Hyperspectral Imaging for Clinical Applications

Computer simulation and modeling of glow discharge optical emission coded aperture elemental mapping

BACKGROUND

Elemental mapping (EM) yields necessary insights into mechanisms of interest in solid samples across multiple disciplines. There are several EM techniques available but long acquisition time is a common limitation. Glow discharge optical emission spectroscopy (GDOES) allows direct quantitative multi-EM at very high throughput (∼10 s s) when coupled to traditional hyperspectral imaging (HSI) techniques. However, GDOES consumes the sample via sputtering, such that traditional HSI sequential scanning requirements lead to loss of information/resolution, which is compounded for multi-EM and limits nanomaterials analysis. Thus, there is a need for faster HSI to enable GDOES multi-EM of nanoscale materials.

RESULTS

Here, a new technique is described, Glow discharge Optical emission Coded Aperture elemental Mapping (GOCAM), that takes advantage of compressive coded aperture spectral imaging to enable multi-EM in a single camera exposure. In this first phase of development, computer model simulations were implemented to study the effects of coded aperture parameters on data fidelity, which showed the best fidelity is achieved at smaller mask element sizes and transmittance of 60 %. In addition, SeSCIGPU demonstrated the best fidelity performance compared to several compressed sensing reconstruction algorithms, including TwIST, GAP-TV, SeSCICPU, and ADMM-TV, as evaluated by studying the effects of varying the corresponding hyperparameters.

SIGNIFICANCE

This study shows GOCAM's feasibility and provides a starting point for the second phase hardware development currently underway. GOCAM's potential to allow multi-EM from solid surfaces in a fraction of a second will be particularly enabling for nanostructured materials characterization.

Low-cost and convenient screening of disease using analysis of physical measurements and recordings

In recent years, there has been substantial work in low-cost medical diagnostics based on the physical manifestations of disease. This is due to advancements in data analysis techniques and classification algorithms and the increased availability of computing power through smart devices. Smartphones and their ability to interface with simple sensors such as inertial measurement units (IMUs), microphones, piezoelectric sensors, etc., or with convenient attachments such as lenses have revolutionized the ability collect medically relevant data easily. Even if the data has relatively low resolution or signal to noise ratio, newer algorithms have made it possible to identify disease with this data. Many low-cost diagnostic tools have been created in medical fields spanning from neurology to dermatology to obstetrics. These tools are particularly useful in low-resource areas where access to expensive diagnostic equipment may not be possible. The ultimate goal would be the creation of a "diagnostic toolkit" consisting of a smartphone and a set of sensors and attachments that can be used to screen for a wide set of diseases in a community healthcare setting. However, there are a few concerns that still need to be overcome in low-cost diagnostics: lack of incentives to bring these devices to market, algorithmic bias, "black box" nature of the algorithms, and data storage/transfer concerns.

Advancing laser ablation assessment in hyperspectral imaging through machine learning

Hyperspectral imaging (HSI) is gaining increasing relevance in medicine, with an innovative application being the intraoperative assessment of the outcome of laser ablation treatment used for minimally invasive tumor removal. However, the high dimensionality and complexity of HSI data create a need for end-to-end image processing workflows specifically tailored to handle these data. This study addresses this challenge by proposing a multi-stage workflow for the analysis of hyperspectral data and allows investigating the performance of different components and modalities for ablation detection and segmentation. To address dimensionality reduction, we integrated principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) to capture dominant variations and reveal intricate structures, respectively. Additionally, we employed the Faster Region-based Convolutional Neural Network (Faster R-CNN) to accurately localize ablation areas. The two-stage detection process of Faster R-CNN, along with the choice of dimensionality reduction technique and data modality, significantly influenced the performance in detecting ablation areas. The evaluation of the ablation detection on an independent test set demonstrated a mean average precision of approximately 0.74, which validates the generalization ability of the models. In the segmentation component, the Mean Shift algorithm showed high quality segmentation without manual cluster definition. Our results prove that the integration of PCA, t-SNE, and Faster R-CNN enables improved interpretation of hyperspectral data, leading to the development of reliable ablation detection and segmentation systems.

