Laboratory Hyperspectral Image Acquisition System Setup and Validation.

Hyperspectral Imaging (HSI) techniques have demonstrated potential to provide useful information in a broad set of applications in different domains, from precision agriculture to environmental science. A first step in the preparation of the algorithms to be employed outdoors starts at a laboratory level, capturing a high amount of samples to be analysed and processed in order to extract the necessary information about the spectral characteristics of the studied samples in the most precise way. In this article, a custom-made scanning system for hyperspectral image acquisition is described. Commercially available components have been carefully selected in order to be integrated into a flexible infrastructure able to obtain data from any Generic Interface for Cameras (GenICam) compliant devices using the gigabyte Ethernet interface. The entire setup has been tested using the Specim FX hyperspectral series (FX10 and FX17) and a Graphical User Interface (GUI) has been developed in order to control the individual components and visualise data. Morphological analysis, spectral response and optical aberration of these pushbroom-type hyperspectral cameras have been evaluated prior to the validation of the whole system with different plastic samples for which spectral signatures are extracted and compared with well-known spectral libraries.

Deep Learning-Based Framework for In Vivo Identification of Glioblastoma Tumor using Hyperspectral Images of Human Brain.

The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor resection in real-time during neurosurgical procedures is a current clinical need. Hyperspectral imaging is a non-contact, non-ionizing, and label-free imaging modality that can assist surgeons during this challenging task without using any contrast agent. In this work, we present a deep learning-based framework for processing hyperspectral images of in vivo human brain tissue. The proposed framework was evaluated by our human image database, which includes 26 in vivo hyperspectral cubes from 16 different patients, among which 258,810 pixels were labeled. The proposed framework is able to generate a thematic map where the parenchymal area of the brain is delineated and the location of the tumor is identified, providing guidance to the operating surgeon for a successful and precise tumor resection. The deep learning pipeline achieves an overall accuracy of 80% for multiclass classification, improving the results obtained with traditional support vector machine (SVM)-based approaches. In addition, an aid visualization system is presented, where the final thematic map can be adjusted by the operating surgeon to find the optimal classification threshold for the current situation during the surgical procedure.

SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples.

The work presented in this paper is focused on the use of spectroscopy to identify the type of tissue of human brain samples employing support vector machine classifiers. Two different spectrometers were used to acquire infrared spectroscopic signatures in the wavenumber range between 1200⁻3500 cm-1. An extensive analysis was performed to find the optimal configuration for a support vector machine classifier and determine the most relevant regions of the spectra for this particular application. The results demonstrate that the developed algorithm is robust enough to classify the infrared spectroscopic data of human brain tissue at three different discrimination levels.

Accelerating the K-Nearest Neighbors Filtering Algorithm to Optimize the Real-Time Classification of Human Brain Tumor in Hyperspectral Images.

The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of information captured by the sensors. One of the stages with higher computational load is the K-Nearest Neighbors (KNN) filtering algorithm. The main goal of this study is to optimize and parallelize the KNN algorithm by exploiting the GPU technology to obtain real-time processing during brain cancer surgical procedures. This parallel version of the KNN performs the neighbor filtering of a classification map (obtained from a supervised classifier), evaluating the different classes simultaneously. The undertaken optimizations and the computational capabilities of the GPU device throw a speedup up to 66.18× when compared to a sequential implementation.

An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation.

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400-1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.

Surgical Aid Visualization System for Glioblastoma Tumor Identification based on Deep Learning and In-Vivo Hyperspectral Images of Human Patients.

Brain cancer surgery has the goal of performing an accurate resection of the tumor and preserving as much as possible the quality of life of the patient. There is a clinical need to develop non-invasive techniques that can provide reliable assistance for tumor resection in real-time during surgical procedures. Hyperspectral imaging (HSI) arises as a new, noninvasive and non-ionizing technique that can assist neurosurgeons during this difficult task. In this paper, we explore the use of deep learning (DL) techniques for processing hyperspectral (HS) images of in-vivo human brain tissue. We developed a surgical aid visualization system capable of offering guidance to the operating surgeon to achieve a successful and accurate tumor resection. The employed HS database is composed of 26 in-vivo hypercubes from 16 different human patients, among which 258,810 labelled pixels were used for evaluation. The proposed DL methods achieve an overall accuracy of 95% and 85% for binary and multiclass classifications, respectively. The proposed visualization system is able to generate a classification map that is formed by the combination of the DL map and an unsupervised clustering via a majority voting algorithm. This map can be adjusted by the operating surgeon to find the suitable configuration for the current situation during the surgical procedure.

