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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (20 November 2020) | Viewed by 35181

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Special Issue Editors

Prof. Dr. Arcangelo Merla

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Department of Engineering and Geology, University of G. d'Annunzio Chieti and Pescara, 65127 Pescara, Italy
Interests: infrared imaging; medical imaging; neuroimaging; psychophysiology; human–machine interaction
Special Issues, Collections and Topics in MDPI journals

Dr. Antonio Maria Chiarelli

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Infrared Imaging Lab, ITAB Institute for Advanced Biomedical Technologies, Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, 66100 Chieti, Italy
Interests: infrared imaging; diffuse optical imaging; neuroimaging; Bayesian statistics; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Daniela Cardone

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Guest Editor

Department of Engineering and Geology, University of G. d'Annunzio Chieti and Pescara, 65127 Pescara, Italy
Interests: artificial intelligence methods; robotics and affective computing; human–machine interaction; processing methods and analysis of biomedical images and physiological signals; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical research advancement is strictly interconnected with the development of novel sensor technology. This Special Issue aims to highlight recent advances in sensor development and to provide an opportunity of knowledge sharing between sensors experts and scientists that use the technology for biomedical applications. Hardware advances have provided the opportunity to utilize health monitoring technologies in heterogeneous environments, ranging from clinical settings to home monitoring. Novel algorithmic approaches (e.g., machine learning), recent growth in computational power, and multimodal integration have enabled new analysis techniques to further exploit biomedical technology. Different innovative detectors and applications that rely on such sensors, with emphasis but not only on wireless or portable technology, are suited topics. Original papers that describe new biomedical research or innovative applications and sensors are welcome. We look forward to and welcome your participation in this Special Issue.

Prof. Dr. Arcangelo Merla
Dr. Antonio Maria Chiarelli
Dr. Daniela Cardone
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Biomedical sensors
Detectors
Medical Imaging
Multimodal monitoring
Portable technology
Machine learning
Data-driven analysis
Image processing
Image analysis for disease detection, MRI, X-ray, PET, etc.

Published Papers (11 papers)

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Research

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14 pages, 6568 KiB

Open AccessArticle

An iPPG-Based Device for Pervasive Monitoring of Multi-Dimensional Cardiovascular Hemodynamics

by Jingjing Luo, Junjie Zhen, Peng Zhou, Wei Chen and Yuzhu Guo

Sensors 2021, 21(3), 872; https://doi.org/10.3390/s21030872 - 28 Jan 2021

Cited by 8 | Viewed by 2795

Abstract

Hemodynamic activities, as an essential measure of physiological and psychological characteristics, can be used for cardiovascular and cerebrovascular disease detection. Photoplethysmography imaging (iPPG) can be applied for such purposes with non-contact advances, however, most cardiovascular hemodynamics of iPPG systems are developed for laboratory research, which limits the application in pervasive healthcare. In this study, a video-based facial iPPG detecting equipment was devised to provide multi-dimensional spatiotemporal hemodynamic pulsations for applications with high portability and self-monitoring requirements. A series of algorithms have also been developed for physiological indices such as heart rate and breath rate extraction, facial region analysis, and visualization of hemodynamic pulsation distribution. Results showed that the new device can provide a reliable measurement of a rich range of cardiovascular hemodynamics. Combined with the advanced computing techniques, the new non-contact iPPG system provides a promising solution for user-friendly pervasive healthcare. Full article

(This article belongs to the Special Issue Sensors for Biomedical Imaging)

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15 pages, 2001 KiB

Open AccessArticle

Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

by Vidas Raudonis, Agne Paulauskaite-Taraseviciene and Kristina Sutiene

Sensors 2021, 21(3), 863; https://doi.org/10.3390/s21030863 - 28 Jan 2021

Cited by 9 | Viewed by 3042

Abstract

Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image. Full article

(This article belongs to the Special Issue Sensors for Biomedical Imaging)

