Contact Endoscopy - Narrow Band Imaging (CE-NBI) data set for laryngeal lesion assessment.

The endoscopic examination of subepithelial vascular patterns within the vocal fold is crucial for clinicians seeking to distinguish between benign lesions and laryngeal cancer. Among innovative techniques, Contact Endoscopy combined with Narrow Band Imaging (CE-NBI) offers real-time visualization of these vascular structures. Despite the advent of CE-NBI, concerns have arisen regarding the subjective interpretation of its images. As a result, several computer-based solutions have been developed to address this issue. This study introduces the CE-NBI data set, the first publicly accessible data set that features enhanced and magnified visualizations of subepithelial blood vessels within the vocal fold. This data set encompasses 11144 images from 210 adult patients with pathological vocal fold conditions, where CE-NBI images are annotated using three distinct label categories. The data set has proven invaluable for numerous clinical assessments geared toward diagnosing laryngeal cancer using Optical Biopsy. Furthermore, given its versatility for various image analysis tasks, we have devised and implemented diverse image classification scenarios using Machine Learning (ML) approaches to address critical clinical challenges in assessing laryngeal lesions.

Vibro-acoustic sensing of tissue-instrument-interactions allows a differentiation of biological tissue in computerised palpation.

BACKGROUND: The shift towards minimally invasive surgery is associated with a significant reduction of tactile information available to the surgeon, with compensation strategies ranging from vision-based techniques to the integration of sensing concepts into surgical instruments. Tactile information is vital for palpation tasks such as the differentiation of tissues or the characterisation of surfaces. This work investigates a new sensing approach to derive palpation-related information from vibration signals originating from instrument-tissue-interactions. METHODS: We conducted a feasibility study to differentiate three non-animal and three animal tissue specimens based on palpation of the surface. A sensor configuration was mounted at the proximal end of a standard instrument opposite the tissue-interaction point. Vibro-acoustic signals of 1680 palpation events were acquired, and the time-varying spectrum was computed using Continuous-Wavelet-Transformation. For validation, nine spectral energy-related features were calculated for a subsequent classification using linear Support Vector Machine and k-Nearest-Neighbor. RESULTS: Indicators derived from the vibration signal are highly stable in a set of palpations belonging to the same tissue specimen, regardless of the palpating subject. Differences in the surface texture of the tissue specimens reflect in those indicators and can serve as a basis for differentiation. The classification following a supervised learning approach shows an accuracy of >93.8% for the three-tissue classification tasks and decreases to 78.8% for a combination of all six tissues. CONCLUSIONS: Simple features derived from the vibro-acoustic signals facilitate the differentiation between biological tissues, showing the potential of the presented approach to provide information related to the interacting tissue. The results encourage further investigation of a yet little-exploited source of information in minimally invasive surgery.

Vibro-Acoustic Sensing of Instrument Interactions as a Potential Source of Texture-Related Information in Robotic Palpation.

The direct tactile assessment of surface textures during palpation is an essential component of open surgery that is impeded in minimally invasive and robot-assisted surgery. When indirectly palpating with a surgical instrument, the structural vibrations from this interaction contain tactile information that can be extracted and analysed. This study investigates the influence of the parameters contact angle α and velocity v→ on the vibro-acoustic signals from this indirect palpation. A 7-DOF robotic arm, a standard surgical instrument, and a vibration measurement system were used to palpate three different materials with varying α and v→. The signals were processed based on continuous wavelet transformation. They showed material-specific signatures in the time-frequency domain that retained their general characteristic for varying α and v→. Energy-related and statistical features were extracted, and supervised classification was performed, where the testing data comprised only signals acquired with different palpation parameters than for training data. The classifiers support vector machine and k-nearest neighbours provided 99.67% and 96.00% accuracy for the differentiation of the materials. The results indicate the robustness of the features against variations in the palpation parameters. This is a prerequisite for an application in minimally invasive surgery but needs to be confirmed in realistic experiments with biological tissues.

Surgeons' requirements for a surgical support system to improve laparoscopic access.

Creating surgical access is a critical step in laparoscopic surgery. Surgeons have to insert a sharp instrument such as the Veress needle or a trocar into the patient's abdomen until the peritoneal cavity is reached. They solely rely on their experience and distorted tactile feedback in that process, leading to a complication rate as high as 14% of all cases. Recent studies have shown the feasibility of surgical support systems that provide intraoperative feedback regarding the insertion process to improve laparoscopic access outcomes. However, to date, the surgeons' requirements for such support systems remain unclear. This research article presents the results of an explorative study that aimed to acquire data about the information that helps surgeons improve laparoscopic access outcomes. The results indicate that feedback regarding the reaching of the peritoneal cavity is of significant importance and should be presented visually or acoustically. Finally, a solution should be straightforward and intuitive to use, should support or even improve the clinical workflow, but also cheap enough to facilitate its usage rate. While this study was tailored to laparoscopic access, its results also apply to other minimally invasive procedures.

