Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning.

De Landro, Martina; Felli, Eric; Collins, Toby; Nkusi, Richard; Baiocchini, Andrea; Barberio, Manuel; Orrico, Annalisa; Pizzicannella, Margherita; Hostettler, Alexandre; Diana, Michele; Saccomandi, Paola (2021). Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning. Sensors, 21(20) MDPI 10.3390/s21206934

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Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches, especially for tumors in high-risk locations. Precise control of the LA effect is required to safely destroy the tumor. Although temperature imaging techniques provide an indirect measurement of the thermal damage, a degree of uncertainty remains about the treatment effect. Optical techniques are currently emerging as tools to directly assess tissue thermal damage. Among them, hyperspectral imaging (HSI) has shown promising results in image-guided surgery and in the thermal ablation field. The highly informative data provided by HSI, associated with deep learning, enable the implementation of non-invasive prediction models to be used intraoperatively. Here we show a novel paradigm "peak temperature prediction model" (PTPM), convolutional neural network (CNN)-based, trained with HSI and infrared imaging to predict LA-induced damage in the liver. The PTPM demonstrated an optimal agreement with tissue damage classification providing a consistent threshold (50.6 ± 1.5 °C) for the damage margins with high accuracy (~0.90). The high correlation with the histology score (r = 0.9085) and the comparison with the measured peak temperature confirmed that PTPM preserves temperature information accordingly with the histopathological assessment.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DBMR Forschung Mu35 > Forschungsgruppe Hepatologie
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DBMR Forschung Mu35 > Forschungsgruppe Hepatologie

04 Faculty of Medicine > Department of Gastro-intestinal, Liver and Lung Disorders (DMLL) > Clinic of Visceral Surgery and Medicine > Hepatology

UniBE Contributor:

Felli, Eric

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1424-8220

Publisher:

MDPI

Language:

English

Submitter:

Rahel Fuhrer

Date Deposited:

09 Nov 2021 13:47

Last Modified:

15 Aug 2024 21:13

Publisher DOI:

10.3390/s21206934

PubMed ID:

34696147

Uncontrolled Keywords:

convolutional neural network deep learning hyperspectral imaging in vivo experiments infrared imaging laser ablation remote sensing thermal damage thermal damage prediction

BORIS DOI:

10.48350/160806

URI:

https://boris.unibe.ch/id/eprint/160806

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