A Microscope Setup and Methodology for Capturing Hyperspectral and RGB Histopathological Imaging Databases

The digitization of pathology departments in hospitals around the world is now a reality. The current commercial solutions applied to digitize histopathological samples consist of a robotic microscope with an RGB-type camera attached to it. This technology is very limited in terms of information captured, as it only works with three spectral bands of the visible electromagnetic spectrum. Therefore, we present an automated system that combines RGB and hyperspectral technology. Throughout this work, the hardware of the system and its components are described along with the developed software and a working methodology to ensure the correct capture of histopathological samples. The software is integrated by the controller of the microscope, which features an autofocus functionality, whole slide scanning with a stitching algorithm, and hyperspectral scanning functionality. As a reference, the time to capture and process a complete sample with 20 regions of high biological interest using the proposed method is estimated at a maximum of 79 min, reducing the time required by a manual operator by at least three times. Both hardware and software can be easily adapted to other systems that might benefit from the advantages of hyperspectral technology.

Advancing DIEP Flap Monitoring with Optical Imaging Techniques: A Narrative Review

OBJECTIVES

This review aims to explore recent advancements in optical imaging techniques for monitoring the viability of Deep Inferior Epigastric Perforator (DIEP) flap reconstruction. The objectives include highlighting the principles, applications, and clinical utility of optical imaging modalities such as near-infrared spectroscopy (NIRS), indocyanine green (ICG) fluorescence angiography, laser speckle contrast imaging (LSCI), hyperspectral imaging (HSI), dynamic infrared thermography (DIRT), and short-wave infrared thermography (SWIR) in assessing tissue perfusion and oxygenation. Additionally, this review aims to discuss the potential of these techniques in enhancing surgical outcomes by enabling timely intervention in cases of compromised flap perfusion.

MATERIALS AND METHODS

A comprehensive literature review was conducted to identify studies focusing on optical imaging techniques for monitoring DIEP flap viability. We searched PubMed, MEDLINE, and relevant databases, including Google Scholar, Web of Science, Scopus, PsycINFO, IEEE Xplore, and ProQuest Dissertations & Theses, among others, using specific keywords related to optical imaging, DIEP flap reconstruction, tissue perfusion, and surgical outcomes. This extensive search ensured we gathered comprehensive data for our analysis. Articles discussing the principles, applications, and clinical use of NIRS, ICG fluorescence angiography, LSCI, HSI, DIRT, and SWIR in DIEP flap monitoring were selected for inclusion. Data regarding the techniques' effectiveness, advantages, limitations, and potential impact on surgical decision-making were extracted and synthesized.

RESULTS

Optical imaging modalities, including NIRS, ICG fluorescence angiography, LSCI, HSI, DIRT, and SWIR offer a non- or minimal-invasive, real-time assessment of tissue perfusion and oxygenation in DIEP flap reconstruction. These techniques provide objective and quantitative data, enabling surgeons to monitor flap viability accurately. Studies have demonstrated the effectiveness of optical imaging in detecting compromised perfusion and facilitating timely intervention, thereby reducing the risk of flap complications such as partial or total loss. Furthermore, optical imaging modalities have shown promise in improving surgical outcomes by guiding intraoperative decision-making and optimizing patient care.

CONCLUSIONS

Recent advancements in optical imaging techniques present valuable tools for monitoring the viability of DIEP flap reconstruction. NIRS, ICG fluorescence angiography, LSCI, HSI, DIRT, and SWIR offer a non- or minimal-invasive, real-time assessment of tissue perfusion and oxygenation, enabling accurate evaluation of flap viability. These modalities have the potential to enhance surgical outcomes by facilitating timely intervention in cases of compromised perfusion, thereby reducing the risk of flap complications. Incorporating optical imaging into clinical practice can provide surgeons with objective and quantitative data, assisting in informed decision-making for optimal patient care in DIEP flap reconstruction surgeries.

Evaluation of Spectrum-Aided Visual Enhancer (SAVE) in Esophageal Cancer Detection Using YOLO Frameworks

