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Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning
| Content Provider | MDPI |
|---|---|
| Author | Landro, Martina De Felli, Eric Collins, Toby Nkusi, Richard Baiocchini, Andrea Barberio, Manuel Orrico, Annalisa Pizzicannella, Margherita Hostettler, Alexandre Diana, Michele Saccomandi, Paola |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 6934 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21206934 |
| Journal | Sensors |
| Issue Number | 20 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-10-19 |
| Access Restriction | Open |
| Subject Keyword | Sensors Radiology, Nuclear Medicine and Imaging Convolutional Neural Network Deep Learning Hyperspectral Imaging Infrared Imaging in Vivo Experiments Laser Ablation Remote Sensing Thermal Damage Thermal Damage Prediction |
| Content Type | Text |
| Resource Type | Article |