Deep Convolution Neural Network for Laryngeal Cancer Classification on Contact Endoscopy-Narrow Band Imaging.

(1) Background: Contact Endoscopy (CE) and Narrow Band Imaging (NBI) are optical imaging modalities that can provide enhanced and magnified visualization of the superficial vascular networks in the laryngeal mucosa. The similarity of vascular structures between benign and malignant lesions causes a challenge in the visual assessment of CE-NBI images. The main objective of this study is to use Deep Convolutional Neural Networks (DCNN) for the automatic classification of CE-NBI images into benign and malignant groups with minimal human intervention. (2) Methods: A pretrained Res-Net50 model combined with the cut-off-layer technique was selected as the DCNN architecture. A dataset of 8181 CE-NBI images was used during the fine-tuning process in three experiments where several models were generated and validated. The accuracy, sensitivity, and specificity were calculated as the performance metrics in each validation and testing scenario. (3) Results: Out of a total of 72 trained and tested models in all experiments, Model 5 showed high performance. This model is considerably smaller than the full ResNet50 architecture and achieved the testing accuracy of 0.835 on the unseen data during the last experiment. (4) Conclusion: The proposed fine-tuned ResNet50 model showed a high performance to classify CE-NBI images into the benign and malignant groups and has the potential to be part of an assisted system for automatic laryngeal cancer detection.

Cyclist Effort Features: A Novel Technique for Image Texture Characterization Applied to Larynx Cancer Classification in Contact Endoscopy-Narrow Band Imaging.

BACKGROUND: Feature extraction is an essential part of a Computer-Aided Diagnosis (CAD) system. It is usually preceded by a pre-processing step and followed by image classification. Usually, a large number of features is needed to end up with the desired classification results. In this work, we propose a novel approach for texture feature extraction. This method was tested on larynx Contact Endoscopy (CE)-Narrow Band Imaging (NBI) image classification to provide more objective information for otolaryngologists regarding the stage of the laryngeal cancer. METHODS: The main idea of the proposed methods is to represent an image as a hilly surface, where different paths can be identified between a starting and an ending point. Each of these paths can be thought of as a Tour de France stage profile where a cyclist needs to perform a specific effort to arrive at the finish line. Several paths can be generated in an image where different cyclists produce an average cyclist effort representing important textural characteristics of the image. Energy and power as two Cyclist Effort Features (CyEfF) were extracted using this concept. The performance of the proposed features was evaluated for the classification of 2701 CE-NBI images into benign and malignant lesions using four supervised classifiers and subsequently compared with the performance of 24 Geometrical Features (GF) and 13 Entropy Features (EF). RESULTS: The CyEfF features showed maximum classification accuracy of 0.882 and improved the GF classification accuracy by 3 to 12 percent. Moreover, CyEfF features were ranked as the top 10 features along with some features from GF set in two feature ranking methods. CONCLUSION: The results prove that CyEfF with only two features can describe the textural characterization of CE-NBI images and can be part of the CAD system in combination with GF for laryngeal cancer diagnosis.

Laryngeal Lesion Classification Based on Vascular Patterns in Contact Endoscopy and Narrow Band Imaging: Manual Versus Automatic Approach.

Longitudinal and perpendicular changes in the vocal fold's blood vessels are associated with the development of benign and malignant laryngeal lesions. The combination of Contact Endoscopy (CE) and Narrow Band Imaging (NBI) can provide intraoperative real-time visualization of the vascular changes in the laryngeal mucosa. However, the visual evaluation of vascular patterns in CE-NBI images is challenging and highly depends on the clinicians' experience. The current study aims to evaluate and compare the performance of a manual and an automatic approach for laryngeal lesion's classification based on vascular patterns in CE-NBI images. In the manual approach, six observers visually evaluated a series of CE+NBI images that belong to a patient and then classified the patient as benign or malignant. For the automatic classification, an algorithm based on characterizing the level of the vessel's disorder in combination with four supervised classifiers was used to classify CE-NBI images. The results showed that the manual approach's subjective evaluation could be reduced by using a computer-based approach. Moreover, the automatic approach showed the potential to work as an assistant system in case of disagreements among clinicians and to reduce the manual approach's misclassification issue.

