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

Multi-modality imaging for assessment of the microcirculation in peripheral artery disease: Bench to clinical practice

Peripheral artery disease (PAD) is a highly prevalent disorder with a high risk of mortality and amputation despite the introduction of novel medical and procedural treatments. Microvascular disease (MVD) is common among patients with PAD, and despite the established role as a predictor of amputations and mortality, MVD is not routinely assessed as part of current standard practice. Recent pre-clinical and clinical perfusion and molecular imaging studies have confirmed the important role of MVD in the pathogenesis and outcomes of PAD. The recent advancements in the imaging of the peripheral microcirculation could lead to a better understanding of the pathophysiology of PAD, and result in improved risk stratification, and our evaluation of response to therapies. In this review, we will discuss the current understanding of the anatomy and physiology of peripheral microcirculation, and the role of imaging for assessment of perfusion in PAD, and the latest advancements in molecular imaging. By highlighting the latest advancements in multi-modality imaging of the peripheral microcirculation, we aim to underscore the most promising imaging approaches and highlight potential research opportunities, with the goal of translating these approaches for improved and personalized management of PAD in the future.

Classification of Benign-Malignant Thyroid Nodules Based on Hyperspectral Technology

In recent years, the incidence of thyroid cancer has rapidly increased. To address the issue of the inefficient diagnosis of thyroid cancer during surgery, we propose a rapid method for the diagnosis of benign and malignant thyroid nodules based on hyperspectral technology. Firstly, using our self-developed thyroid nodule hyperspectral acquisition system, data for a large number of diverse thyroid nodule samples were obtained, providing a foundation for subsequent diagnosis. Secondly, to better meet clinical practical needs, we address the current situation of medical hyperspectral image classification research being mainly focused on pixel-based region segmentation, by proposing a method for nodule classification as benign or malignant based on thyroid nodule hyperspectral data blocks. Using 3D CNN and VGG16 networks as a basis, we designed a neural network algorithm (V3Dnet) for classification based on three-dimensional hyperspectral data blocks. In the case of a dataset with a block size of 50 × 50 × 196, the classification accuracy for benign and malignant samples reaches 84.63%. We also investigated the impact of data block size on the classification performance and constructed a classification model that includes thyroid nodule sample acquisition, hyperspectral data preprocessing, and an algorithm for thyroid nodule classification as benign and malignant based on hyperspectral data blocks. The proposed model for thyroid nodule classification is expected to be applied in thyroid surgery, thereby improving surgical accuracy and providing strong support for scientific research in related fields.

Hyperspectral and multispectral imaging in neurosurgery: a systematic literature review and meta-analysis

INTRODUCTION

The neuro-surgical community is witnessing a rising interest for surgical application of multispectral/hyperspectral imaging. Several potential technical applications of this optical imaging are reported, but the set-up is variable and so are the processing methods. We present a systematic review of the relevant literature on the topic.

MATERIALS AND METHODS

A literature search based on the PRISMA principles was performed on PubMed, SCOPUS, and Web of Science, using MESH terms and Boolean operators. Papers regarding intra-operative in-vivo application of multispectral and/or hyperspectral imaging in humans during neurosurgical procedures were included. Papers reporting technologies related to radiological applications were excluded. A meta-analysis on the performance metrics was also conducted.

RESULTS

Our search string retrieved 20 papers. The main applications of optical imaging during neurosurgery concern tumour detection and improvement of the extent of resection (15 papers) or visualization of perfusion changes during neuro-oncology or neuro-vascular surgery (5 papers). All the retrieved articles were pilot studies, proof of concepts, or case reports, with limited number of patients recruited. Sensitivity, specificity, and accuracy were promising in most of the reports, but the metanalysis showed heterogeneous approaches and results among studies.

CONCLUSIONS

The present review shows that several approaches are currently being tested to integrate hyperspectral imaging in neurosurgery, but most of the studies reported a limited pool of patients, with different approaches to data collection and analysis. Further studies on larger cohorts of patients are therefore desirable to fully explore the potential of this imaging technique.

Unveiling the potential of diffusion model-based framework with transformer for hyperspectral image classification

Hyperspectral imaging has gained popularity for analysing remotely sensed images in various fields such as agriculture and medical. However, existing models face challenges in dealing with the complex relationships and characteristics of spectral-spatial data due to the multi-band nature and data redundancy of hyperspectral data. To address this limitation, we propose a novel approach called DiffSpectralNet, which combines diffusion and transformer techniques. The diffusion method is able extract diverse and meaningful spectral-spatial features, leading to improvement in HSI classification. Our approach involves training an unsupervised learning framework based on the diffusion model to extract high-level and low-level spectral-spatial features, followed by the extraction of intermediate hierarchical features from different timestamps for classification using a pre-trained denoising U-Net. Finally, we employ a supervised transformer-based classifier to perform the HSI classification. We conduct comprehensive experiments on three publicly available datasets to validate our approach. The results demonstrate that our framework significantly outperforms existing approaches, achieving state-of-the-art performance. The stability and reliability of our approach are demonstrated across various classes in all datasets.

Hyperspectral imaging for monitoring of free flaps of the oral cavity: A feasibility study

OBJECTIVES

Hyperspectral imaging (HSI) provides spectral information about hemoglobin, water and oxygen supply and has thus great potential in perfusion monitoring. The aim of the present study was to investigate the feasibility of HSI in the postoperative monitoring of intraoral free flaps.

METHODS

The 14 patients receiving reconstructive head and neck surgery with a radial forearm free flap were included. HSI was performed intraoperatively (t0), on Day 1 (t1), 2 (t2), 3-6 (t3), 7-9 (t4), 10-11 (t5) and 12-15 (t6) postoperatively. Flap tissue perfusion was assessed on defined regions of interest by calculating the perfusion indices Tissue Hemoglobin Index (THI), hemoglobin oxygenation (StO2 ), Near Infrared Perfusion Index (NIR Perfusion Index) and Tissue Water Index (TWI).

RESULTS

Image quality varied depending on location of the flap and time of measurement. StO2 was >50 intraoperatively and >40 on t1 for all patients. A significant difference was found solely for TWI between t0 and t2 and t0 and t4. No flap loss occurred.

CONCLUSIONS

The use of HSI in the monitoring of intraoral flaps is feasible and might become a valuable addition to the current clinical examination of free flaps.

Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection

Brain surgery is one of the most common and effective treatments for brain tumour. However, neurosurgeons face the challenge of determining the boundaries of the tumour to achieve maximum resection, while avoiding damage to normal tissue that may cause neurological sequelae to patients. Hyperspectral (HS) imaging (HSI) has shown remarkable results as a diagnostic tool for tumour detection in different medical applications. In this work, we demonstrate, with a robust k-fold cross-validation approach, that HSI combined with the proposed processing framework is a promising intraoperative tool for in-vivo identification and delineation of brain tumours, including both primary (high-grade and low-grade) and secondary tumours. Analysis of the in-vivo brain database, consisting of 61 HS images from 34 different patients, achieve a highest median macro F1-Score result of 70.2 ± 7.9% on the test set using both spectral and spatial information. Here, we provide a benchmark based on machine learning for further developments in the field of in-vivo brain tumour detection and delineation using hyperspectral imaging to be used as a real-time decision support tool during neurosurgical workflows.

Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review

Central nervous system tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive. AI, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.

Unsupervised band selection of medical hyperspectral images guided by data gravitation and weak correlation

BACKGROUND AND OBJECTIVE

Medical hyperspectral images (MHSIs) are used for a contact-free examination of patients without harmful radiation. However, high-dimensionality images contain large amounts of data that are sparsely distributed in a high-dimensional space, which leads to the "curse of dimensionality" (called Hughes' phenomenon) and increases the complexity and cost of data processing and storage. Hence, there is a need for spectral dimensionality reduction before the clinical application of MHSIs. Some dimensionality-reducing strategies have been proposed; however, they distort the data within MHSIs.

METHODS

To compress dimensionality without destroying the original data structure, we propose a method that involves data gravitation and weak correlation-based ranking (DGWCR) for removing bands of noise from MHSIs while clustering signal-containing bands. Band clustering is done by using the connection centre evolution (CCE) algorithm and selecting the most representative bands in each cluster based on the composite force. The bands within the clusters are ranked using the new entropy-containing matrix, and a global ranking of bands is obtained by applying an S-shaped strategy. The source code is available at https://www.github.com/zhangchenglong1116/DGWCR.

RESULTS

Upon feeding the reduced-dimensional images into various classifiers, the experimental results demonstrated that the small number of bands selected by the proposed DGWCR consistently achieved higher classification accuracy than the original data. Unlike other reference methods (e.g. the latest deep-learning-based strategies), DGWCR chooses the spectral bands with the least redundancy and greatest discrimination.

CONCLUSION

In this study, we present a method for efficient band selection for MHSIs that alleviates the "curse of dimensionality". Experiments were validated with three MHSIs in the human brain, and they outperformed several other band selection methods, demonstrating the clinical potential of DGWCR.

Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging

One of the leading causes of cancer deaths is esophageal cancer (EC) because identifying it in early stage is challenging. Computer-aided diagnosis (CAD) could detect the early stages of EC have been developed in recent years. Therefore, in this study, complete meta-analysis of selected studies that only uses hyperspectral imaging to detect EC is evaluated in terms of their diagnostic test accuracy (DTA). Eight studies are chosen based on the Quadas-2 tool results for systematic DTA analysis, and each of the methods developed in these studies is classified based on the nationality of the data, artificial intelligence, the type of image, the type of cancer detected, and the year of publishing. Deeks' funnel plot, forest plot, and accuracy charts were made. The methods studied in these articles show the automatic diagnosis of EC has a high accuracy, but external validation, which is a prerequisite for real-time clinical applications, is lacking.

Hyperspectral imaging enables the differentiation of differentially inflated and perfused pulmonary tissue: a proof-of-concept study in pulmonary lobectomies for intersegmental plane mapping

OBJECTIVES

The identification of the intersegmental plane is a major interoperative challenges during pulmonary segmentectomies. The objective of this pilot study is to test the feasibility of lung perfusion assessment by Hyperspectral Imaging for identification of the intersegmental plane.

METHODS

A pilot study (clinicaltrials.org: NCT04784884) was conducted in patients with lung cancer. Measuring tissue oxygenation (StO₂; upper tissue perfusion), organ hemoglobin index (OHI), near-infrared index (NIR; deeper tissue perfusion) and tissue water index (TWI), the Hyperspectral Imaging measurements were carried out in inflated (P_vent) and deflated pulmonary lobes (P_nV) as well as in deflated pulmonary lobes with divided circulation (P_nVC) before dissection of the lobar bronchus.

RESULTS

A total of 341 measuring points were evaluated during pulmonary lobectomies. Pulmonary lobes showed a reduced StO2 (P_vent: 84.56% ± 3.92 vs. P_nV: 63.62% ± 11.62 vs. P_nVC: 39.20% ± 23.57; p<0.05) and NIR-perfusion (P_vent: 50.55 ± 5.62 vs. P_nV: 47.55 ± 3.38 vs. P_nVC: 27.60 ± 9.33; p<0.05). There were no differences of OHI and TWI between the three groups.

CONCLUSIONS

This pilot study demonstrates that HSI enables differentiation between different ventilated and perfused pulmonary tissue as a precondition for HSI segment mapping.

Intraoperative Determination of Bronchus Stump and Anastomosis Perfusion with Hyperspectral Imaging

BACKGROUND

The intraoperative evaluation of bronchus perfusion is limited. Hyperspectral Imaging (HSI) is a newly established intraoperative imaging technique that enables a non-invasive, real-time perfusion analysis. Therefore, the purpose of this study was to determine the intraoperative perfusion of bronchus stump and anastomosis during pulmonary resections with HSI.

METHODS

In this prospective, IDEAL Stage 2a study (Clinicaltrials.gov: NCT04784884) HSI measurements were carried out before bronchial dissection and after bronchial stump formation or bronchial anastomosis, respectively. Tissue oxygenation (StO₂; upper tissue perfusion), organ hemoglobin index (OHI), near-infrared index (NIR; deeper tissue perfusion) and tissue water index (TWI) were calculated.

RESULTS

Bronchus stumps showed a reduced NIR (77.82 ± 10.27 vs 68.01 ± 8.95; P = 0,02158) and OHI (48.60 ± 1.39 vs 38.15 ± 9.74; P = <.0001), although the perfusion of the upper tissue layers was equivalent before and after resection (67.42% ± 12.53 vs 65.91% ± 10.40). In the sleeve resection group, we found both a significant decrease in StO 2 and NIR between central bronchus and anastomosis region (StO₂: 65.09% ± 12.57 vs 49.45 ± 9.94; P = .044; NIR: 83.73 ± 10.92 vs 58.62 ± 3.01; P = .0063). Additionally, NIR was decreased in the re-anastomosed bronchus compared to central bronchus region (83.73 ± 10.92 vs 55.15 ± 17.56; P = .0029).

CONCLUSIONS

Although both bronchus stumps and anastomosis show an intraoperative reduction of tissue perfusion, there is no difference of tissue hemoglobin level in bronchus anastomosis.