The early detection of esophageal cancer presents a substantial difficulty, which contributes to its status as a primary cause of cancer-related fatalities. This study used You Only Look Once (YOLO) frameworks, specifically YOLOv5 and YOLOv8, to predict and detect early-stage EC by using a dataset sourced from the Division of Gastroenterology and Hepatology, Ditmanson Medical Foundation, Chia-Yi Christian Hospital. The dataset comprised 2741 white-light images (WLI) and 2741 hyperspectral narrowband images (HSI-NBI). They were divided into 60% training, 20% validation, and 20% test sets to facilitate robust detection. The images were produced using a conversion method called the spectrum-aided vision enhancer (SAVE). This algorithm can transform a WLI into an NBI without requiring a spectrometer or spectral head. The main goal was to identify dysplasia and squamous cell carcinoma (SCC). The model's performance was evaluated using five essential metrics: precision, recall, F1-score, mAP, and the confusion matrix. The experimental results demonstrated that the HSI model exhibited improved learning capabilities for SCC characteristics compared with the original RGB images. Within the YOLO framework, YOLOv5 outperformed YOLOv8, indicating that YOLOv5's design possessed superior feature-learning skills. The YOLOv5 model, when used in conjunction with HSI-NBI, demonstrated the best performance. It achieved a precision rate of 85.1% (CI95: 83.2-87.0%, p < 0.01) in diagnosing SCC and an F1-score of 52.5% (CI95: 50.1-54.9%, p < 0.01) in detecting dysplasia. The results of these figures were much better than those of YOLOv8. YOLOv8 achieved a precision rate of 81.7% (CI95: 79.6-83.8%, p < 0.01) and an F1-score of 49.4% (CI95: 47.0-51.8%, p < 0.05). The YOLOv5 model with HSI demonstrated greater performance than other models in multiple scenarios. This difference was statistically significant, suggesting that the YOLOv5 model with HSI significantly improved detection capabilities.

Hyperspectral retinal imaging biomarkers of ocular and systemic diseases

Hyperspectral imaging is a frontier in the field of medical imaging technology. It enables the simultaneous collection of spectroscopic and spatial data. Structural and physiological information encoded in these data can be used to identify and localise typically elusive biomarkers. Studies of retinal hyperspectral imaging have provided novel insights into disease pathophysiology and new ways of non-invasive diagnosis and monitoring of retinal and systemic diseases. This review provides a concise overview of recent advances in retinal hyperspectral imaging.

Development of a large volume line scanning, high spectral range and resolution 3D hyperspectral photoluminescence imaging microscope for diamond and other high refractive index materials

Hyperspectral photoluminescence (PL) imaging is a powerful technique that can be used to understand the spatial distribution of emitting species in many materials. Volumetric hyperspectral imaging of weakly emitting color centers often necessitates considerable data collection times when using commercial systems. We report the development of a line-scanning hyperspectral imaging microscope capable of measuring the luminescence emission spectra for diamond volumes up to 2.20 × 30.00 × 6.30 mm with a high lateral spatial resolution of 1-3 µm. In an single X-λ measurement, spectra covering a 711 nm range, in a band from 400-1100 nm, with a spectral resolution up to 0.25 nm can be acquired. Data sets can be acquired with 723 (X) × 643 (Y) × 1172 (λ) pixels at a rate of 6 minutes/planar image slice, allowing for volumetric hyperspectral imaging with high sampling. This instrument demonstrates the ability to detect emission from several different color centers in diamond both at the surface and internally, providing a non-destructive method to probe their 3D spatial distribution, and is currently not achievable with any other commonly used system or technique.

Mapping cutaneous field carcinogenesis of nonmelanoma skin cancer using mesoscopic imaging of pro-inflammation cues

Nonmelanoma skin cancers remain the most widely diagnosed types of cancers globally. Thus, for optimal patient management, it has become imperative that we focus our efforts on the detection and monitoring of cutaneous field carcinogenesis. The concept of field cancerization (or field carcinogenesis), introduced by Slaughter in 1953 in the context of oral cancer, suggests that invasive cancer may emerge from a molecularly and genetically altered field affecting a substantial area of underlying tissue including the skin. A carcinogenic field alteration, present in precancerous tissue over a relatively large area, is not easily detected by routine visualization. Conventional dermoscopy and microscopy imaging are often limited in assessing the entire carcinogenic landscape. Recent efforts have suggested the use of noninvasive mesoscopic (between microscopic and macroscopic) optical imaging methods that can detect chronic inflammatory features to identify pre-cancerous and cancerous angiogenic changes in tissue microenvironments. This concise review covers major types of mesoscopic optical imaging modalities capable of assessing pro-inflammatory cues by quantifying blood haemoglobin parameters and hemodynamics. Importantly, these imaging modalities demonstrate the ability to detect angiogenesis and inflammation associated with actinically damaged skin. Representative experimental preclinical and human clinical studies using these imaging methods provide biological and clinical relevance to cutaneous field carcinogenesis in altered tissue microenvironments in the apparently normal epidermis and dermis. Overall, mesoscopic optical imaging modalities assessing chronic inflammatory hyperemia can enhance the understanding of cutaneous field carcinogenesis, offer a window of intervention and monitoring for actinic keratoses and nonmelanoma skin cancers and maximise currently available treatment options.

