RESUMO
PURPOSE: Live intra-operative functional imaging has multiple potential clinical applications, such as localization of ischemia, assessment of organ transplantation success and perfusion monitoring. Recent research has shown that live monitoring of functional tissue properties, such as tissue oxygenation and blood volume fraction, is possible using multispectral imaging in laparoscopic surgery. While the illuminant spectrum is typically kept constant in laparoscopic surgery and can thus be estimated from preoperative calibration images, a key challenge in open surgery originates from the dynamic changes of lighting conditions. METHODS: The present paper addresses this challenge with a novel approach to light source calibration based on specular highlight analysis. It involves the acquisition of low-exposure time images serving as a basis for recovering the illuminant spectrum from pixels that contain a dominant specular reflectance component. RESULTS: Comprehensive in silico and in vivo experiments with a range of different light sources demonstrate that our approach enables an accurate and robust recovery of the illuminant spectrum in the field of view of the camera, which results in reduced errors with respect to the estimation of functional tissue properties. Our approach further outperforms state-of-the-art methods proposed in the field of computer vision. CONCLUSION: Our results suggest that low-exposure multispectral images are well suited for light source calibration via specular highlight analysis. This work thus provides an important first step toward live functional imaging in open surgery.
Assuntos
Laparoscopia/métodos , Iluminação , Monitorização Intraoperatória/métodos , Calibragem , Simulação por Computador , HumanosRESUMO
OBJECTIVE: Surgical data science is evolving into a research field that aims to observe everything occurring within and around the treatment process to provide situation-aware data-driven assistance. In the context of endoscopic video analysis, the accurate classification of organs in the field of view of the camera proffers a technical challenge. Herein, we propose a new approach to anatomical structure classification and image tagging that features an intrinsic measure of confidence to estimate its own performance with high reliability and which can be applied to both RGB and multispectral imaging (MI) data. METHODS: Organ recognition is performed using a superpixel classification strategy based on textural and reflectance information. Classification confidence is estimated by analyzing the dispersion of class probabilities. Assessment of the proposed technology is performed through a comprehensive in vivo study with seven pigs. RESULTS: When applied to image tagging, mean accuracy in our experiments increased from 65% (RGB) and 80% (MI) to 90% (RGB) and 96% (MI) with the confidence measure. CONCLUSION: Results showed that the confidence measure had a significant influence on the classification accuracy, and MI data are better suited for anatomical structure labeling than RGB data. SIGNIFICANCE: This paper significantly enhances the state of art in automatic labeling of endoscopic videos by introducing the use of the confidence metric, and by being the first study to use MI data for in vivo laparoscopic tissue classification. The data of our experiments will be released as the first in vivo MI dataset upon publication of this paper.
Assuntos
Procedimentos Cirúrgicos do Sistema Digestório/métodos , Sistema Digestório/diagnóstico por imagem , Processamento de Imagem Assistida por Computador/métodos , Laparoscopia/métodos , Animais , Baço/diagnóstico por imagem , Suínos , Gravação em VídeoRESUMO
PURPOSE: Multispectral imaging can provide reflectance measurements at multiple spectral bands for each image pixel. These measurements can be used for estimation of important physiological parameters, such as oxygenation, which can provide indicators for the success of surgical treatment or the presence of abnormal tissue. The goal of this work was to develop a method to estimate physiological parameters in an accurate and rapid manner suited for modern high-resolution laparoscopic images. METHODS: While previous methods for oxygenation estimation are based on either simple linear methods or complex model-based approaches exclusively suited for off-line processing, we propose a new approach that combines the high accuracy of model-based approaches with the speed and robustness of modern machine learning methods. Our concept is based on training random forest regressors using reflectance spectra generated with Monte Carlo simulations. RESULTS: According to extensive in silico and in vivo experiments, the method features higher accuracy and robustness than state-of-the-art online methods and is orders of magnitude faster than other nonlinear regression based methods. CONCLUSION: Our current implementation allows for near real-time oxygenation estimation from megapixel multispectral images and is thus well suited for online tissue analysis.