Spectral Similarity Measures for In Vivo Human Tissue Discrimination Based on Hyperspectral Imaging

PROBLEM

Similarity measures are widely used as an approved method for spectral discrimination or identification with their applications in different areas of scientific research. Even though a range of works have been presented, only a few showed slightly promising results for human tissue, and these were mostly focused on pathological and non-pathological tissue classification.

METHODS

In this work, several spectral similarity measures on hyperspectral (HS) images of in vivo human tissue were evaluated for tissue discrimination purposes. Moreover, we introduced two new hybrid spectral measures, called SID-JM-TAN(SAM) and SID-JM-TAN(SCA). We analyzed spectral signatures obtained from 13 different human tissue types and two different materials (gauze, instruments), collected from HS images of 100 patients during surgeries.

RESULTS

The quantitative results showed the reliable performance of the different similarity measures and the proposed hybrid measures for tissue discrimination purposes. The latter produced higher discrimination values, up to 6.7 times more than the classical spectral similarity measures. Moreover, an application of the similarity measures was presented to support the annotations of the HS images. We showed that the automatic checking of tissue-annotated thyroid and colon tissues was successful in 73% and 60% of the total spectra, respectively. The hybrid measures showed the highest performance. Furthermore, the automatic labeling of wrongly annotated tissues was similar for all measures, with an accuracy of up to 90%.

CONCLUSION

In future work, the proposed spectral similarity measures will be integrated with tools to support physicians in annotations and tissue labeling of HS images.

Deep Learning in Medical Hyperspectral Images: A Review

With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and achieve some progress. This paper introduces the principles and techniques of hyperspectral imaging systems, summarizes the common medical hyperspectral imaging systems, and summarizes the progress of some emerging spectral imaging systems through analyzing the literature. In particular, this article introduces the more frequently used medical hyperspectral images and the pre-processing techniques of the spectra, and in other sections, it discusses the main developments of medical hyperspectral combined with deep learning for disease diagnosis. On the basis of the previous review, tne limited factors in the study on the application of deep learning to hyperspectral medical images are outlined, promising research directions are summarized, and the future research prospects are provided for subsequent scholars.

Imaging perfusion changes in oncological clinical applications by hyperspectral imaging: a literature review

BACKGROUND

Hyperspectral imaging (HSI) is a promising imaging modality that uses visible light to obtain information about blood flow. It has the distinct advantage of being noncontact, nonionizing, and noninvasive without the need for a contrast agent. Among the many applications of HSI in the medical field are the detection of various types of tumors and the evaluation of their blood flow, as well as the healing processes of grafts and wounds. Since tumor perfusion is one of the critical factors in oncology, we assessed the value of HSI in quantifying perfusion changes during interventions in clinical oncology through a systematic review of the literature.

MATERIALS AND METHODS

The PubMed and Web of Science electronic databases were searched using the terms "hyperspectral imaging perfusion cancer" and "hyperspectral imaging resection cancer". The inclusion criterion was the use of HSI in clinical oncology, meaning that all animal, phantom, ex vivo, experimental, research and development, and purely methodological studies were excluded.

RESULTS

Twenty articles met the inclusion criteria. The anatomic locations of the neoplasms in the selected articles were as follows: kidneys (1 article), breasts (2 articles), eye (1 article), brain (4 articles), entire gastrointestinal (GI) tract (1 article), upper GI tract (5 articles), and lower GI tract (6 articles).

CONCLUSIONS

HSI is a potentially attractive imaging modality for clinical application in oncology, with assessment of mastectomy skin flap perfusion after reconstructive breast surgery and anastomotic perfusion during reconstruction of gastrointenstinal conduit as the most promising at present.

The diagnostic accuracy of intraoperative differentiation and delineation techniques in brain tumours

Brain tumour identification and delineation in a timeframe of seconds would significantly guide and support surgical decisions. Here, treatment is often complicated by the infiltration of gliomas in the surrounding brain parenchyma. Accurate delineation of the invasive margins is essential to increase the extent of resection and to avoid postoperative neurological deficits. Currently, histopathological annotation of brain biopsies and genetic phenotyping still define the first line treatment, where results become only available after surgery. Furthermore, adjuvant techniques to improve intraoperative visualisation of the tumour tissue have been developed and validated. In this review, we focused on the sensitivity and specificity of conventional techniques to characterise the tumour type and margin, specifically fluorescent-guided surgery, neuronavigation and intraoperative imaging as well as on more experimental techniques such as mass spectrometry-based diagnostics, Raman spectrometry and hyperspectral imaging. Based on our findings, all investigated methods had their advantages and limitations, guiding researchers towards the combined use of intraoperative imaging techniques. This can lead to an improved outcome in terms of extent of tumour resection and progression free survival while preserving neurological outcome of the patients.

Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review

Accurate tissue differentiation during orthopedic and neurological surgeries is critical, given that such surgeries involve operations on or in the vicinity of vital neurovascular structures and erroneous surgical maneuvers can lead to surgical complications. By now, the number of emerging technologies tackling the problem of intraoperative tissue classification methods is increasing. Therefore, this systematic review paper intends to give a general overview of existing technologies. The review was done based on the PRISMA principle and two databases: PubMed and IEEE Xplore. The screening process resulted in 60 full-text papers. The general characteristics of the methodology from extracted papers included data processing pipeline, machine learning methods if applicable, types of tissues that can be identified with them, phantom used to conduct the experiment, and evaluation results. This paper can be useful in identifying the problems in the current status of the state-of-the-art intraoperative tissue classification methods and designing new enhanced techniques.

Adaptive denoising hyperspectral data for visualization enhancement of intraoperative tissue

The vast amount of reflectance information obtained from the hyperspectral imaging devices offers great opportunities for investigating the function and structure of human tissue. However, the captured hyperspectral data often contain various noises due to the intrinsic imperfection of associated electrical and optical imaging components. This work proposed an automatic total variation algorithm to suppress the noises while preserving the details of the spectral and spatial information. The variation of spectral images at neighboring bands was calculated for regulating the total variation of hyperspectral data so that the spectral-dependent noises can be treated differentially across all bands. Experimental results demonstrated that the proposed method could effectively remove the spectral noises, especially near the ends of those extreme bands. The noise suppressed hyperspectral data could then be used for the visualization enhancement on pathophysiological conditions of intraoperative observed anatomies such as the vessels of brain tissues.

Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery

The study of brain science is vital to human health. The application of hyperspectral imaging in biomedical fields has grown dramatically in recent years due to their unique optical imaging method and multidimensional information acquisition. Hyperspectral imaging technology can acquire two-dimensional spatial information and one-dimensional spectral information of biological samples simultaneously, covering the ultraviolet, visible and infrared spectral ranges with high spectral resolution, which can provide diagnostic information about the physiological, morphological and biochemical components of tissues and organs. This technology also presents finer spectral features for brain imaging studies, and further provides more auxiliary information for cerebral disease research. This paper reviews the recent advance of hyperspectral imaging in cerebral diagnosis. Firstly, the experimental setup, image acquisition and pre-processing, and analysis methods of hyperspectral technology were introduced. Secondly, the latest research progress and applications of hyperspectral imaging in brain tissue metabolism, hemodynamics, and brain cancer diagnosis in recent years were summarized briefly. Finally, the limitations of the application of hyperspectral imaging in cerebral disease diagnosis field were analyzed, and the future development direction was proposed.

Visible and Near-Infrared hyperspectral imaging (HSI) can reliably quantify CD3 and CD45 positive inflammatory cells in myocarditis: Pilot study on formalin-fixed paraffin-embedded specimens from myocard obtained during autopsy

INTRODUCTION

To implement Hyperspectral Imaging (HSI) as a tool for quantifying inflammatory cells in tissue specimens by the example of myocarditis in a collective of forensic patients.

MATERIAL AND METHODS

44 consecutive patients with suspected myocardial inflammation at autopsy, diagnosed between 2013 and 2018 at the Institute of ForensicMedicine, Medical University of Innsbruck, were selected for this study. Using the IMEC SNAPSCAN camera, visible and near infrared hyperspectral images were collected from slides stained with CD3 and CD45 to assess quantity and spatial distribution of positive cells. Results were compared with visual assessment (VA) and conventional digital image analysis (DIA).

RESULTS

Finally, specimens of 40 patients were evaluated, of whom 36 patients (90%) suffered from myocarditis, two patients (5%) had suspected healing/healed myocarditis, and two did no have myocarditis (5%). The amount of CD3 and CD45 positive cells did not differ significantly between VA, HSI, and DIA (p^VA/HSI/DIA = 0.46 for CD3 and 0.81 for CD45). Coheńs Kappa showed a very high correlation between VA versus HSI, VA versus DIA, and HSI versus DIA for CD3 (Coheńs Kappa = 0.91, 1.00, and 0.91, respectively). For CD45 an almost as high correlation was seen for VA versus HSI and HSI versus DIA (Coheńs Kappa = 0.75 and 0.70) and VA versus DIA (Coheńs Kappa = 0.89).

CONCLUSION

HSI is a reliable and objective method to count inflammatory cells in tissue slides of suspected myocarditis. Implementation of HSI in digital pathology might further expand the possibility of a sophisticated method.

Evaluation of Hyperspectral Imaging for Follow-Up Assessment after Revascularization in Peripheral Artery Disease

Background: Assessment of tissue oxygenation is an important aspect of detection and monitoring of patients with peripheral artery disease (PAD). Hyperspectral imaging (HSI) is a non-contact technology for assessing microcirculatory function by quantifying tissue oxygen saturation (StO₂). This study investigated whether HSI can be used to monitor skin oxygenation in patients with PAD after appropriate treatment of the lower extremities. Methods: For this purpose, 37 patients with PAD were studied by means of ankle–brachial index (ABI) and HSI before and after surgical or endovascular therapy. Thereby, the oxygenation parameter StO₂ and near infrared (NIR) perfusion index were quantified in seven angiosomes on the diseased lower leg and foot. In addition, the effects of skin temperature and physical activity on StO₂ and the NIR perfusion index and the respective inter-operator variability of these parameters were investigated in 25 healthy volunteers. Results: In all patients, the ABI significantly increased after surgical and endovascular therapy. In parallel, HSI revealed significant changes in both StO₂ and NIR perfusion index in almost all studied angiosomes depending on the performed treatment. The increase in tissue oxygenation saturation was especially pronounced after surgical treatment. Neither heat nor cold, nor physical activity, nor repeated assessments of HSI parameters by independent investigators significantly affected the results on StO₂ and the NIR perfusion index. Conclusions: Tissue oxygen saturation data obtained with HSI are robust to external confounders, such as temperature and physical activity, and do not show inter-operator variability; therefore, can be used as an additional technique to established methods, such as the ABI, to monitor peripheral perfusion in patients with PAD.

Empirical Mode Decomposition Based Hyperspectral Data Analysis for Brain Tumor Classification

The capability of Hyperspectral Imaging (HSI) in rapidly acquiring abundant reflectance data in a non-invasive manner, makes it an ideal tool for obtaining diagnostic information about tissue pathology. Identifying wavelengths that provide the most discriminatory clues for specific pathologies will greatly assist in understanding their underlying biochemical characteristics. In this paper, we propose an efficient and computationally inexpensive method for determining the most relevant spectral bands for brain tumor classification. Empirical mode decomposition was used in combination with extrema analysis to extract the relevant bands based on the morphological characteristics of the spectra. The results of our experiments indicate that the proposed method outperforms the benchmark in reducing computational complexity while performing comparably with a 7-times reduction in the feature-set for classification on the test data.

Artificial Intelligence in Brain Tumour Surgery-An Emerging Paradigm

Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced.

VNIR-NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection

Currently, intraoperative guidance tools used for brain tumor resection assistance during surgery have several limitations. Hyperspectral (HS) imaging is arising as a novel imaging technique that could offer new capabilities to delineate brain tumor tissue in surgical-time. However, the HS acquisition systems have some limitations regarding spatial and spectral resolution depending on the spectral range to be captured. Image fusion techniques combine information from different sensors to obtain an HS cube with improved spatial and spectral resolution. This paper describes the contributions to HS image fusion using two push-broom HS cameras, covering the visual and near-infrared (VNIR) [400–1000 nm] and near-infrared (NIR) [900–1700 nm] spectral ranges, which are integrated into an intraoperative HS acquisition system developed to delineate brain tumor tissue during neurosurgical procedures. Both HS images were registered using intensity-based and feature-based techniques with different geometric transformations to perform the HS image fusion, obtaining an HS cube with wide spectral range [435–1638 nm]. Four HS datasets were captured to verify the image registration and the fusion process. Moreover, segmentation and classification methods were evaluated to compare the performance results between the use of the VNIR and NIR data, independently, with respect to the fused data. The results reveal that the proposed methodology for fusing VNIR–NIR data improves the classification results up to 21% of accuracy with respect to the use of each data modality independently, depending on the targeted classification problem.

