Pilar Beatriz Garcia-Allende

Concurrent video-rate color and near-infrared fluorescence laparoscopy

Abstract. The visual identification and demarcation of tumors and tumor margins remains challenging due to the low optical contrast of cancer cells over surrounding tissues. Fluorescence molecular imaging was recently considered clinically for improving cancer detection during open surgery. We present herein a next step in the development of fluorescence molecular guidance by describing a novel video-rate imaging laparoscope capable of concurrently recording color and near-infrared fluorescence images and video. Video-rate operation is based on graphics processing unit-based image processing. We examine the optical characteristics of the system developed and provide performance metrics related to intra-operative endoscopic guidance, showcased on phantoms and endoscopic color and fluorescence molecular imaging of tumors in a mouse model of the human disease.

Automated identification of tumor microscopic morphology based on macroscopically measured scatter signatures

An automated algorithm and methodology is presented to identify tumor-tissue morphologies based on broadband scatter data measured by raster scan imaging of the samples. A quasi-confocal reflectance imaging system was used to directly measure the tissue scatter reflectance in situ, and the spectrum was used to identify the scattering power, amplitude, and total wavelength-integrated intensity. Pancreatic tumor and normal samples were characterized using the instrument, and subtle changes in the scatter signal were encountered within regions of each sample. Discrimination between normal versus tumor tissue was readily performed using a K-nearest neighbor classifier algorithm. A similar approach worked for regions of tumor morphology when statistical preprocessing of the scattering parameters was included to create additional data features. This type of automated interpretation methodology can provide a tool for guiding surgical resection in areas where microscopy imaging cannot be realized efficiently by the surgeon. In addition, the results indicate important design changes for future systems.

Automated classification of breast pathology using local measures of broadband reflectance

We demonstrate that morphological features pertinent to a tissues pathology may be ascertained from localized measures of broadband reflectance, with a mesoscopic resolution (100-μm lateral spot size) that permits scanning of an entire margin for residual disease. The technical aspects and optimization of a k-nearest neighbor classifier for automated diagnosis of pathologies are presented, and its efficacy is validated in 29 breast tissue specimens. When discriminating between benign and malignant pathologies, a sensitivity and specificity of 91 and 77% was achieved. Furthermore, detailed subtissue-type analysis was performed to consider how diverse pathologies influence scattering response and overall classification efficacy. The increased sensitivity of this technique may render it useful to guide the surgeon or pathologist where to sample pathology for microscopic assessment.

Morphological analysis of optical coherence tomography images for automated classification of gastrointestinal tissues.

The impact of digestive diseases, which include disorders affecting the oropharynx and alimentary canal, ranges from the inconvenience of a transient diarrhoea to dreaded conditions such as pancreatic cancer, which are usually fatal. Currently, the major limitation for the diagnosis of such diseases is sampling error because, even in the cases of rigorous adherence to biopsy protocols, only a tiny fraction of the surface of the involved gastrointestinal tract is sampled. Optical coherence tomography (OCT), which is an interferometric imaging technique for the minimally invasive measurement of biological samples, could decrease sampling error, increase yield, and even eliminate the need for tissue sampling provided that an automated, quick and reproducible tissue classification system is developed. Segmentation and quantification of ophthalmologic pathologies using OCT traditionally rely on the extraction of thickness and size measures from the OCT images, but layers are often not observed in nonopthalmic OCT imaging. Distinct mathematical methods, namely Principal Component Analysis (PCA) and textural analyses including both spatial textural analysis derived from the two-dimensional discrete Fourier transform (DFT) and statistical texture analysis obtained independently from center-symmetric autocorrelation (CSAC) and spatial grey-level dependency matrices (SGLDM), have been previously reported to overcome this problem. We propose an alternative approach consisting of a region segmentation according to the intensity variation along the vertical axis and a pure statistical technique for feature quantification, i.e. morphological analysis. Qualitative and quantitative comparisons with traditional approaches are accomplished in the discrimination of freshly-excised specimens of gastrointestinal tissues to exhibit the feasibility of the proposed method for computer-aided diagnosis (CAD) in the clinical setting.

