Surgical optomics: hyperspectral imaging and deep learning towards precision intraoperative automatic tissue recognition-results from the EX-MACHYNA trial.

Date publication

mai 2024

Journal

Surgical endoscopy

Auteurs

Membres identifiés du Cancéropôle Est :
Pr MARESCAUX Jacques , Pr PESSAUX Patrick

Tous les auteurs :
Bannone E, Collins T, Esposito A, Cinelli L, De Pastena M, Pessaux P, Felli E, Andreotti E, Okamoto N, Barberio M, Felli E, Montorsi RM, Ingaglio N, Rodríguez-Luna MR, Nkusi R, Marescaux J, Hostettler A, Salvia R, Diana M

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/38789623

Résumé

Hyperspectral imaging (HSI), combined with machine learning, can help to identify characteristic tissue signatures enabling automatic tissue recognition during surgery. This study aims to develop the first HSI-based automatic abdominal tissue recognition with human data in a prospective bi-center setting.

Mots clés

Convolutional neural networks, Deep learning, Hyperspectral imaging, Image-guided surgery, Semantic scene segmentation, Surgical data science

Référence

Surg Endosc. 2024 05 24;: