FPI Based Hyperspectral Imager for the Complex Surfaces—Calibration, Illumination and Applications

Hyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types. Typical challenges of these applications relate to complex skin surfaces, leaving some skin areas unreachable. In this study, we introduce a novel spectral imaging concept and conduct a clinical pre-test, the findings of which can be used to develop the concept towards a clinical application. The SICSURFIS spectral imager concept combines a piezo-actuated Fabry–Pérot interferometer (FPI) based hyperspectral imager, a specially designed LED module and several sizes of stray light protection cones for reaching and adapting to the complex skin surfaces. The imager is designed for the needs of photometric stereo imaging for providing the skin surface models (3D) for each captured wavelength. The captured HS images contained 33 selected wavelengths (ranging from 477 nm to 891 nm), which were captured simultaneously with accordingly selected LEDs and three specific angles of light. The pre-test results show that the data collected with the new SICSURFIS imager enable the use of the spectral and spatial domains with surface model information. The imager can reach complex skin surfaces. Healthy skin, basal cell carcinomas and intradermal nevi lesions were classified and delineated pixel-wise with promising results, but further studies are needed. The results were obtained with a convolutional neural network

FPI Based Hyperspectral Imager for the Complex Surfaces—Calibration, Illumination and Applications

Hyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types. Typical challenges of these applications relate to complex skin surfaces, leaving some skin areas unreachable. In this study, we introduce a novel spectral imaging concept and conduct a clinical pre-test, the findings of which can be used to develop the concept towards a clinical application. The SICSURFIS spectral imager concept combines a piezo-actuated Fabry–Pérot interferometer (FPI) based hyperspectral imager, a specially designed LED module and several sizes of stray light protection cones for reaching and adapting to the complex skin surfaces. The imager is designed for the needs of photometric stereo imaging for providing the skin surface models (3D) for each captured wavelength. The captured HS images contained 33 selected wavelengths (ranging from 477 nm to 891 nm), which were captured simultaneously with accordingly selected LEDs and three specific angles of light. The pre-test results show that the data collected with the new SICSURFIS imager enable the use of the spectral and spatial domains with surface model information. The imager can reach complex skin surfaces. Healthy skin, basal cell carcinomas and intradermal nevi lesions were classified and delineated pixel-wise with promising results, but further studies are needed. The results were obtained with a convolutional neural network

Computer aided diagnosis system using dermatoscopical image

Computer Aided Diagnosis (CAD) systems for melanoma detection aim to mirror the expert dermatologist decision when watching a dermoscopic or clinical image. Computer Vision techniques, which can be based on expert knowledge or not, are used to characterize the lesion image. This information is delivered to a machine learning algorithm, which gives a diagnosis suggestion as an output. This research is included into this field, and addresses the objective of implementing a complete CAD system using ‘state of the art’ descriptors and dermoscopy images as input. Some of them are based on expert knowledge and others are typical in a wide variety of problems. Images are initially transformed into oRGB, a perceptual color space, looking for both enhancing the information that images provide and giving human perception to machine algorithms. Feature selection is also performed to find features that really contribute to discriminate between benign and malignant pigmented skin lesions (PSL). The problem of robust model fitting versus statistically significant system evaluation is critical when working with small datasets, which is indeed the case. This topic is not generally considered in works related to PSLs. Consequently, a method that optimizes the compromise between these two goals is proposed, giving non-overfitted models and statistically significant measures of performance. In this manner, different systems can be compared in a fairer way. A database which enjoys wide international acceptance among dermatologists is used for the experiments.Ingeniería de Sistemas Audiovisuale

Advancing Artificial Intelligence in Sensors, Signals, and Imaging Informatics.

ObjectiveTo identify research works that exemplify recent developments in the field of sensors, signals, and imaging informatics.MethodA broad literature search was conducted using PubMed and Web of Science, supplemented with individual papers that were nominated by section editors. A predefined query made from a combination of Medical Subject Heading (MeSH) terms and keywords were used to search both sources. Section editors then filtered the entire set of retrieved papers with each paper having been reviewed by two section editors. Papers were assessed on a three-point Likert scale by two section editors, rated from 0 (do not include) to 2 (should be included). Only papers with a combined score of 2 or above were considered.ResultsA search for papers was executed at the start of January 2019, resulting in a combined set of 1,459 records published in 2018 in 119 unique journals. Section editors jointly filtered the list of candidates down to 14 nominations. The 14 candidate best papers were then ranked by a group of eight external reviewers. Four papers, representing different international groups and journals, were selected as the best papers by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board.ConclusionsThe fields of sensors, signals, and imaging informatics have rapidly evolved with the application of novel artificial intelligence/machine learning techniques. Studies have been able to discover hidden patterns and integrate different types of data towards improving diagnostic accuracy and patient outcomes. However, the quality of papers varied widely without clear reporting standards for these types of models. Nevertheless, a number of papers have demonstrated useful techniques to improve the generalizability, interpretability, and reproducibility of increasingly sophisticated models