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22 pages, 9622 KiB

Open AccessArticle

The RoScan Thermal 3D Body Scanning System: Medical Applicability and Benefits for Unobtrusive Sensing and Objective Diagnosis

by Adam Chromy and Ludek Zalud

Sensors 2020, 20(22), 6656; https://doi.org/10.3390/s20226656 - 20 Nov 2020

Cited by 3 | Viewed by 2687

Abstract

The RoScan is a novel, high-accuracy multispectral surface scanning system producing colored 3D models that include a thermal layer. (1) Background: at present, medicine still exhibits a lack of objective diagnostic methods. As many diseases involve thermal changes, thermography may appear to be a convenient technique for the given purpose; however, there are three limiting problems: exact localization, resolution vs. range, and impossibility of quantification. (2) Methods: the basic principles and benefits of the system are described. The procedures rely on a robotic manipulator with multiple sensors to create a multispectral 3D model. Importantly, the structure is robust, scene-independent, and features quantifiable measurement uncertainty; thus, all of the above problems of medical thermography are resolved. (3) Results: the benefits were demonstrated by several pilot case studies: medicament efficacy assessment in dermatology, objective recovery progress assessment in traumatology, applied force quantification in forensic sciences, exact localization of the cause of pain in physiotherapy, objective assessment of atopic dermatitis, and soft tissue volumetric measurements. (4) Conclusion: the RoScan addresses medical quantification, which embodies a frequent problem in several medical sectors, and can deliver new, objective information to improve the quality of healthcare and to eliminate false diagnoses. Full article

(This article belongs to the Special Issue Sensors for Biomedical Imaging)

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17 pages, 10057 KiB

Open AccessArticle

Fiber Bragg Grating Sensors for Performance Evaluation of Fast Magnetic Resonance Thermometry on Synthetic Phantom

by Martina De Landro, Jacopo Ianniello, Maxime Yon, Alexey Wolf, Bruno Quesson, Emiliano Schena and Paola Saccomandi

Sensors 2020, 20(22), 6468; https://doi.org/10.3390/s20226468 - 12 Nov 2020

Cited by 14 | Viewed by 3237

Abstract

The increasing recognition of minimally invasive thermal treatment of tumors motivate the development of accurate thermometry approaches for guaranteeing the therapeutic efficacy and safety. Magnetic Resonance Thermometry Imaging (MRTI) is nowadays considered the gold-standard in thermometry for tumor thermal therapy, and assessment of its performances is required for clinical applications. This study evaluates the accuracy of fast MRTI on a synthetic phantom, using dense ultra-short Fiber Bragg Grating (FBG) array, as a reference. Fast MRTI is achieved with a multi-slice gradient-echo echo-planar imaging (GRE-EPI) sequence, allowing monitoring the temperature increase induced with a 980 nm laser source. The temperature distributions measured with 1 mm-spatial resolution with both FBGs and MRTI were compared. The root mean squared error (RMSE) value obtained by comparing temperature profiles showed a maximum error of 1.2 °C. The Bland-Altman analysis revealed a mean of difference of 0.1 °C and limits of agreement 1.5/−1.3 °C. FBG sensors allowed to extensively assess the performances of the GRE-EPI sequence, in addition to the information on the MRTI precision estimated by considering the signal-to-noise ratio of the images (0.4 °C). Overall, the results obtained for the GRE-EPI fully satisfy the accuracy (~2 °C) required for proper temperature monitoring during thermal therapies. Full article

(This article belongs to the Special Issue Sensors for Biomedical Imaging)

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19 pages, 896 KiB

Open AccessArticle

Assessing Changes in Dielectric Properties Due to Nanomaterials Using a Two-Port Microwave System

by Mohammed Rahman, Rachita Lahri, Syed Ahsan, Maya Thanou and Panagiotis Kosmas

Sensors 2020, 20(21), 6228; https://doi.org/10.3390/s20216228 - 31 Oct 2020

Cited by 3 | Viewed by 2312

Abstract

Detecting changes in the dielectric properties of tissues at microwave frequencies can offer simple and cost effective tools for cancer detection. These changes can be enhanced by the use of nanoparticles (NPs) that are characterised by both increased tumour uptake and high dielectric constant. This paper presents a two-port experimental setup to assess the impact of contrast enhancement on microwave signals. The study focuses on carbon nanotubes, as they have been previously shown to induce high microwave dielectric contrast. We investigate multiwall carbon nanotubes (MWNT) and their -OH functionalised version (MWNT-OH) dispersed in tissue phantoms as contrast enhancing NPs, as well as salt (NaCl) solutions as reference mixtures which can be easily dissolved inside water mixtures and thus induce dielectric contrast changes reliably. MWNT and MWNT-OH are characterised by atomic force microscopy, and their dielectric properties are measured when dispersed in 60% glycerol–water mixtures. Salt concentrations between 10 and 50 mg/mL in 60% glycerol mixtures are also studied as homogeneous samples known to affect the dielectric constant. Contrast enhancement is then evaluated using a simplified two-port microwave system to identify the impact on microwave signals with respect to dielectric contrast. Numerical simulations are also conducted to compare results with the experimental findings. Our results suggest that this approach can be used as a reliable method to screen and assess contrast enhancing materials with regards to a microwave system’s ability to detect their impact on a target. Full article