Automatic Recognition of Colon and Esophagogastric Cancer with Machine Learning and Hyperspectral Imaging

There are approximately 1.8 million diagnoses of colorectal cancer, 1 million diagnoses of stomach cancer, and 0.6 million diagnoses of esophageal cancer each year globally. An automatic computer-assisted diagnostic (CAD) tool to rapidly detect colorectal and esophagogastric cancer tissue in optical images would be hugely valuable to a surgeon during an intervention. Based on a colon dataset with 12 patients and an esophagogastric dataset of 10 patients, several state-of-the-art machine learning methods have been trained to detect cancer tissue using hyperspectral imaging (HSI), including Support Vector Machines (SVM) with radial basis function kernels, Multi-Layer Perceptrons (MLP) and 3D Convolutional Neural Networks (3DCNN). A leave-one-patient-out cross-validation (LOPOCV) with and without combining these sets was performed. The ROC-AUC score of the 3DCNN was slightly higher than the MLP and SVM with a difference of 0.04 AUC. The best performance was achieved with the 3DCNN for colon cancer and esophagogastric cancer detection with a high ROC-AUC of 0.93. The 3DCNN also achieved the best DICE scores of 0.49 and 0.41 on the colon and esophagogastric datasets, respectively. These scores were significantly improved using a patient-specific decision threshold to 0.58 and 0.51, respectively. This indicates that, in practical use, an HSI-based CAD system using an interactive decision threshold is likely to be valuable. Experiments were also performed to measure the benefits of combining the colorectal and esophagogastric datasets (22 patients), and this yielded significantly better results with the MLP and SVM models.

Evaluation of hyperspectral imaging to quantify perfusion changes during the modified Allen test

OBJECTIVES

To evaluate the capability of hyperspectral imaging (HSI), a contact-less and noninvasive technology, to monitor perfusion changes of the hand during a modified Allen test (MAT) and cuff occlusion test. Furthermore, the study aimed at obtaining objective perfusion parameters of the hand.

METHODS

HSI of the hand was performed on 20 healthy volunteers with a commercially available HSI system during a MAT and a cuff occlusion test. Besides gathering red-green-blue (RGB) images, the perfusion parameters tissue hemoglobin index (THI), (superficial tissue) hemoglobin oxygenation (StO2), near-infrared perfusion (NIR), and tissue water index (TWI) were calculated for four different regions of interest on the hand. For the MAT, occlusion (OI; the ratio between the condition during occlusion and before occlusion) and reperfusion (RI; the ratio between the non-occlusion state and the prior occlusion state) indices were calculated for each perfusion parameter. All data were correlated to the clinical findings.

RESULTS

False-color images showed visible differences between the various perfusion conditions during the MAT and cuff occlusion test. THI, StO2, and NIR behaved as expected from physiology, while TWI did not in the context of this study. During rest, mean THI, StO2, and NIR of the hand were 34 ± 2, 72 ± 9, and 61 ± 6, respectively. The RI for THI showed a roundabout threefold increase after reperfusion of both radial and ulnar artery and was thus, distinctly pronounced when compared with StO2 and NIR (~1.25). The OI was lowest for THI when compared with StO2 and NIR.

CONCLUSIONS

HSI with its parameters THI, StO2, and NIR proved to be suitable to evaluate perfusion of the hand. By this, it could complement visual inspection during the MAT for evaluating the functionality of the superficial palmary arch before radial or ulnar artery harvest. The presented RI might deliver useful comparative values to detect pathological perfusion disorders at an early stage. As microcirculation monitoring is crucial for many medical issues, HSI shows potential to be used, besides further applications, in the monitoring of (free) flaps and transplants and microcirculation monitoring of critically ill patients.

New evidence from hyperspectral imaging analysis on the effect of photobiomodulation therapy on normal skin oxygenation

The aim of this study was to assess the changes induced by photobiomodulation therapy in oxygenation of normal skin and underlying tissue using hyperspectral imaging combined with a chemometric regression approach. Eleven healthy adult volunteers were enrolled in this study. The dorsal side of the left hand of each subject was exposed to photobiomodulation therapy, while the correspondent side of the right hand was used as a control (placebo effect). Laser irradiation was performed with a laser diode system (635 nm, 15mW, 9 J/cm²) for 900 s. Changes in skin oxygenation were assessed before and after applying the photobiomodulation therapy and placebo using the hyperspectral imaging. Hyperspectral data analysis showed that variations of oxyhemoglobin and deoxyhemoglobin concentrations had no statistical significance in both groups. In conclusion, photobiomodulation therapy does not induce changes in oxyhemoglobin and deoxyhemoglobin concentrations in the normal skin measured from hyperspectral images, at least at λ = 635 nm and 900-s exposure time.

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

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.

Multivariate analysis of Brillouin imaging data by supervised and unsupervised learning

Brillouin imaging relies on the reliable extraction of subtle spectral information from hyperspectral datasets. To date, the mainstream practice has been to use line fitting of spectral features to retrieve the average peak shift and linewidth parameters. Good results, however, depend heavily on sufficient signal-to-noise ratio and may not be applicable in complex samples that consist of spectral mixtures. In this work, we thus propose the use of various multivariate algorithms that can be used to perform supervised or unsupervised analysis of the hyperspectral data, with which we explore advanced image analysis applications, namely unmixing, classification and segmentation in a phantom and live cells. The resulting images are shown to provide more contrast and detail, and obtained on a timescale ∼10² faster than fitting. The estimated spectral parameters are consistent with those calculated from pure fitting.

Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification

Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.

Diagnosis of cholangiocarcinoma from microscopic hyperspectral pathological dataset by deep convolution neural networks

This paper focuses on automatic Cholangiocarcinoma (CC) diagnosis from microscopic hyperspectral (HSI) pathological dataset with deep learning method. The first benchmark based on the microscopic hyperspectral pathological images is set up. Particularly, 880 scenes of multidimensional hyperspectral Cholangiocarcinoma images are collected and manually labeled each pixel as either tumor or non-tumor for supervised learning. Moreover, each scene from the slide is given a binary label indicating whether it is from a patient or a normal person. Different from traditional RGB images, the HSI acquires pixels in multiple spectral intervals, which is added as an extension on the channel dimension of 3-channel RGB image. This work aims at fully exploiting the spatial-spectral HSI data through a deep Convolution Neural Network (CNN). The whole scene is first divided into several patches. Then they are fed into CNN for the tumor/non-tumor binary prediction and the tumor area regression. The further diagnosis on the scene is made by random forest based on the features from patch prediction. Experiments show that HSI provides a more accurate result than RGB image. Moreover, a spectral interval convolution and normalization scheme are proposed for further mining the spectral information in HSI, which demonstrates the effectiveness of the spatial-spectral data for CC diagnosis.