Hyperspectral push-broom imager using a volume Bragg grating as an angular filter

A hyperspectral push-broom imager has been designed, constructed, and tested. The narrow angular selectivity of a weakly index modulated volume Bragg grating is utilized to replace the objective lens, slit, and collimating lens of a conventional slit-based hyperspectral push-broom imager. The imager comprises a dispersion grating, an angular filter grating, a focusing lens, and an image sensor. The imager has a field of view (FOV) of 17 degrees in the spatial direction, a spectral range from 400 nm to 900 nm, and a spectral resolution of 2.1 nm. The acquired hyperspectral data cubes are presented, and the influence of wavelength-dependent incident angle errors is analyzed.

Hyperspectral imaging-based erythema classification in atopic dermatitis

BACKGROUND/PURPOSE

Among the characteristics that appear in the epidermis of the skin, erythema is primarily evaluated through qualitative scales, such as visual assessment (VA). However, VA is not ideal because it relies on the experience and skill of dermatologists. In this study, we propose a new evaluation method based on hyperspectral imaging (HSI) to improve the accuracy of erythema diagnosis in clinical settings and investigate the applicability of HSI to skin evaluation.

METHODS

For this study, 23 subjects diagnosed with atopic dermatitis were recruited. The inside of the right arm is selected as the target area and photographed using a hyperspectral camera (HS). Subsequently, based on the erythema severity visually assessed by a dermatologist, the severity classification performance of the RGB and HS images is compared.

RESULTS

Erythema severity is classified as high when using (i) all reflectances of the entire HSI band and (ii) a combination of color features (R of RGB, a* of CIEL*a*b*) and five selected bands through band selection. However, as the number of features increases, the amount of calculation increases and becomes inefficient; therefore, (ii), which uses only seven features, is considered to perform classification more efficiently than (i), which uses 150 features.

CONCLUSION

In conclusion, we demonstrate that HSI can be applied to erythema severity classification, which can further increase the accuracy and reliability of diagnosis when combined with other features observed in erythema. Additionally, the scope of its application can be expanded to various studies related to skin pigmentation.

Patient-Derived Microphysiological Systems for Precision Medicine

Patient-derived microphysiological systems (P-MPS) have emerged as powerful tools in precision medicine that provide valuable insight into individual patient characteristics. This review discusses the development of P-MPS as an integration of patient-derived samples, including patient-derived cells, organoids, and induced pluripotent stem cells, into well-defined MPSs. Emphasizing the necessity of P-MPS development, its significance as a nonclinical assessment approach that bridges the gap between traditional in vitro models and clinical outcomes is highlighted. Additionally, guidance is provided for engineering approaches to develop microfluidic devices and high-content analysis for P-MPSs, enabling high biological relevance and high-throughput experimentation. The practical implications of the P-MPS are further examined by exploring the clinically relevant outcomes obtained from various types of patient-derived samples. The construction and analysis of these diverse samples within the P-MPS have resulted in physiologically relevant data, paving the way for the development of personalized treatment strategies. This study describes the significance of the P-MPS in precision medicine, as well as its unique capacity to offer valuable insights into individual patient characteristics.

Endoscopic Hyperspectral Imaging System to Discriminate Tissue Characteristics in Tissue Phantom and Orthotopic Mouse Pancreatic Tumor Model

In this study, we developed an endoscopic hyperspectral imaging (eHSI) system and evaluated its performance in analyzing tissues within tissue phantoms and orthotopic mouse pancreatic tumor models. Our custom-built eHSI system incorporated a liquid crystal tunable filter. To assess its tissue discrimination capabilities, we acquired images of tissue phantoms, distinguishing between fat and muscle regions. The system underwent supervised training using labeled samples, and this classification model was then applied to other tissue phantom images for evaluation. In the tissue phantom experiment, the eHSI effectively differentiated muscle from fat and background tissues. The precision scores regarding fat tissue classification were 98.3% for the support vector machine, 97.7% for the neural network, and 96.0% with a light gradient-boosting machine algorithm, respectively. Furthermore, we applied the eHSI system to identify tumors within an orthotopic mouse pancreatic tumor model. The F-score of each pancreatic tumor-bearing model reached 73.1% for the KPC tumor model and 63.1% for the Pan02 tumor models. The refined imaging conditions and optimization of the fine-tuning of classification algorithms enhance the versatility and diagnostic efficacy of eHSI in biomedical applications.