Evaluation of Vascular Patterns Using Contact Endoscopy and Narrow-Band Imaging (CE-NBI) for the Diagnosis of Vocal Fold Malignancy.

The endoscopic detection of perpendicular vascular changes (PVC) of the vocal folds has been associated with vocal fold cancer, dysplastic lesions, and papillomatosis, according to a classification proposed by the European Laryngological Society (ELS). The combination of contact endoscopy with narrow-band imaging (NBI-CE) allows intraoperatively a highly contrasted, real-time visualization of vascular changes of the vocal folds. Aim of the present study was to determine the association of PVC to specific histological diagnoses, the level of interobserver agreement in the detection of PVC, and their diagnostic effectiveness in diagnosing laryngeal malignancy. The evaluation of our data confirmed the association of PVC to vocal fold cancer, dysplastic lesions, and papillomatosis. The level of agreement between the observers in the identification of PVC was moderate for the less-experienced observers and almost perfect for the experienced observers. The identification of PVC during NBI-CE proved to be a valuable indicator for diagnosing malignant and premalignant lesions.

Novel automated vessel pattern characterization of larynx contact endoscopic video images.

PURPOSE: Contact endoscopy (CE) is a minimally invasive procedure providing real-time information about the cellular and vascular structure of the superficial layer of laryngeal mucosa. This method can be combined with optical enhancement methods such as narrow band imaging (NBI). However, these techniques have some problems like subjective interpretation of vascular patterns and difficulty in differentiation between benign and malignant lesions. We propose a novel automated approach for vessel pattern characterization of larynx CE + NBI images in order to solve these problems. METHODS: In this approach, five indicators were computed to characterize the level of vessel's disorder based on evaluation of consistency of gradient and two-dimensional curvature analysis and then 24 features were extracted from these indicators. The method evaluated the ability of the extracted features to classify CE + NBI images based on the vascular pattern and based on the laryngeal lesions. Four datasets were generated from 32 patients involving 1485 images. The classification scenarios were implemented using four supervised classifiers. RESULTS: For classification of CE + NBI images based on the vascular pattern, polykernel support vector machine (SVM), SVM with radial basis function (RBF), k-nearest neighbor (kNN), and random forest (RF) show an accuracy of 97%, 96%, 96%, and 96%, respectively. For the classification based on the histopathology, Polykernel SVM showed an accuracy of 84%, 86% and 84%, RBF SVM showed an accuracy of 81%, 87% and 83%, kNN showed an accuracy of 89%, 87%, 91%, RF showed an accuracy of 90%, 88% and 91% for classification between benign histopathologies, between malignant histopathologies and between benign and malignant lesions, respectively. CONCLUSION: These promising results show that the proposed method could solve the problem of subjectivity in interpretation of vascular patterns and also support the clinicians in the early detection of benign, pre-malignant and malignant lesions.

Non-Fluorinated Polymer Composite Proton Exchange Membranes for Fuel Cell Applications - A Review.

The critical component of a proton exchange membrane fuel cell (PEMFC) system is the proton exchange membrane (PEM). Perfluorosulfonic acid membranes such as Nafion are currently used for PEMFCs in industry, despite suffering from reduced proton conductivity due to dehydration at higher temperatures. However, operating at temperatures below 100 °C leads to cathode flooding, catalyst poisoning by CO, and complex system design with higher cost. Research has concentrated on the membrane material and on preparation methods to achieve high proton conductivity, thermal, mechanical and chemical stability, low fuel crossover and lower cost at high temperatures. Non-fluorinated polymers are a promising alternative. However, improving the efficiency at higher temperatures has necessitated modifications and the inclusion of inorganic materials in a polymer matrix to form a composite membrane can be an approach to reach the target performance, while still reducing costs. This review focuses on recent research in composite PEMs based on non-fluorinated polymers. Various inorganic fillers incorporated in the PEM structure are reviewed in terms of their properties and the effect on PEM fuel cell performance. The most reliable polymers and fillers with potential for high temperature proton exchange membranes (HTPEMs) are also discussed.

Improving the Gas-Separation Properties of PVAc-Zeolite 4A Mixed-Matrix Membranes through Nano-Sizing and Silanation of the Zeolite.