Tongue Tumor Detection in Hyperspectral Images Using Deep Learning Semantic Segmentation

OBJECTIVE

The utilization of hyperspectral imaging (HSI) in real-time tumor segmentation during a surgery have recently received much attention, but it remains a very challenging task.

METHODS

In this work, we propose semantic segmentation methods, and compare them with other relevant deep learning algorithms for tongue tumor segmentation. To the best of our knowledge, this is the first work using deep learning semantic segmentation for tumor detection in HSI data using channel selection, and accounting for more spatial tissue context, and global comparison between the prediction map, and the annotation per sample. Results, and Conclusion: On a clinical data set with tongue squamous cell carcinoma, our best method obtains very strong results of average dice coefficient, and area under the ROC-curve of [Formula: see text], and [Formula: see text], respectively on the original spatial image size. The results show that a very good performance can be achieved even with a limited amount of data. We demonstrate that important information regarding tumor decision is encoded in various channels, but some channel selection, and filtering is beneficial over the full spectra. Moreover, we use both visual (VIS), and near-infrared (NIR) spectrum, rather than commonly used only VIS spectrum; although VIS spectrum is generally of higher significance, we demonstrate NIR spectrum is crucial for tumor capturing in some cases.

SIGNIFICANCE

The HSI technology augmented with accurate deep learning algorithms has a huge potential to be a promising alternative to digital pathology or a doctors' supportive tool in real-time surgeries.

COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer

The COVID-19 virus is spreading across the world very rapidly. The World Health Organization (WHO) declared it a global pandemic on 11 March 2020. Early detection of this virus is necessary because of the unavailability of any specific drug. The researchers have developed different techniques for COVID-19 detection, but only a few of them have achieved satisfactory results. There are three ways for COVID-19 detection to date, those are real-time reverse transcription-polymerize chain reaction (RT-PCR), Computed Tomography (CT), and X-ray plays. In this work, we have proposed a less expensive computational model for automatic COVID-19 detection from Chest X-ray and CT-scan images. Our paper has a two-fold contribution. Initially, we have extracted deep features from the image dataset and then introduced a completely novel meta-heuristic feature selection approach, named Clustering-based Golden Ratio Optimizer (CGRO). The model has been implemented on three publicly available datasets, namely the COVID CT-dataset, SARS-Cov-2 dataset, and Chest X-Ray dataset, and attained state-of-the-art accuracies of 99.31%, 98.65%, and 99.44%, respectively.

Automated Vision-Based Microsurgical Skill Analysis in Neurosurgery Using Deep Learning: Development and Preclinical Validation

BACKGROUND/OBJECTIVE

Technical skill acquisition is an essential component of neurosurgical training. Educational theory suggests that optimal learning and improvement in performance depends on the provision of objective feedback. Therefore, the aim of this study was to develop a vision-based framework based on a novel representation of surgical tool motion and interactions capable of automated and objective assessment of microsurgical skill.

METHODS

Videos were obtained from 1 expert, 6 intermediate, and 12 novice surgeons performing arachnoid dissection in a validated clinical model using a standard operating microscope. A mask region convolutional neural network framework was used to segment the tools present within the operative field in a recorded video frame. Tool motion analysis was achieved using novel triangulation metrics. Performance of the framework in classifying skill levels was evaluated using the area under the curve and accuracy. Objective measures of classifying the surgeons' skill level were also compared using the Mann-Whitney U test, and a value of P < 0.05 was considered statistically significant.

RESULTS

The area under the curve was 0.977 and the accuracy was 84.21%. A number of differences were found, which included experts having a lower median dissector velocity (P = 0.0004; 190.38 ms^-1 vs. 116.38 ms^-1), and a smaller inter-tool tip distance (median 46.78 vs. 75.92; P = 0.0002) compared with novices.

CONCLUSIONS

Automated and objective analysis of microsurgery is feasible using a mask region convolutional neural network, and a novel tool motion and interaction representation. This may support technical skills training and assessment in neurosurgery.

Hyperspectral imaging as a diagnostic tool to differentiate between amalgam tattoos and other dark pigmented intraoral lesions

The goal of this project is to identify any in-depth benefits and drawbacks in the diagnosis of amalgam tattoos and other pigmented intraoral lesions using hyperspectral imagery collected from amalgam tattoos, benign, and malignant melanocytic neoplasms. Software solutions capable of classifying pigmented lesions of the skin already exist, but conventional red, green and blue images may be reaching an upper limit in their performance. Emerging technologies, such as hyperspectral imaging (HSI) utilize more than a hundred, continuous data channels, while also collecting data in the infrared. A total of 18 paraffin-embedded human tissue specimens of dark pigmented intraoral lesions (including the lip) were analyzed using visible and near-infrared (VIS-NIR) hyperspectral imagery obtained from HE-stained histopathological slides. Transmittance data were collected between 450 and 900 nm using a snapshot camera mounted to a microscope with a halogen light source. VIS-NIR spectra collected from different specimens, such as melanocytic cells and other tissues (eg, epithelium), produced distinct and diagnostic spectra that were used to identify these materials in several regions of interest, making it possible to distinguish between intraoral amalgam tattoos (intramucosal metallic foreign bodies) and melanocytic lesions of the intraoral mucosa and the lip (each with P < .01 using the independent t test). HSI is presented as a diagnostic tool for the rapidly growing field of digital pathology. In this preliminary study, amalgam tattoos were reliably differentiated from melanocytic lesions of the oral cavity and the lip.

Hyperspectral Imaging for Glioblastoma Surgery: Improving Tumor Identification Using a Deep Spectral-Spatial Approach

The primary treatment for malignant brain tumors is surgical resection. While gross total resection improves the prognosis, a supratotal resection may result in neurological deficits. On the other hand, accurate intraoperative identification of the tumor boundaries may be very difficult, resulting in subtotal resections. Histological examination of biopsies can be used repeatedly to help achieve gross total resection but this is not practically feasible due to the turn-around time of the tissue analysis. Therefore, intraoperative techniques to recognize tissue types are investigated to expedite the clinical workflow for tumor resection and improve outcome by aiding in the identification and removal of the malignant lesion. Hyperspectral imaging (HSI) is an optical imaging technique with the power of extracting additional information from the imaged tissue. Because HSI images cannot be visually assessed by human observers, we instead exploit artificial intelligence techniques and leverage a Convolutional Neural Network (CNN) to investigate the potential of HSI in twelve in vivo specimens. The proposed framework consists of a 3D-2D hybrid CNN-based approach to create a joint extraction of spectral and spatial information from hyperspectral images. A comparison study was conducted exploiting a 2D CNN, a 1D DNN and two conventional classification methods (SVM, and the SVM classifier combined with the 3D-2D hybrid CNN) to validate the proposed network. An overall accuracy of 80% was found when tumor, healthy tissue and blood vessels were classified, clearly outperforming the state-of-the-art approaches. These results can serve as a basis for brain tumor classification using HSI, and may open future avenues for image-guided neurosurgical applications.