Machine Learning Method and Hyperspectral Imaging for Precise Determination of Glucose and Silicon Levels

This article introduces an algorithm for detecting glucose and silicon levels in solution. The research focuses on addressing the critical need for accurate and efficient glucose monitoring, particularly in the context of diabetic management. Understanding and monitoring silicon levels in the body is crucial due to its significant role in various physiological processes. Silicon, while often overshadowed by other minerals, plays a vital role in bone health, collagen formation, and connective tissue integrity. Moreover, recent research suggests its potential involvement in neurological health and the prevention of certain degenerative diseases. Investigating silicon levels becomes essential for a comprehensive understanding of its impact on overall health and well-being and paves the way for targeted interventions and personalized healthcare strategies. The approach presented in this paper is based on the integration of hyperspectral data and artificial intelligence techniques. The algorithm investigates the effectiveness of two distinct models utilizing SVMR and a perceptron independently. SVMR is employed to establish a robust regression model that maps input features to continuous glucose and silicon values. The study outlines the methodology, including feature selection, model training, and evaluation metrics. Experimental results demonstrate the algorithm's effectiveness at accurately predicting glucose and silicon concentrations and showcases its potential for real-world application in continuous glucose and silicon monitoring systems.

Fluorescence spectroscopic profiling of urine samples for predicting kidney transplant rejection

Rejection is the primary factor affecting the functionality of a kidney post-transplant, where its prompt prediction of risk significantly influences therapeutic strategies and clinical outcomes. Current graft health assessment methods, including serum creatinine measurements and transplant kidney puncture biopsies, possess considerable limitations. In contrast, urine serves as a direct indicator of the graft's degenerative stage and provides a more accurate measure than peripheral blood analysis, given its non-invasive collection of kidney-specific metabolite. This research entailed collecting fluorescent fingerprint data from 120 urine samples of post-renal transplant patients using hyperspectral imaging, followed by the development of a learning model to detect various forms of immunological rejection. The model successfully identified multiple rejection types with an average diagnostic accuracy of 95.56 %.Beyond proposing an innovative approach for predicting the risk of complications post-kidney transplantation, this study heralds the potential introduction of a non-invasive, rapid, and accurate supplementary method for risk assessment in clinical practice.

Rapid Determination of Positive-Negative Bacterial Infection Based on Micro-Hyperspectral Technology

To meet the demand for rapid bacterial detection in clinical practice, this study proposed a joint determination model based on spectral database matching combined with a deep learning model for the determination of positive-negative bacterial infection in directly smeared urine samples. Based on a dataset of 8124 urine samples, a standard hyperspectral database of common bacteria and impurities was established. This database, combined with an automated single-target extraction, was used to perform spectral matching for single bacterial targets in directly smeared data. To address the multi-scale features and the need for the rapid analysis of directly smeared data, a multi-scale buffered convolutional neural network, MBNet, was introduced, which included three convolutional combination units and four buffer units to extract the spectral features of directly smeared data from different dimensions. The focus was on studying the differences in spectral features between positive and negative bacterial infection, as well as the temporal correlation between positive-negative determination and short-term cultivation. The experimental results demonstrate that the joint determination model achieved an accuracy of 97.29%, a Positive Predictive Value (PPV) of 97.17%, and a Negative Predictive Value (NPV) of 97.60% in the directly smeared urine dataset. This result outperformed the single MBNet model, indicating the effectiveness of the multi-scale buffered architecture for global and large-scale features of directly smeared data, as well as the high sensitivity of spectral database matching for single bacterial targets. The rapid determination solution of the whole process, which combines directly smeared sample preparation, joint determination model, and software analysis integration, can provide a preliminary report of bacterial infection within 10 min, and it is expected to become a powerful supplement to the existing technologies of rapid bacterial detection.

Silicon-Based Avalanche Photodiodes: Advancements and Applications in Medical Imaging

Avalanche photodiodes have emerged as a promising technology with significant potential for various medical applications. This article presents an overview of the advancements and applications of avalanche photodiodes in the field of medical imaging. Avalanche photodiodes offer distinct advantages over traditional photodetectors, including a higher responsivity, faster response times, and superior signal-to-noise ratios. These characteristics make avalanche photodiodes particularly suitable for medical-imaging modalities that require a high detection efficiency, excellent timing resolution, and enhanced spatial resolution. This review explores the key features of avalanche photodiodes, discusses their applications in medical-imaging techniques, and highlights the challenges and future prospects in utilizing avalanche photodiodes for medical purposes. Special attention is paid to the recent progress in silicon-compatible avalanche photodiodes.