(This article belongs to the Special Issue Sensors for Biomedical Imaging)

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13 pages, 3058 KiB

Open AccessArticle

Organ Contouring for Lung Cancer Patients with a Seed Generation Scheme and Random Walks

by Da-Chuan Cheng, Jen-Hong Chi, Shih-Neng Yang and Shing-Hong Liu

Sensors 2020, 20(17), 4823; https://doi.org/10.3390/s20174823 - 26 Aug 2020

Cited by 4 | Viewed by 2378

Abstract

In this study, we proposed a semi-automated and interactive scheme for organ contouring in radiotherapy planning for patients with non-small cell lung cancers. Several organs were contoured, including the lungs, airway, heart, spinal cord, body, and gross tumor volume (GTV). We proposed some schemes to automatically generate and vanish the seeds of the random walks (RW) algorithm. We considered 25 lung cancer patients, whose computed tomography (CT) images were obtained from the China Medical University Hospital (CMUH) in Taichung, Taiwan. The manual contours made by clinical oncologists were taken as the gold standard for comparison to evaluate the performance of our proposed method. The Dice coefficient between two contours of the same organ was computed to evaluate the similarity. The average Dice coefficients for the lungs, airway, heart, spinal cord, and body and GTV segmentation were 0.92, 0.84, 0.83, 0.73, 0.85 and 0.66, respectively. The computation time was between 2 to 4 min for a whole CT sequence segmentation. The results showed that our method has the potential to assist oncologists in the process of radiotherapy treatment in the CMUH, and hopefully in other hospitals as well, by saving a tremendous amount of time in contouring. Full article

(This article belongs to the Special Issue Sensors for Biomedical Imaging)

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26 pages, 8099 KiB

Open AccessArticle

A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks

by Jianning Chi, Shuang Zhang, Xiaosheng Yu, Chengdong Wu and Yang Jiang

Sensors 2020, 20(15), 4301; https://doi.org/10.3390/s20154301 - 1 Aug 2020

Cited by 11 | Viewed by 3315

Abstract

Pulmonary nodule detection in chest computed tomography (CT) is of great significance for the early diagnosis of lung cancer. Therefore, it has attracted more and more researchers to propose various computer-assisted pulmonary nodule detection methods. However, these methods still could not provide convincing results because the nodules are easily confused with calcifications, vessels, or other benign lumps. In this paper, we propose a novel deep convolutional neural network (DCNN) framework for detecting pulmonary nodules in the chest CT image. The framework consists of three cascaded networks: First, a U-net network integrating inception structure and dense skip connection is proposed to segment the region of lung parenchyma from the chest CT image. The inception structure is used to replace the first convolution layer for better feature extraction with respect to multiple receptive fields, while the dense skip connection could reuse these features and transfer them through the network. Secondly, a modified U-net network where all the convolution layers are replaced by dilated convolution is proposed to detect the “suspicious nodules” in the image. The dilated convolution can increase the receptive fields to improve the ability of the network in learning global information of the image. Thirdly, a modified U-net adapting multi-scale pooling and multi-resolution convolution connection is proposed to find the true pulmonary nodule in the image with multiple candidate regions. During the detection, the result of the former step is used as the input of the latter step to follow the “coarse-to-fine” detection process. Moreover, the focal loss, perceptual loss and dice loss were used together to replace the cross-entropy loss to solve the problem of imbalance distribution of positive and negative samples. We apply our method on two public datasets to evaluate its ability in pulmonary nodule detection. Experimental results illustrate that the proposed method outperform the state-of-the-art methods with respect to accuracy, sensitivity and specificity. Full article

(This article belongs to the Special Issue Sensors for Biomedical Imaging)

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17 pages, 4459 KiB

Open AccessArticle

Towards Optical Imaging for Spine Tracking without Markers in Navigated Spine Surgery

by Francesca Manni, Adrian Elmi-Terander, Gustav Burström, Oscar Persson, Erik Edström, Ronald Holthuizen, Caifeng Shan, Svitlana Zinger, Fons van der Sommen and Peter H. N. de With

