Del Fiol G (2021) A scoping review of transfer learning research on medical image analysis using imagenet. Comput Biol Med 128:104115CrossRefPubMed
18.
Santilli AM, Jamzad A, Janssen NN, Kaufmann M, Connolly L, Vanderbeck K, Wang A, McKay D, Rudan JF, Fichtinger G, Mousavi P (2020) Perioperative margin detection in basal cell carcinoma using a deep learning framework: a feasibility study. Int J Comput Assist Radiol Surg 15(5):887–896CrossRefPubMed
19.
Schäfer KC, Balog J, Szaniszlo T, Szalay D, Mezey G, Dénes J, Bognar L, Oertel M, Takáts Z (2011) Real time analysis of brain tissue by direct combination of ultrasonic surgical aspiration and sonic spray mass spectrometry. Anal Chem 83(20):7729–7735CrossRefPubMed
20.
Taleb A, Lippert C, Klein T, Nabi M (2021) Multimodal self-supervised learning for medical image analysis. In: International Conference on Information Processing in Medical Imaging. pp 661–673
21.
22.
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921–2929
Metadata
Title
ImSpect: Image-driven self-supervised learning for surgical margin evaluation with mass spectrometry
Authors
Laura Connolly
Fahimeh Fooladgar
Amoon Jamzad
Martin Kaufmann
Ayesha Syeda
Kevin Ren
Purang Abolmaesumi
John F. Rudan
Doug McKay
Gabor Fichtinger
Parvin Mousavi
Publication date
10-04-2024
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-024-03106-1