The Identification of Fritillaria Species Using Hyperspectral Imaging with Enhanced One-Dimensional Convolutional Neural Networks via Attention Mechanism

Combining deep learning and hyperspectral imaging (HSI) has proven to be an effective approach in the quality control of medicinal and edible plants. Nonetheless, hyperspectral data contains redundant information and highly correlated characteristic bands, which can adversely impact sample identification. To address this issue, we proposed an enhanced one-dimensional convolutional neural network (1DCNN) with an attention mechanism. Given an intermediate feature map, two attention modules are constructed along two separate dimensions, channel and spectral, and then combined to enhance relevant features and to suppress irrelevant ones. Validated by Fritillaria datasets, the results demonstrate that an attention-enhanced 1DCNN model outperforms several machine learning algorithms and shows consistent improvements over a vanilla 1DCNN. Notably under VNIR and SWIR lenses, the model obtained 98.97% and 99.35% for binary classification between Fritillariae Cirrhosae Bulbus (FCB) and other non-FCB species, respectively. Additionally, it still achieved an extraordinary accuracy of 97.64% and 98.39% for eight-category classification among Fritillaria species. This study demonstrated the application of HSI with artificial intelligence can serve as a reliable, efficient, and non-destructive quality control method for authenticating Fritillaria species. Moreover, our findings also illustrated the great potential of the attention mechanism in enhancing the performance of the vanilla 1DCNN method, providing reference for other HSI-related quality controls of plants with medicinal and edible uses.

Enteroscopy versus Video Capsule Endoscopy for Automatic Diagnosis of Small Bowel Disorders-A Comparative Analysis of Artificial Intelligence Applications

BACKGROUND

Small bowel disorders present a diagnostic challenge due to the limited accessibility of the small intestine. Accurate diagnosis is made with the aid of specific procedures, like capsule endoscopy or double-ballon enteroscopy, but they are not usually solicited and not widely accessible. This study aims to assess and compare the diagnostic effectiveness of enteroscopy and video capsule endoscopy (VCE) when combined with artificial intelligence (AI) algorithms for the automatic detection of small bowel diseases.

MATERIALS AND METHODS

We performed an extensive literature search for relevant studies about AI applications capable of identifying small bowel disorders using enteroscopy and VCE, published between 2012 and 2023, employing PubMed, Cochrane Library, Google Scholar, Embase, Scopus, and ClinicalTrials.gov databases.

RESULTS

Our investigation discovered a total of 27 publications, out of which 21 studies assessed the application of VCE, while the remaining 6 articles analyzed the enteroscopy procedure. The included studies portrayed that both investigations, enhanced by AI, exhibited a high level of diagnostic accuracy. Enteroscopy demonstrated superior diagnostic capability, providing precise identification of small bowel pathologies with the added advantage of enabling immediate therapeutic intervention. The choice between these modalities should be guided by clinical context, patient preference, and resource availability. Studies with larger sample sizes and prospective designs are warranted to validate these results and optimize the integration of AI in small bowel diagnostics.

CONCLUSIONS

The current analysis demonstrates that both enteroscopy and VCE with AI augmentation exhibit comparable diagnostic performance for the automatic detection of small bowel disorders.

Pediatric Brain Tissue Segmentation Using a Snapshot Hyperspectral Imaging (sHSI) Camera and Machine Learning Classifier

Pediatric brain tumors are the second most common type of cancer, accounting for one in four childhood cancer types. Brain tumor resection surgery remains the most common treatment option for brain cancer. While assessing tumor margins intraoperatively, surgeons must send tissue samples for biopsy, which can be time-consuming and not always accurate or helpful. Snapshot hyperspectral imaging (sHSI) cameras can capture scenes beyond the human visual spectrum and provide real-time guidance where we aim to segment healthy brain tissues from lesions on pediatric patients undergoing brain tumor resection. With the institutional research board approval, Pro00011028, 139 red-green-blue (RGB), 279 visible, and 85 infrared sHSI data were collected from four subjects with the system integrated into an operating microscope. A random forest classifier was used for data analysis. The RGB, infrared sHSI, and visible sHSI models achieved average intersection of unions (IoUs) of 0.76, 0.59, and 0.57, respectively, while the tumor segmentation achieved a specificity of 0.996, followed by the infrared HSI and visible HSI models at 0.93 and 0.91, respectively. Despite the small dataset considering pediatric cases, our research leveraged sHSI technology and successfully segmented healthy brain tissues from lesions with a high specificity during pediatric brain tumor resection procedures.