Sensors 2020, 20(13), 3641; https://doi.org/10.3390/s20133641 - 29 Jun 2020

Cited by 15 | Viewed by 3754

Abstract

Surgical navigation systems are increasingly used for complex spine procedures to avoid neurovascular injuries and minimize the risk for reoperations. Accurate patient tracking is one of the prerequisites for optimal motion compensation and navigation. Most current optical tracking systems use dynamic reference frames (DRFs) attached to the spine, for patient movement tracking. However, the spine itself is subject to intrinsic movements which can impact the accuracy of the navigation system. In this study, we aimed to detect the actual patient spine features in different image views captured by optical cameras, in an augmented reality surgical navigation (ARSN) system. Using optical images from open spinal surgery cases, acquired by two gray-scale cameras, spinal landmarks were identified and matched in different camera views. A computer vision framework was created for preprocessing of the spine images, detecting and matching local invariant image regions. We compared four feature detection algorithms, Speeded Up Robust Feature (SURF), Maximal Stable Extremal Region (MSER), Features from Accelerated Segment Test (FAST), and Oriented FAST and Rotated BRIEF (ORB) to elucidate the best approach. The framework was validated in 23 patients and the 3D triangulation error of the matched features was <

0.5

mm. Thus, the findings indicate that spine feature detection can be used for accurate tracking in navigated surgery. Full article

(This article belongs to the Special Issue Sensors for Biomedical Imaging)

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31 pages, 1232 KiB

Open AccessArticle

Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

by Jakub Browarczyk, Adam Kurowski and Bozena Kostek

Sensors 2020, 20(8), 2403; https://doi.org/10.3390/s20082403 - 23 Apr 2020

Cited by 4 | Viewed by 3713

Abstract

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments. Full article

(This article belongs to the Special Issue Sensors for Biomedical Imaging)

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23 pages, 5602 KiB

Open AccessArticle

Intraretinal Fluid Pattern Characterization in Optical Coherence Tomography Images

by Joaquim de Moura, Plácido L. Vidal, Jorge Novo, José Rouco, Manuel G. Penedo and Marcos Ortega

Sensors 2020, 20(7), 2004; https://doi.org/10.3390/s20072004 - 3 Apr 2020

Cited by 15 | Viewed by 3722

Abstract

Optical Coherence Tomography (OCT) has become a relevant image modality in the ophthalmological clinical practice, as it offers a detailed representation of the eye fundus. This medical imaging modality is currently one of the main means of identification and characterization of intraretinal cystoid regions, a crucial task in the diagnosis of exudative macular disease or macular edema, among the main causes of blindness in developed countries. This work presents an exhaustive analysis of intensity and texture-based descriptors for its identification and classification, using a complete set of 510 texture features, three state-of-the-art feature selection strategies, and seven representative classifier strategies. The methodology validation and the analysis were performed using an image dataset of 83 OCT scans. From these images, 1609 samples were extracted from both cystoid and non-cystoid regions. The different tested configurations provided satisfactory results, reaching a mean cross-validation test accuracy of 92.69%. The most promising feature categories identified for the issue were the Gabor filters, the Histogram of Oriented Gradients (HOG), the Gray-Level Run-Length matrix (GLRL), and the Laws’ texture filters (LAWS), being consistently and considerably selected along all feature selector algorithms in the top positions of different relevance rankings. Full article

(This article belongs to the Special Issue Sensors for Biomedical Imaging)

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Review

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22 pages, 6244 KiB

Open AccessReview

Tablet Technology for Writing and Drawing during Functional Magnetic Resonance Imaging: A Review

by Zhongmin Lin, Fred Tam, Nathan W. Churchill, Tom A. Schweizer and Simon J. Graham

Sensors 2021, 21(2), 401; https://doi.org/10.3390/s21020401 - 8 Jan 2021

Cited by 2 | Viewed by 3193

Abstract

Functional magnetic resonance imaging (fMRI) is a powerful modality to study brain activity. To approximate naturalistic writing and drawing behaviours inside the scanner, many fMRI-compatible tablet technologies have been developed. The digitizing feature of the tablets also allows examination of behavioural kinematics with greater detail than using paper. With enhanced ecological validity, tablet devices have advanced the fields of neuropsychological tests, neurosurgery, and neurolinguistics. Specifically, tablet devices have been used to adopt many traditional paper-based writing and drawing neuropsychological tests for fMRI. In functional neurosurgery, tablet technologies have enabled intra-operative brain mapping during awake craniotomy in brain tumour patients, as well as quantitative tremor assessment for treatment outcome monitoring. Tablet devices also play an important role in identifying the neural correlates of writing in the healthy and diseased brain. The fMRI-compatible tablets provide an excellent platform to support naturalistic motor responses and examine detailed behavioural kinematics. Full article

(This article belongs to the Special Issue Sensors for Biomedical Imaging)

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