Mixed-matrix membranes containing synthesised nano-sized zeolite 4A and PVAc were fabricated to investigate the effect of zeolite loading on membrane morphology, polymer-filler interaction, thermal stability and gas separation properties. SEM studies revealed that, although the membranes with 40 wt % nano-sized zeolite particles were distributed uniformly through the polymer matrix without voids, the membranes with 15 wt % zeolite loading showed agglomeration. With increasing zeolite content, the thermal stability improved, the permeability decreased and the selectivity increased. The effect of silanation on dispersion of 15 wt % zeolite 4A nanoparticles through PVAc was investigated by post-synthesis modification of the zeolite with 3-Aminopropyl(diethoxy)methylsilane. Modification of the nanoparticles improved their dispersion in PVAc, resulting in higher thermal stability than the corresponding unmodified zeolite membrane. Modification also decreased the rigidity of the membrane. Partial pore blockage of the modified zeolite nanoparticles after silanation caused a further decrease in permeability, compared to the 15 wt % unmodified zeolite membrane.

Electrochemotherapy: A Review of Current Status, Alternative IGP Approaches, and Future Perspectives.

The efficiency of electroporation (EP) has made it a widely used therapeutic procedure to transfer cell killing substances effectively to the target site. A lot of researches are being done on EP-based cancer treatment techniques. Electrochemotherapy (ECT) is the first EP-based application in the field of drug administration. ECT is a local and nonthermal treatment of cancer that combines the use of a medical device with pharmaceutical agents to obtain local tumor control in solid cancers. It involves the application of eight, 100µs, pulses at 1 or 5000 Hz frequency and specified electric field (V/cm) with a median duration of 25 minutes. The efficacy of chemotherapeutic drugs increases by applying short and intense electrical pulses. Several clinical studies proposed ECT as a safe and complementary curative or palliative treatment option (curative intent of 50% to 63% in the treatment of Basal Cell Carcinoma (BCC)) to treat a number of solid tumors and skin malignancies, which are not suitable for conventional treatments. It is used currently for treatment of cutaneous and subcutaneous lesions, without consideration of their histology. On the contrary, it is also becoming a practical method for treatment of internal, deep-seated tumors and tissues. A review of this method, needed instruments, alternative image-guided procedures (IGP) approaches, and future perspectives and recommendations are discussed in this paper.

Parametrical modelling for texture characterization-A novel approach applied to ultrasound thyroid segmentation.

Texture analysis is an important topic in Ultrasound (US) image analysis for structure segmentation and tissue classification. In this work a novel approach for US image texture feature extraction is presented. It is mainly based on parametrical modelling of a signal version of the US image in order to process it as data resulting from a dynamical process. Because of the predictive characteristics of such a model representation, good estimations of texture features can be obtained with less data than generally used methods require, allowing higher robustness to low Signal-to-Noise ratio and a more localized US image analysis. The usability of the proposed approach was demonstrated by extracting texture features for segmenting the thyroid in US images. The obtained results showed that features corresponding to energy ratios between different modelled texture frequency bands allowed to clearly distinguish between thyroid and non-thyroid texture. A simple k-means clustering algorithm has been used for separating US image patches as belonging to thyroid or not. Segmentation of thyroid was performed in two different datasets obtaining Dice coefficients over 85%.

Assessment of Sound Features for Needle Perforation Event Detection.

This paper studies the use of non-invasive acoustic emission recordings for clinical device tracking. In particular, audio signals recorded at the proximal end of a needle are used to detect perforation events that occur when the needle tip crosses internal tissue layers.A comparative study is performed to assess the capacity of different features and envelopes in detecting perforation events. The results obtained from the considered experimental setup show a statistically significant correlation between the extracted envelopes and the perforation events, thus leading the way for future development of perforation detection algorithms.

A Preliminary Study on Automatic Characterization and Classification of Vascular Patterns of Contact Endoscopy Images.

The structure and organization of blood vessels in the vocal fold go through changes during the advancement from healthy to benign and further on to malignant stages. Contact Endoscopy (CE) is an optical technique providing real-time information related to the vascular structure of laryngeal mucosa. However, this technique comes with subjectivity in the interpretation of vascular patterns. In this study, a novel automated approach is proposed for vessel pattern charac-terization and classification of larynx CE + Narrow Band Imaging (NBI) images. This method is mainly based on the computation of indicators related to the level of disorder of vessels. 12 features were extracted from the indicators and were fed into two supervised classifiers. Linear Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) showed an accuracy of 95.76% and 93.92% for vascular patterns and 86.04% and 82.23% for larynx histopathologies classification, respectively. These promising results show that the proposed method can potentially solve the subjectivity issues of CE.