Alternative intraoperative optical imaging modalities for fluorescence angiography in gastrointestinal surgery: spectral imaging and imaging photoplethysmography

INTRODUCTION

Intraoperative near-infrared fluorescence angiography with indocyanine green (ICG-FA) is a well-established modality in gastrointestinal surgery. Its main drawback is the application of a fluorescent agent with possible side effects for patients. The goal of this review paper is the presentation of alternative, non-invasive optical imaging methods and their comparison with ICG-FA.

MATERIAL AND METHODS

The principles of ICG-FA, spectral imaging, imaging photoplethysmography (iPPG), and their applications in gastrointestinal surgery are described based on selected published works.

RESULTS

The main applications of the three modalities are the evaluation of tissue perfusion, the identification of risk structures, and tissue segmentation or classification. While the ICG-FA images are mainly evaluated visually, leading to subjective interpretations, quantitative physiological parameters and tissue segmentation are provided in spectral imaging and iPPG. The combination of ICG-FA and spectral imaging is a promising method.

CONCLUSIONS

Non-invasive spectral imaging and iPPG have shown promising results in gastrointestinal surgery. They can overcome the main drawbacks of ICG-FA, i.e. the use of contrast agents, the lack of quantitative analysis, repeatability, and a difficult standardization of the acquisition. Further technical improvements and clinical evaluations are necessary to establish them in daily clinical routine.

Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.

A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma

BACKGROUND AND AIMS

Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)-based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS-FNA cytology specimens.

METHODS

HSI images were captured of pancreatic EUS-FNA cytological specimens from benign pancreatic tissues (n = 33) and PDAC (n = 39) prepared using a liquid-based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization (AGF-Visualization) was used to visualize the regions of important classification features identified by the model.

RESULTS

A total of 1913 HSI images were obtained. Our ResNet18-SimSiam model achieved an accuracy of 0.9204, sensitivity of 0.9310 and specificity of 0.9123 (area under the curve of 0.9625) when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF-Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei.

CONCLUSIONS

An HSI-based model was developed to diagnose cytological PDAC specimens obtained using EUS-guided sampling. Under the supervision of experienced cytopathologists, we performed multi-staged consecutive in-depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC.

Deep ultraviolet high-resolution microscopic hyperspectral imager and its biological tissue detection

Ultraviolet (UV) hyperspectral imaging technology is commonly used in the field of atmospheric remote sensing. In recent years, some in-laboratory research has been carried out for substance detection and identification. In this paper, UV hyperspectral imaging technology is introduced into microscopy to better utilize the obvious absorption characteristics of components, such as proteins and nucleic acids in biological tissues in the ultraviolet band. A deep UV microscopic hyperspectral imager based on the Offner structure with F # 2.5, low spectral keystone and smile is designed and developed. A 0.68 numerical aperture microscope objective is designed. The spectral range of the system is from 200 nm to 430 nm; the spectral resolution is better than 0.5 nm; and the spatial resolution is better than 1.3 µm. The K562 cells can be distinguished by transmission spectrum of nucleus. The UV microscopic hyperspectral image of the unstained mouse liver slices showed similar results to the microscopic image after hematoxylin and eosin staining, which could help to simplify the pathological examination process. Both results show a great performance in spatial and spectral detecting capabilities of our instrument, which has the potential for biomedical research and diagnosis.

Signal to noise ratio quantifies the contribution of spectral channels to classification of human head and neck tissues ex vivo using deep learning and multispectral imaging

SIGNIFICANCE

Accurate identification of tissues is critical for performing safe surgery. Combining multispectral imaging (MSI) with deep learning is a promising approach to increasing tissue discrimination and classification. Evaluating the contributions of spectral channels to tissue discrimination is important for improving MSI systems.

AIM

Develop a metric to quantify the contributions of individual spectral channels to tissue classification in MSI.