Direct identification of breast cancer pathologies using blind separation of label-free localized reflectance measurements

Breast tumors are blindly identified using Principal (PCA) and Independent Component Analysis (ICA) of localized reflectance measurements. No assumption of a particular theoretical model for the reflectance needs to be made, while the resulting features are proven to have discriminative power of breast pathologies. Normal, benign and malignant breast tissue types in lumpectomy specimens were imaged ex vivo and a surgeon-guided calibration of the system is proposed to overcome the limitations of the blind analysis. A simple, fast and linear classifier has been proposed where no training information is required for the diagnosis. A set of 29 breast tissue specimens have been diagnosed with a sensitivity of 96% and specificity of 95% when discriminating benign from malignant pathologies. The proposed hybrid combination PCA-ICA enhanced diagnostic discrimination, providing tumor probability maps, and intermediate PCA parameters reflected tissue optical properties.

Comprehensive phantom for interventional fluorescence molecular imaging

Abstract. Fluorescence imaging has been considered for over a half-century as a modality that could assist surgical guidance and visualization. The administration of fluorescent molecules with sensitivity to disease biomarkers and their imaging using a fluorescence camera can outline pathophysiological parameters of tissue invisible to the human eye during operation. The advent of fluorescent agents that target specific cellular responses and molecular pathways of disease has facilitated the intraoperative identification of cancer with improved sensitivity and specificity over nonspecific fluorescent dyes that only outline the vascular system and enhanced permeability effects. With these new abilities come unique requirements for developing phantoms to calibrate imaging systems and algorithms. We briefly review herein progress with fluorescence phantoms employed to validate fluorescence imaging systems and results. We identify current limitations and discuss the level of phantom complexity that may be required for developing a universal strategy for fluorescence imaging calibration. Finally, we present a phantom design that could be used as a tool for interlaboratory system performance evaluation.

Spectral and Optimized Marks for Qualitative Material Discrimination

A method for automatic qualitative classification of liquid samples based on their absorption spectrum in the ultraviolet, visible and near-infrared regions is presented. A fiber-optic setup has been employed for material characterization. A simple technique based on image matching is proposed to perform spectra comparison. This alternative implementation of conventional spectrum matching methodologies can be easily translated to hardware platforms thus improving the time response of the classification system. The proposed method does not make any assumption on the probability density function of the data and it is also capable of automatic outlier removal. “Spectral marks” based on the polar representation of the absorption spectra and their “optimized marks” improved by means of Principal Component Analysis have been implemented. Preliminary discrimination results have been obtained on the classification of different oil samples from seeds and olives.

Optical spectroscopic sensors: From the control of industrial processes to tumor delineation

Optical spectroscopy is a consolidated line of research with several translational opportunities in the industrial and clinical contexts. The reflected and transmitted light spectrum of gaseous, liquid and solid materials offers direct information about the identification and quantification of their components, its morphology, etc. Different fiber-optics and non-fiber optics systems acquire the spectrum depending on the application field. Supervised or blind analyses of the materials spectrum allow the separation of raw material, to know the presence and concentration of dangerous gases, the assessment on the correct recipes of textile dyes or the identification of tumors and cardiovascular pathologies.

Spectral marks for qualitative discriminant analysis

In this paper, a method for the automatic qualitative discrimination of liquid samples based on their absorption spectrum in the ultraviolet, visible and near-infrared regions is presented. An alternative implementation of conventional spectrum matching methodologies is proposed working towards the improvement of the response time of the discrimination system. The method takes advantage of not making assumptions on the probability density function of the data and it is also capable of automatic outlier removal. Preliminary discrimination results have been evaluated on the classification of different oil samples from seeds and olives. The system here proposed could be easily and efficiently implemented in hardware platforms, improving in this way the system performance.

Automated segmentation based upon remitted scatter spectra from pathologically distinct tumor regions

Multi-spectral scatter visualization of tissue ultra-structure in situ can provide a unique tool for guiding surgical resection, but since changes are subtle and the data is multi-parametric, an automated methodology was sought to interpret these data, in order to classify their tissue sub-type. Tissue types observed across AsPC-1 pancreatic tumor samples were pathologically classified under three major groups (epithelium, fibrosis and necrosis) and the variations in scattering parameters, i.e. scattering power, scattering amplitude and average scattered intensity, across these groups were analyzed. The proposed scheme uses statistical pre-processing of the scattering parameter images to create additional data features followed by a k-nearest neighbors (kNN) based algorithm for tissue type classification. The classification accuracy inside some predefined regions of interest was determined and the mean region values of scattering parameters turned out to be stronger data sets for classification, rather than the individual pixel values. This presumably indicates that pixel-to-pixel variations in the remitted spectra need to be minimized for reliable classification approaches. Results show a strong correlation between the automated and expert-based classification within the predefined regions of interest.