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

Authors :
Martina De Landro
Eric Felli
Toby Collins
Richard Nkusi
Andrea Baiocchini
Manuel Barberio
Annalisa Orrico
Margherita Pizzicannella
Alexandre Hostettler
Michele Diana
Paola Saccomandi
Source :
Sensors, Vol 21, Iss 20, p 6934 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

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.

Details

Language :
English
ISSN :
14248220
Volume :
21
Issue :
20
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.5301238791464962ac963fa4b0665b73
Document Type :
article
Full Text :
https://doi.org/10.3390/s21206934