APPROACH

MSI was integrated into a digital operating microscope with three sensors and seven illuminants. Two convolutional neural network (CNN) models were trained to classify 11 head and neck tissue types using white light (RGB) or MSI images. The signal to noise ratio (SNR) of spectral channels was compared with the impact of channels on tissue classification performance as determined using CNN visualization methods.

RESULTS

Overall tissue classification accuracy was higher with use of MSI images compared with RGB images, both for classification of all 11 tissue types and binary classification of nerve and parotid ( p < 0.001 ). Removing spectral channels with SNR > 20 reduced tissue classification accuracy.

CONCLUSIONS

The spectral channel SNR is a useful metric for both understanding CNN tissue classification and quantifying the contributions of different spectral channels in an MSI system.

Staging of Skin Cancer Based on Hyperspectral Microscopic Imaging and Machine Learning

Skin cancer, a common type of cancer, is generally divided into basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant melanoma (MM). The incidence of skin cancer has continued to increase worldwide in recent years. Early detection can greatly reduce its morbidity and mortality. Hyperspectral microscopic imaging (HMI) technology can be used as a powerful tool for skin cancer diagnosis by reflecting the changes in the physical structure and microenvironment of the sample through the differences in the HMI data cube. Based on spectral data, this work studied the staging identification of SCC and the influence of the selected region of interest (ROI) on the staging results. In the SCC staging identification process, the optimal result corresponded to the standard normal variate transformation (SNV) for spectra preprocessing, the partial least squares (PLS) for dimensionality reduction, the hold-out method for dataset partition and the random forest (RF) model for staging identification, with the highest staging accuracy of 0.952 ± 0.014, and a kappa value of 0.928 ± 0.022. By comparing the staging results based on spectral characteristics from the nuclear compartments and peripheral regions, the spectral data of the nuclear compartments were found to contribute more to the accurate staging of SCC.

Assessment of angle-dependent spectral distortion to develop accurate hyperspectral endoscopy

Hyperspectral endoscopy has shown its potential to improve disease diagnosis in gastrointestinal tracts. Recent approaches in developing hyperspectral endoscopy are mainly focusing on enhancing image speed and quality of spectral information under a clinical environment, but there are many issues in obtaining consistent spectral information due to complicated imaging conditions, including imaging angle, non-uniform illumination, working distance, and low reflected signal. We quantitatively investigated the effect of imaging angle on the distortion of spectral information by exploiting a bifurcated fiber, spectrometer, and tissue-mimicking phantom. Spectral distortion becomes severe as increasing the angle of the imaging fiber or shortening camera exposure time for fast image acquisition. Moreover, spectral ranges from 450 to 550 nm are more susceptible to the angle-dependent spectral distortion than longer spectral ranges. Therefore, imaging angles close to normal and longer target spectral ranges with enough detector exposure time could minimize spectral distortion in hyperspectral endoscopy. These findings will help implement clinical HSI endoscopy for the robust and accurate measurement of spectral information from patients in vivo.

[Artificial intelligence and hyperspectral imaging for image-guided assistance in minimally invasive surgery]

BACKGROUND

Intraoperative imaging assists surgeons during minimally invasive procedures. Hyperspectral imaging (HSI) is a noninvasive and noncontact optical technique with great diagnostic potential in medicine. The combination with artificial intelligence (AI) approaches to analyze HSI data is called intelligent HSI in this article.

OBJECTIVE

What are the medical applications and advantages of intelligent HSI for minimally invasive visceral surgery?

MATERIAL AND METHODS

Within various clinical studies HSI data from multiple in vivo tissue types and oncological resections were acquired using an HSI camera system. Different AI algorithms were evaluated for detection and discrimination of organs, risk structures and tumors.

RESULTS

In an experimental animal study 20 different organs could be differentiated with high precision (> 95%) using AI. In vivo, the parathyroid glands could be discriminated from surrounding tissue with an F1 score of 47% and sensitivity of 75%, and the bile duct with an F1 score of 79% and sensitivity of 90%. Furthermore, ex vivo tumor tissue could be successfully detected with an area under the receiver operating characteristic (ROC) curve (AUC) larger than 0.91.

DISCUSSION

This study demonstrates that intelligent HSI can automatically and accurately detect different tissue types. Despite great progress in the last decade intelligent HSI still has limitations. Thus, accurate AI algorithms that are easier to understand for the user and an extensive standardized and continuously growing database are needed. Further clinical studies should support the various medical applications and lead to the adoption of intelligent HSI in the clinical routine practice.