Oral and Dental Spectral Image Database—ODSI-DB

The most common imaging methods used in dentistry are X-ray imaging and RGB color photography. However, both imaging methods provide only a limited amount of information on the wavelength-dependent optical properties of the hard and soft tissues in the mouth. Spectral imaging, on the other hand, provides significantly more information on the medically relevant dental and oral features (e.g. caries, calculus, and gingivitis). Due to this, we constructed a spectral imaging setup and acquired 316 oral and dental reflectance spectral images, 215 of which are annotated by medical experts, of 30 human test subjects. Spectral images of the subjects’ faces and other areas of interest were captured, along with other medically relevant information (e.g., pulse and blood pressure). We collected these oral, dental, and face spectral images, their annotations and metadata into a publicly available database that we describe in this paper. This oral and dental spectral image database (ODSI-DB) provides a vast amount of data that can be used for developing, e.g., pattern recognition and machine vision applications for dentistry.

Artificial intelligence for brain diseases: A systematic review

Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.

Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing

Hyperspectral imaging is a multidimensional optical technique with the potential of providing fast and accurate tissue classification. The main challenge is the adequate processing of the multidimensional information usually linked to long processing times and significant computational costs, which require expensive hardware. In this study, we address the problem of tissue classification for intraoperative hyperspectral images of in vivo brain tissue. For this goal, two methodologies are introduced that rely on a blind linear unmixing (BLU) scheme for practical tissue classification. Both methodologies identify the characteristic end-members related to the studied tissue classes by BLU from a training dataset and classify the pixels by a minimum distance approach. The proposed methodologies are compared with a machine learning method based on a supervised support vector machine (SVM) classifier. The methodologies based on BLU achieve speedup factors of ~459× and ~429× compared to the SVM scheme, while keeping constant and even slightly improving the classification performance.

Intraoperative Assessment of Gastric Sleeve Oxygenation Using Hyperspectral Imaging in Esophageal Resection: A Feasibility Study

Introduction

Sufficient tissue oxygenation is essential for anastomotic healing in visceral surgery. Hyperspectral imaging (HSI) is a noncontact, noninvasive technique for clinical assessment of tissue oxygenation in real time.

Methods

In this case series, HSI was used in 4 patients who were admitted for either esophageal cancer or cardiac carcinoma (AEG type I or II). Thoraco-abdominal surgical esophageal resection was performed after staging and neoadjuvant therapy. Intraoperative oxygenation of superficial (StO₂) and underlying tissue (NIR perfusion index) of the gastric sleeve were studied intrathoracic by means of the TIVITA® Tissue HSI camera. This was performed prior to esophagogastric anastomosis. The postoperative course, especially in view of surgical complications, was recorded.

Results

Assessment of StO₂ and NIR perfusion index was performed in 4 regions of interest per gastric sleeve, aboral and oral of the clinically determined resection line. It allowed the fast quantification of gastric oxygenation prior gastroesophageal anastomosis. Median StO₂ aboral of the determined resection line was 69%, while median StO₂ in the oral part of the gastric sleeve was found at 53%. In contrast, the median NIR perfusion index was similar aboral (80) and oral (82) of the resection line. In none of the 4 studied patients, an anastomotic failure appeared.

Discussion/Conclusion

This report suggests that HSI is a feasible technique for intraoperative assessment of tissue oxygenation before gastroesophageal anastomosis and might reduce the incidence of anastomotic failure in the gastrointestinal tract.

Spectrally Tunable Neural Network-Assisted Segmentation of Microneurosurgical Anatomy

Background

Distinct tissue types are differentiated based on the surgeon’s knowledge and subjective visible information, typically assisted with white-light intraoperative imaging systems. Narrow-band imaging (NBI) assists in tissue identification and enables automated classifiers, but many anatomical details moderate computational predictions and cause bias. In particular, tissues’ light-source-dependent optical characteristics, anatomical location, and potentially hazardous microstructural changes such as peeling have been overlooked in previous literature.

Methods

Narrow-band images of five (n = 5) facial nerves (FNs) and internal carotid arteries (ICAs) were captured from freshly frozen temporal bones. The FNs were split into intracranial and intratemporal samples, and ICAs’ adventitia was peeled from the distal end. Three-dimensional (3D) spectral data were captured by a custom-built liquid crystal tunable filter (LCTF) spectral imaging (SI) system. We investigated the normal variance between the samples and utilized descriptive and machine learning analysis on the image stack hypercubes.

Results

Reflectance between intact and peeled arteries in lower-wavelength domains between 400 and 576 nm was significantly different (p < 0.05). Proximal FN could be differentiated from distal FN in a higher range, 490–720 nm (p < 0.001). ICA with intact tunica differed from proximal FN nearly thorough the VIS range, 412–592 nm (p < 0.001) and 664–720 nm (p < 0.05) as did its distal counterpart, 422–720 nm (p < 0.001). The availed U-Net algorithm classified 90.93% of the pixels correctly in comparison to tissue margins delineated by a specialist.

Conclusion

Selective NBI represents a promising method for assisting tissue identification and computational segmentation of surgical microanatomy. Further multidisciplinary research is required for its clinical applications and intraoperative integration.

Non-Destructive Early Detection and Quantitative Severity Stage Classification of Tomato Chlorosis Virus (ToCV) Infection in Young Tomato Plants Using Vis–NIR Spectroscopy

Tomato chlorosis virus (ToCV) is a serious, emerging tomato pathogen that has a significant impact on the quality and quantity of tomato production worldwide. Detecting ToCV via means of spectral measurements in an early pre-symptomatic stage offers an alternative to the existing laboratory methods, leading to better disease management in the field. In this study, leaf spectra from healthy and diseased leaves were measured with a spectrometer. The diseased leaves were subjected to RT-qPCR for the detection and quantification of the titer of ToCV. Neighborhood component analysis (NCA) algorithm was employed for the feature selection of the effective wavelengths and the most important vegetation indices out of the 24 that were tested. Two machine learning methods, namely XY-fusion network (XY-F) and multilayer perceptron with automated relevance determination (MLP–ARD), were employed for the estimation of the disease existence and viral load in the tomato leaves. The results showed that before outlier elimination, the MLP–ARD classifier generally outperformed the XY-F network with an overall accuracy of 92.1% against 88.3% for the XY-F. Outlier elimination contributed to the performance of the classifiers as the overall accuracy for both XY-F and MLP–ARD reached 100%.

Callico G. Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support

Skin cancer is one of the most common forms of cancer worldwide and its early detection its key to achieve an effective treatment of the lesion. Commonly, skin cancer diagnosis is based on dermatologist expertise and pathological assessment of biopsies. Although there are diagnosis aid systems based on morphological processing algorithms using conventional imaging, currently, these systems have reached their limit and are not able to outperform dermatologists. In this sense, hyperspectral (HS) imaging (HSI) arises as a new non-invasive technology able to facilitate the detection and classification of pigmented skin lesions (PSLs), employing the spectral properties of the captured sample within and beyond the human eye capabilities. This paper presents a research carried out to develop a dermatological acquisition system based on HSI, employing 125 spectral bands captured between 450 and 950 nm. A database composed of 76 HS PSL images from 61 patients was obtained and labeled and classified into benign and malignant classes. A processing framework is proposed for the automatic identification and classification of the PSL based on a combination of unsupervised and supervised algorithms. Sensitivity and specificity results of 87.5% and 100%, respectively, were obtained in the discrimination of malignant and benign PSLs. This preliminary study demonstrates, as a proof-of-concept, the potential of HSI technology to assist dermatologists in the discrimination of benign and malignant PSLs during clinical routine practice using a real-time and non-invasive hand-held device.

Classification of hyperspectral endocrine tissue images using support vector machines

BACKGROUND

Thyroidectomy is one of the most commonly performed surgical procedures. The region of the neck has a very complex structural organization. It would be beneficial to introduce a tool that can assist the surgeon in tissue discrimination during the procedure. One such solution is the noninvasive and contactless technique, called hyperspectral imaging (HSI).

METHODS

To interpret the HSI data, we implemented a supervised classification method to automatically discriminate the parathyroid, the thyroid, and the recurrent laryngeal nerve from surrounding tissue(muscle, skin) and materials (instruments, gauze). A leave-one-patient-out cross-validation was performed.

RESULTS

The best performance was obtained using support vector machine (SVM) with a classification and visualization in less than 1.4 seconds. A mean patient accuracy of 68% ± 23% was obtained for all tissues and material types.

CONCLUSIONS

The proposed method showed promising results and have to be confirmed on a larger cohort of patient data.

Repurposing Molecular Imaging and Sensing for Cancer Image-Guided Surgery

Gone are the days when medical imaging was used primarily to visualize anatomic structures. The emergence of molecular imaging (MI), championed by radiolabeled ¹⁸F-FDG PET, has expanded the information content derived from imaging to include pathophysiologic and molecular processes. Cancer imaging, in particular, has leveraged advances in MI agents and technology to improve the accuracy of tumor detection, interrogate tumor heterogeneity, monitor treatment response, focus surgical resection, and enable image-guided biopsy. Surgeons are actively latching on to the incredible opportunities provided by medical imaging for preoperative planning, intraoperative guidance, and postoperative monitoring. From label-free techniques to enabling cancer-selective imaging agents, image-guided surgery provides surgical oncologists and interventional radiologists both macroscopic and microscopic views of cancer in the operating room. This review highlights the current state of MI and sensing approaches available for surgical guidance. Salient features of nuclear, optical, and multimodal approaches will be discussed, including their strengths, limitations, and clinical applications. To address the increasing complexity and diversity of methods available today, this review provides a framework to identify a contrast mechanism, suitable modality, and device. Emerging low-cost, portable, and user-friendly imaging systems make the case for adopting some of these technologies as the global standard of care in surgical practice.

Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches

Biomedical imaging Is an important source of information in cancer research. Characterizations of cancer morphology at onset, progression, and in response to treatment provide complementary information to that gleaned from genomics and clinical data. Accurate extraction and classification of both visual and latent image features Is an increasingly complex challenge due to the increased complexity and resolution of biomedical image data. In this paper, we present four deep learning-based image analysis methods from the Computational Precision Medicine (CPM) satellite event of the 21st International Medical Image Computing and Computer Assisted Intervention (MICCAI 2018) conference. One method Is a segmentation method designed to segment nuclei in whole slide tissue images (WSIs) of adult diffuse glioma cases. It achieved a Dice similarity coefficient of 0.868 with the CPM challenge datasets. Three methods are classification methods developed to categorize adult diffuse glioma cases into oligodendroglioma and astrocytoma classes using radiographic and histologic image data. These methods achieved accuracy values of 0.75, 0.80, and 0.90, measured as the ratio of the number of correct classifications to the number of total cases, with the challenge datasets. The evaluations of the four methods indicate that (1) carefully constructed deep learning algorithms are able to produce high accuracy in the analysis of biomedical image data and (2) the combination of radiographic with histologic image information improves classification performance.

Applications of hyperspectral imaging in the detection and diagnosis of solid tumors

Hyperspectral imaging (HSI) is an emerging new technology in solid tumor diagnosis and detection. It incorporates traditional imaging and spectroscopy together to obtain both spatial and spectral information from tissues simultaneously in a non-invasive manner. This imaging modality is based on the principle that different tissues inherit different spectral reflectance responses that present as unique spectral fingerprints. HSI captures those composition-specific fingerprints to identify cancerous and normal tissues. It becomes a promising tool for performing tumor diagnosis and detection from the label-free histopathological examination to real-time intraoperative assistance. This review introduces the basic principles of HSI and summarizes its methodology and recent advances in solid tumor detection. In particular, the advantages of HSI applied to solid tumors are highlighted to show its potential for clinical use.

Extended Blind End-member and Abundance Extraction for Biomedical Imaging Applications

In some applications of biomedical imaging, a linear mixture model can represent the constitutive elements (end-members) and their contributions (abundances) per pixel of the image. In this work, the extended blind end-member and abundance extraction (EBEAE) methodology is mathematically formulated to address the blind linear unmixing (BLU) problem subject to positivity constraints in optical measurements. The EBEAE algorithm is based on a constrained quadratic optimization and an alternated least-squares strategy to jointly estimate end-members and their abundances. In our proposal, a local approach is used to estimate the abundances of each end-member by maximizing their entropy, and a global technique is adopted to iteratively identify the end-members by reducing the similarity among them. All the cost functions are normalized, and four initialization approaches are suggested for the end-members matrix. Synthetic datasets are used first for the EBEAE validation at different noise types and levels, and its performance is compared to state-of-the-art algorithms in BLU. In a second stage, three experimental biomedical imaging applications are addressed with EBEAE: m-FLIM for chemometric analysis in oral cavity samples, OCT for macrophages identification in post-mortem artery samples, and hyper-spectral images for in-vivo brain tissue classification and tumor identification. In our evaluations, EBEAE was able to provide a quantitative analysis of the samples with none or minimal a priori information.