Detecting brain tumor in pathological slides using hyperspectral imaging.

Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. This research work presents a proof-of-concept on the use of HSI data to automatically detect human brain tumor tissue in pathological slides. The samples, consisting of hyperspectral cubes collected from 400 nm to 1000 nm, were acquired from ten different patients diagnosed with high-grade glioma. Based on the diagnosis provided by pathologists, a spectral library of normal and tumor tissues was created and processed using three different supervised classification algorithms. Results prove that HSI is a suitable technique to automatically detect high-grade tumors from pathological slides.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

Microbiological Study of León Raw Cow-Milk Cheese, a Spanish Craft Variety.

The levels of several microbial groups (aerobic mesophilic flora, aerobic psychrotrophic flora, lactic acid bacteria, Micrococcaceae, enterococci, Enterobacteriaceae, and molds and yeasts), and some biochemical parameters were investigated during the manufacture and ripening of four batches of León cow cheese produced from raw milk without the addition of starter cultures. The study of the microbial characteristics of this cheese constitutes the first step towards the establishment of a starter culture which would allow the making of a product both more uniform and safer from the point of view of health. The total microbial counts were high throughout the elaboration and ripening. Almost all the microbial groups reached their maximum counts in curd and afterwards dropped throughout the ripening process. The greatest drop was shown by Enterobacteriaceae, which had disappeared after 3 months of ripening. Lactic acid bacteria were the major microbial group, reaching counts similar to the total aerobic mesophilic flora at all sampling points. Lactococcus lactis subsp. lactis dominated in milk (62.5% of the isolates obtained in de Man-Rogosa-Sharpe (MRS) agar at this sampling point), curd (82.5% of the isolates obtained at this sampling point) and one-week-old cheese (85% of isolates obtained at this sampling point), while Lactobacillus casei subsp. casei was the most predominant species in eight-week-old cheese (55% of isolates obtained at this sampling point) and twelve-week-old cheese (47.5% of isolates obtained at this sampling point). According to our data, a starter suitable for the production of León cow cheese would be made up of these two species. Some species of Leuconostoc or enterococci could also be added to this starter with the aim of improving the organoleptic characteristics of the final product or to emphasize the characteristics of this variety.

Population Levels, Species, and Characteristics of Micrococcaceae during the Manufacturing and Ripening of Armada-Sobado Hard Goat's Milk Cheese.

The changes in the counts and the species of Micrococcaceae were studied throughout the manufacturing and ripening of a Spanish hard goat's milk cheese, the Armada-Sobado variety. In the milk, counts on mannitol salt agar (MSA) ranged from 2 × 104 to 5 × 104 CFU/g. These counts showed the maximum value in the curd (7 × 104 to 4 × 105 CFU/g), decreasing afterwards slowly but steadily throughout the ripening process to reach final counts on average 2 logarithmic units lower than those found in the curd. Of 280 isolates obtained from MSA during manufacturing and ripening, 66 (24%) were considered to be Micrococcaceae . Staphylococcus sciuri (22.5% of the isolates at this sampling point) and Staphylococcus saprophyticus (7.5%) were the only two species of staphylococci isolated from the milk. In the curd, S. sciuri increased its proportion (30%) whilst the percentage of S. saprophyticus remained constant. None of these species was isolated from the cheese. S. aureus was detected only in curd (7.5% of the isolates obtained at this sampling point). S. xylosus , S. capitis , S. epidermidis , and S. warneri were isolated from curd and cheese, or exclusively from cheese, but always in very low proportions. Micrococcus varians (10%) and M. roseus (5%) were the two species of micrococci isolated from the milk. M. varians increased its proportion in curd (17.5%) and could not be isolated in cheese. M. roseus appeared neither in curd nor in cheese. All the isolated staphylococcal strains were tested for production of A, B, C, and D enterotoxins. The three isolated strains of Staphylococcus aureus produced A and C enterotoxins, but neither B or D. Of 41 coagulase-negative strains only two of the Staphylococcus sciuri isolated from milk produced C enterotoxins.