Deep Learning Analysis of In Vivo Hyperspectral Images for Automated Intraoperative Nerve Detection

Diagnostics

Valentin Bencteux¹  Eric Felli¹  Manuel Barberio¹  Richard Nkusi²  Michele Diana³  Alexandre Hostettler³  Jacques Marescaux³  Toby Collins³  Massimo Giuseppe Viola⁴ 

[1] Department of Research, Institute of Image-Guided Surgery, IHU-Strasbourg, 67091 Strasbourg, France;Department of Research, Research Institute against Digestive Cancer, IRCAD Africa, Kigali 2 KN 30 ST, Rwanda;Department of Research, Research Institute against Digestive Cancer, IRCAD, 67091 Strasbourg, France;Department of Surgery, Ospedale Card. G. Panico, 73039 Tricase, Italy;

关键词: hyperspectral imaging;    artificial intelligence;    tissue recognition;    intraoperative navigation tool;    optical imaging;    deep learning;   

DOI  :  10.3390/diagnostics11081508

来源: DOAJ

【 摘 要 】

Nerves are critical structures that may be difficult to recognize during surgery. Inadvertent nerve injuries can have catastrophic consequences for the patient and lead to life-long pain and a reduced quality of life. Hyperspectral imaging (HSI) is a non-invasive technique combining photography with spectroscopy, allowing non-invasive intraoperative biological tissue property quantification. We show, for the first time, that HSI combined with deep learning allows nerves and other tissue types to be automatically recognized in in vivo hyperspectral images. An animal model was used, and eight anesthetized pigs underwent neck midline incisions, exposing several structures (nerve, artery, vein, muscle, fat, skin). State-of-the-art machine learning models were trained to recognize these tissue types in HSI data. The best model was a convolutional neural network (CNN), achieving an overall average sensitivity of 0.91 and a specificity of 1.0, validated with leave-one-patient-out cross-validation. For the nerve, the CNN achieved an average sensitivity of 0.76 and a specificity of 0.99. In conclusion, HSI combined with a CNN model is suitable for in vivo nerve recognition.

【 授权许可】

Unknown