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Special Issue "Trends and Prospects in Medical Hyperspectral Imagery"

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

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 7568

Special Issue Editors

Dr. César Sanz
E-Mail Website
Guest Editor
Universidad Politécnica de Madrid, Madrid, Spain
Interests: digital electronic design; hyperspectral imaging for health applications; video coding; high-performance heterogeneous computing.
Dr. Eduardo Juárez
E-Mail Website
Guest Editor
Universidad Politécnica de Madrid, Madrid, Spain
Interests: hyperspectral imaging for health applications; high-performance heterogeneous computing; energy consumption and performance optimization; mixed reality and immersive environments.
Dr. Miguel Chavarrías
E-Mail Website
Guest Editor
Universidad Politécnica de Madrid, Madrid, Spain
Interests: high-performance computing; parallel computing; video processing; hyperspectral imaging.

Special Issue Information

Hyperspectral Imaging (HSI) is a technology to identify and estimate the distribution of materials within a captured scene based on the measured surface reflectance at each spectral band. Although initially aimed at remote sensing applications, there is a growing research interest to perform in vivo HSI processing during surgeries to help in discerning between cancerous and healthy tissues and locating tumor margins. Outstanding camera size reduction in recent years has made hyperspectral camera arrays (HCA) a possibility that could bring the advantages of Immersive HSI to hospitals.

The promotion of personalized medicine in healthcare should leverage the availability of appropriate decision support tools based on the integration of available, e.g. ,MRI, IOUS, or emerging diagnostic means, e.g., HSI. Contributions from several fields such as Artificial Intelligence (AI), Neurosurgery, Electronic, and Computer Engineering, Pathology, 3D Graphics, Bio-Optics and Data Science, among others, are required to develop, from a multidisciplinary perspective, easy-to-use solutions which lead to better diagnostic accuracy and increased treatment effectiveness.

The aim of this Special Issue is to introduce to the remote sensing community results and applications of the Hyperspectral Imaging field into medical research. The goal is to provide beyond-state-of-the-art solutions to the challenge of building decision support tools for personalized medicine. Therefore, contributions such as integration of HSI with MRI or IOUS, real-time mapping of brain tumors, intraoperative immersive classification, bio-inspired classification algorithms, in vitro and ex vivo HSI analysis from resected samples or high-performance real-time HSI processing platform implementations, among others, are expected.

Dr. César Sanz
Dr. Eduardo Juárez
Dr. Miguel Chavarrías
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 2400 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

  • Hyperspectral imaging
  • Personalized medicine
  • Classification algorithms
  • Real-time HSI processing
  • Immersive HSI
  • Digital image processing
  • Multimedia signal processing

Published Papers (5 papers)

