Optimal Hyperspectral Band Selection for Tissue Oxygenation Mapping with Generative Adversarial Network.
Annu Int Conf IEEE Eng Med Biol Soc
; 2023: 1-4, 2023 07.
Article
em En
| MEDLINE
| ID: mdl-38082981
ABSTRACT
Tissue oxygenation assessment using hyperspectral imaging is an emerging technique for the diagnosis and pre- and post-treatment monitoring of ischemic patients. However, the high spectral resolution of hyperspectral imaging leads to large data sizes and a long imaging time. In this study, we propose a method that utilizes multi-objective evolutionary algorithms to determine the optimal hyperspectral band combination when developing a deep learning model for predicting tissue oxygenation from hyperspectral images. Our results confirm that the deep learning model effectively predicts tissue oxygenation images for various oxygenation states. Moreover, we demonstrate that a high-performance prediction model can be developed using only a small number of spectral bands, indicating the potential for more efficient non-contact tissue oxygenation mapping with the proposed method.Clinical Relevance- The proposed method allows for the non-contact and efficient acquisition of two-dimensional tissue oxygenation information in various oxygenation states.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Isquemia
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article