VIBRATIONAL SPECTROSCOPY FOR THE ASSESSMENT OF VULVAL DISEASE

Vibrational spectroscopic diagnostic techniques have significant potential to improve the care of women with benign, premalignant and malignant vulval diseases by reducing the reliance on traditional biopsy and histopathology. These techniques also have the potential to augment clinicians’ ability to differentiate different types of vulval disease at the time of surgery for neoplastic vulval disease. In addition, vibrational spectroscopic techniques offer the opportunity to assess molecular changes associated with the development of vulval cancer that are not apparent on routine histopathological assessment. The work outlined in this thesis evaluates the role of emerging techniques in vibrational spectroscopy to address this need within three key themes: 1. Developmentofavibrationalspectroscopicdiagnostictechniquetoreducethe reliance on traditional biopsy and histopathological diagnosis. 2. Developmentofavibrationalspectroscopicdiagnostictechniqueforimproving the delineation of disease margins at the time of surgery for pre-malignant and malignant vulval conditions. 3. Evaluation of a vibrational spectroscopic tool for augmenting and automating aspects of vulval histopathology. Raman spectroscopic mapping of 91 fresh frozen vulval tissue sections combined with multivariate spectral analysis was used to demonstrate that malignant vulval disease could be differentiated from non-neoplastic and premalignant vulval disease with a sensitivity of 97% and specificity of 78%. The technique was then tested in experimental conditions closer to in-vivo application, measuring spectra from 91 whole fresh frozen tissue blocks using microscope and probe Raman systems. This demonstrated the technique could differentiate malignant from non-neoplastic and premalignant vulval disease with sensitivities of 84% to 92% and specificities of 84% to 64% respectively. In a separate investigation vulval tissue blocks from 27 women with suspected lichen sclerosus underwent Raman spectroscopic point measurements. Multivariate analysis demonstrated Raman spectroscopy could be used to differentiate lichen sclerosus from other vulval disorders with a similar clinical appearance with a sensitivity of sensitivity of 91% and specificity of 80%. Fourier transform infrared (FTIR) spectroscopic mapping of 93 fixed paraffin embedded tissue sections was used to demonstrate that malignant vulval disease could be differentiated from non-neoplastic and premalignant with vulval disease with an approximate sensitivity of 100% and specificity of 79%. In addition FTIR spectroscopy was used to differentiate molecular changes in vulval intraepithelial neoplasia (VIN) and lichen sclerosus (LS) found in association with vulval squamous cell carcinoma (SCC). Analysis of FTIR spectroscopic tissue maps from 48 patients demonstrated the technique could differentiate LS associated with SCC with a sensitivity of approximately 100% and specificity of 84% and VIN associated with SCC with a sensitivity of approximately 100% and specificity 58%. This thesis demonstrates the considerable potential of vibrational spectroscopy in this clinical setting. The research has made significant progress in each of the three themes outlined above and indicates that further work is warranted to develop the techniques towards routine clinical application

A Review on Skin Disease Classification and Detection Using Deep Learning Techniques

Skin cancer ranks among the most dangerous cancers. Skin cancers are commonly referred to as Melanoma. Melanoma is brought on by genetic faults or mutations on the skin, which are caused by Unrepaired Deoxyribonucleic Acid (DNA) in skin cells. It is essential to detect skin cancer in its infancy phase since it is more curable in its initial phases. Skin cancer typically progresses to other regions of the body. Owing to the disease's increased frequency, high mortality rate, and prohibitively high cost of medical treatments, early diagnosis of skin cancer signs is crucial. Due to the fact that how hazardous these disorders are, scholars have developed a number of early-detection techniques for melanoma. Lesion characteristics such as symmetry, colour, size, shape, and others are often utilised to detect skin cancer and distinguish benign skin cancer from melanoma. An in-depth investigation of deep learning techniques for melanoma's early detection is provided in this study. This study discusses the traditional feature extraction-based machine learning approaches for the segmentation and classification of skin lesions. Comparison-oriented research has been conducted to demonstrate the significance of various deep learning-based segmentation and classification approaches

A Step Forward in Breast Cancer Research: From a Natural-Like Experimental Model to a Preliminary Photothermal Approach

Supplementary Materials - The following are available online at https://www.mdpi.com/1422-0067/21/24/9681/s1, Figure S1: GNPs’ size distribution by intensity (%) obtained by DLS.Breast cancer is one of the most frequently diagnosed malignancies and common causes of cancer death in women. Recent studies suggest that environmental exposures to certain chemicals, such as 7,12-Dimethylbenzanthracene (DMBA), a chemical present in tobacco, may increase the risk of developing breast cancer later in life. The first-line treatments for breast cancer (surgery, chemotherapy or a combination of both) are generally invasive and frequently associated with severe side effects and high comorbidity. Consequently, novel approaches are strongly required to find more natural-like experimental models that better reflect the tumors’ etiology, physiopathology and response to treatments, as well as to find more targeted, efficient and minimally invasive treatments. This study proposes the development and an in deep biological characterization of an experimental model using DMBA-tumor-induction in Sprague-Dawley female rats. Moreover, a photothermal therapy approach using a near-infrared laser coupled with gold nanoparticles was preliminarily assessed. The gold nanoparticles were functionalized with Epidermal Growth Factor, and their physicochemical properties and in vitro effects were characterized. DMBA proved to be a very good and selective inductor of breast cancer, with 100% incidence and inducing an average of 4.7 tumors per animal. Epigenetic analysis showed that tumors classified with worst prognosis were hypomethylated. The tumor-induced rats were then subjected to a preliminary treatment using functionalized gold nanoparticles and its activation by laser (650–900 nm). The treatment outcomes presented very promising alterations in terms of tumor histology, confirming the presence of necrosis in most of the cases. Although this study revealed encouraging results as a breast cancer therapy, it is important to define tumor eligibility and specific efficiency criteria to further assess its application in breast cancer treatment on other species.The APC was funded by Faculty of Pharmacy, University of Coimbra and Coimbra Chemistry Centre, Department of Chemistry, University of Coimbra. This work was also supported by Fundação para a Ciência e Tecnologia (FCT), Portugal under the project’s references UIDB/00645/2020 and UID/DTP/04138/2019. TFG was supported by FCT, Portugal under the reference SFRH/BD/147306/2019. Thanks to FCT/MCTES for the financial support to CESAM (UIDP/50017/2020+UIDB/50017/2020), through national funds.info:eu-repo/semantics/publishedVersio