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Research

Article
GoRG: Towards a GPU-Accelerated Multiview Hyperspectral Depth Estimation Tool for Medical Applications
Sensors 2021, 21(12), 4091; https://doi.org/10.3390/s21124091 - 14 Jun 2021
Cited by 1 | Viewed by 1118
Abstract
HyperSpectral (HS) images have been successfully used for brain tumor boundary detection during resection operations. Nowadays, these classification maps coexist with other technologies such as MRI or IOUS that improve a neurosurgeon’s action, with their incorporation being a neurosurgeon’s task. The project in [...] Read more.
HyperSpectral (HS) images have been successfully used for brain tumor boundary detection during resection operations. Nowadays, these classification maps coexist with other technologies such as MRI or IOUS that improve a neurosurgeon’s action, with their incorporation being a neurosurgeon’s task. The project in which this work is framed generates an unified and more accurate 3D immersive model using HS, MRI, and IOUS information. To do so, the HS images need to include 3D information and it needs to be generated in real-time operating room conditions, around a few seconds. This work presents Graph cuts Reference depth estimation in GPU (GoRG), a GPU-accelerated multiview depth estimation tool for HS images also able to process YUV images in less than 5.5 s on average. Compared to a high-quality SoA algorithm, MPEG DERS, GoRG YUV obtain quality losses of −0.93 dB, −0.6 dB, and −1.96% for WS-PSNR, IV-PSNR, and VMAF, respectively, using a video synthesis processing chain. For HS test images, GoRG obtains an average RMSE of 7.5 cm, with most of its errors in the background, needing around 850 ms to process one frame and view. These results demonstrate the feasibility of using GoRG during a tumor resection operation. Full article
(This article belongs to the Special Issue Trends and Prospects in Medical Hyperspectral Imagery)
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Article
Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification
Sensors 2021, 21(11), 3827; https://doi.org/10.3390/s21113827 - 31 May 2021
Cited by 5 | Viewed by 1836
Abstract
Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist [...] Read more.
Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature. Full article
(This article belongs to the Special Issue Trends and Prospects in Medical Hyperspectral Imagery)
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Article
Curve-Based Classification Approach for Hyperspectral Dermatologic Data Processing
Sensors 2021, 21(3), 680; https://doi.org/10.3390/s21030680 - 20 Jan 2021
Viewed by 791
Abstract
This paper shows new contributions in the detection of skin cancer, where we present the use of a customized hyperspectral system that captures images in the spectral range from 450 to 950 nm. By choosing a 7 × 7 sub-image of each channel [...] Read more.
This paper shows new contributions in the detection of skin cancer, where we present the use of a customized hyperspectral system that captures images in the spectral range from 450 to 950 nm. By choosing a 7 × 7 sub-image of each channel in the hyperspectral image (HSI) and then taking the mean and standard deviation of these sub-images, we were able to make fits of the resulting curves. These fitted curves had certain characteristics, which then served as a basis of classification. The most distinct fit was for the melanoma pigmented skin lesions (PSLs), which is also the most aggressive malignant cancer. Furthermore, we were able to classify the other PSLs in malignant and benign classes. This gives us a rather complete classification method for PSLs with a novel perspective of the classification procedure by exploiting the variability of each channel in the HSI. Full article
(This article belongs to the Special Issue Trends and Prospects in Medical Hyperspectral Imagery)
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Article
Hyperspectral Imagery for Assessing Laser-Induced Thermal State Change in Liver
Sensors 2021, 21(2), 643; https://doi.org/10.3390/s21020643 - 18 Jan 2021
Cited by 9 | Viewed by 1151
Abstract
This work presents the potential of hyperspectral imaging (HSI) to monitor the thermal outcome of laser ablation therapy used for minimally invasive tumor removal. Our main goal is the establishment of indicators of the thermal damage of living tissues, which can be used [...] Read more.
This work presents the potential of hyperspectral imaging (HSI) to monitor the thermal outcome of laser ablation therapy used for minimally invasive tumor removal. Our main goal is the establishment of indicators of the thermal damage of living tissues, which can be used to assess the effect of the procedure. These indicators rely on the spectral variation of temperature-dependent tissue chromophores, i.e., oxyhemoglobin, deoxyhemoglobin, methemoglobin, and water. Laser treatment was performed at specific temperature thresholds (from 60 to 110 °C) on in-vivo animal liver and was assessed with a hyperspectral camera (500–995 nm) during and after the treatment. The indicators were extracted from the hyperspectral images after the following processing steps: the breathing motion compensation and the spectral and spatial filtering, the selection of spectral bands corresponding to specific tissue chromophores, and the analysis of the areas under the curves for each spectral band. Results show that properly combining spectral information related to deoxyhemoglobin, methemoglobin, lipids, and water allows for the segmenting of different zones of the laser-induced thermal damage. This preliminary investigation provides indicators for describing the thermal state of the liver, which can be employed in the future as clinical endpoints of the procedure outcome. Full article
(This article belongs to the Special Issue Trends and Prospects in Medical Hyperspectral Imagery)
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Article
Hyperspectral Imaging for Glioblastoma Surgery: Improving Tumor Identification Using a Deep Spectral-Spatial Approach
Sensors 2020, 20(23), 6955; https://doi.org/10.3390/s20236955 - 05 Dec 2020
Cited by 9 | Viewed by 1810
Abstract
The primary treatment for malignant brain tumors is surgical resection. While gross total resection improves the prognosis, a supratotal resection may result in neurological deficits. On the other hand, accurate intraoperative identification of the tumor boundaries may be very difficult, resulting in subtotal [...] Read more.
The primary treatment for malignant brain tumors is surgical resection. While gross total resection improves the prognosis, a supratotal resection may result in neurological deficits. On the other hand, accurate intraoperative identification of the tumor boundaries may be very difficult, resulting in subtotal resections. Histological examination of biopsies can be used repeatedly to help achieve gross total resection but this is not practically feasible due to the turn-around time of the tissue analysis. Therefore, intraoperative techniques to recognize tissue types are investigated to expedite the clinical workflow for tumor resection and improve outcome by aiding in the identification and removal of the malignant lesion. Hyperspectral imaging (HSI) is an optical imaging technique with the power of extracting additional information from the imaged tissue. Because HSI images cannot be visually assessed by human observers, we instead exploit artificial intelligence techniques and leverage a Convolutional Neural Network (CNN) to investigate the potential of HSI in twelve in vivo specimens. The proposed framework consists of a 3D–2D hybrid CNN-based approach to create a joint extraction of spectral and spatial information from hyperspectral images. A comparison study was conducted exploiting a 2D CNN, a 1D DNN and two conventional classification methods (SVM, and the SVM classifier combined with the 3D–2D hybrid CNN) to validate the proposed network. An overall accuracy of 80% was found when tumor, healthy tissue and blood vessels were classified, clearly outperforming the state-of-the-art approaches. These results can serve as a basis for brain tumor classification using HSI, and may open future avenues for image-guided neurosurgical applications. Full article
(This article belongs to the Special Issue Trends and Prospects in Medical Hyperspectral Imagery)
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