Special Issue "Computer-aided Biomedical Imaging 2020: Advances and Prospects"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editors

Guest Editor
Prof. Dr. Marcos Ortega Hortas Website E-Mail
Professor of Computer Science, University of A Coruña, Spain
Interests: computer vision; biomedical image processing; pattern recognition and medical informatics
Guest Editor
Dr. Jorge Novo Buján Website E-Mail
Computer Science Department, University of A Coruña, Spain
Interests: computer vision; image processing; pattern recognition; biomedical image processing; machine learning
Guest Editor
Dr. Pablo Mesejo Santiago Website E-Mail
Marie Curie Individual Fellowship, Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
Interests: computer vision (image segmentation, image classification, image registration); machine learning (deep and shallow neural networks, ensemble classifiers); soft computing (metaheuristics); biomedical image analysis (in neuroscience, gastroenterology, and forensic sciences)

Special Issue Information

Dear Colleagues,

At present, image acquisition and analysis are a fundamental basis in many disciplines of biomedical scope. In tasks such as screening, diagnosis, treatment, drug development, molecular analysis, etc., visual information is crucial for a successful performance. Given the existence of numerous image modalities with more and more quality, the time and effort demanded from the specialists for its manual analysis is appalling, leading to an underutilization of the available visual information. Thus, computerized solutions for aiding in the process of the image analysis via automatic or semi-automatic procedures come as a necessity. In recent years, along with the availability of huge amounts of biomedical imaging data, new computer-based paradigms (e.g., Big Data or deep learning) are experiencing increasing popularity, improving traditional procedures related to many applications, including biomedical image analysis.

This Special Issue focuses on the recent advances and prospects in computer-aided biomedical imaging and welcomes contributions in topics that include but are not limited to:

  • Biomedical image analysis
  • Deep learning in biomedicine
  • Artificial Intelligence in biomedicine
  • Applied soft computing
  • Computer-assisted diagnosis
  • Image-guided therapy
  • Image-guided surgery and intervention
  • 2D and 3D modeling
  • 2D and 3D segmentation
  • 2D and 3D reconstruction
  • 2d and 3D registration and fusion
  • Motion analysis
  • Telemedicine with medical images
  • Image quality assessment
  • Applications of Big Data in imaging
  • Biomedical robotics and haptics

Prof. Dr. Marcos Ortega Hortas
Dr. Jorge Novo Buján
Dr. Pablo Mesejo Santiago
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 papers will be 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. Applied Sciences 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 1500 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 image analysis 
  • Deep learning in biomedicine
  • Artificial Intelligence in biomedicine 
  • Applied soft computing 
  • Computer-assisted diagnosis
  • Image-guided therapy 
  • Image-guided surgery and intervention
  • 2D and 3D modeling
  • 2D and 3D segmentation 
  • 2D and 3D reconstruction
  • 2d and 3D registration and fusion
  • Motion analysis
  • Telemedicine with medical images
  • Image quality assessment 
  • Applications of Big Data in imaging 
  • Biomedical robotics and haptics

Published Papers (3 papers)

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Research

Open AccessArticle
Identification of Knee Cartilage Changing Pattern
Appl. Sci. 2019, 9(17), 3469; https://doi.org/10.3390/app9173469 - 22 Aug 2019
Abstract
This paper studied the changing pattern of knee cartilage using 3D knee magnetic resonance (MR) images over a 12-month period. As a pilot study, we focused on the medial tibia compartment of the knee joint. To quantify the thickness of cartilage in this [...] Read more.
This paper studied the changing pattern of knee cartilage using 3D knee magnetic resonance (MR) images over a 12-month period. As a pilot study, we focused on the medial tibia compartment of the knee joint. To quantify the thickness of cartilage in this compartment, we utilized two methods: one was measurement through manual segmentation of cartilage on each slice of the 3D MR sequence; the other was measurement through cartilage damage index (CDI), which quantified the thickness on a few informative locations on cartilage. We employed the artificial neural networks (ANNs) to model the changing pattern of cartilage thickness. The input feature space was composed of the thickness information at a cartilage location as well as its neighborhood from baseline year data. The output categories were ‘changed’ and ‘no-change’, based on the thickness difference at the same location between the baseline year and the 12-month follow-up data. Different ANN models were trained by using CDI features and manual segmentation features. Further, for each type of feature, individual models were trained at different subregions of the medial tibia compartment, i.e., the bottom part, the middle part, the upper part, and the whole. Based on the experiment results, we found that CDI features generated better prediction performance than manual segmentation, on both whole medial tibia compartment and any subregion. For CDI, the best performance in term of AUC was obtained using the central CDI locations (AUC = 0.766), while the best performance for manual segmentation was obtained using all slices of the 3D MR sequence (AUC = 0.656). As experiment results showed, the CDI method demonstrated a stronger pattern of cartilage change than the manual segmentation method, which required up to 6-hour manual delineation of all MRI slices. The result should be further validated by extending the experiment to other compartments. Full article
(This article belongs to the Special Issue Computer-aided Biomedical Imaging 2020: Advances and Prospects)
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Open AccessArticle
Quantitative CT Analysis for Predicting the Behavior of Part-Solid Nodules with Solid Components Less than 6 mm: Size, Density and Shape Descriptors
Appl. Sci. 2019, 9(16), 3428; https://doi.org/10.3390/app9163428 - 20 Aug 2019
Abstract
Persistent part-solid nodules (PSNs) with a solid component <6 mm usually represent minimally invasive adenocarcinomas and are significantly less aggressive than PSNs with a solid component ≥6 mm. However, not all PSNs with a small solid component behave in the same way: some [...] Read more.
Persistent part-solid nodules (PSNs) with a solid component <6 mm usually represent minimally invasive adenocarcinomas and are significantly less aggressive than PSNs with a solid component ≥6 mm. However, not all PSNs with a small solid component behave in the same way: some nodules exhibit an indolent course, whereas others exhibit more aggressive behavior. Thus, predicting the future behavior of this subtype of PSN remains a complex and fascinating diagnostic challenge. The main purpose of this study was to apply open-source software to investigate which quantitative computed tomography (CT) features may be useful for predicting the behavior of a select group of PSNs. We retrospectively selected 50 patients with a single PSN with a solid component <6 mm and diameter <15 mm. Computerized analysis was performed using ImageJ software for each PSN and various quantitative features were calculated from the baseline CT images. The area, perimeter, mean Feret diameter, linear mass density, circularity and solidity were significantly related to nodule growth (p ≤ 0.031). Therefore, quantitative CT analysis was helpful for predicting the future behavior of a select group of PSNs with a solid component <6 mm and diameter <15 mm. Full article
(This article belongs to the Special Issue Computer-aided Biomedical Imaging 2020: Advances and Prospects)
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Open AccessArticle
Multi-Scale Heterogeneous 3D CNN for False-Positive Reduction in Pulmonary Nodule Detection, Based on Chest CT Images
Appl. Sci. 2019, 9(16), 3261; https://doi.org/10.3390/app9163261 - 09 Aug 2019
Abstract
Currently, lung cancer has one of the highest mortality rates because it is often caught too late. Therefore, early detection is essential to reduce the risk of death. Pulmonary nodules are considered key indicators of primary lung cancer. Developing an efficient and accurate [...] Read more.
Currently, lung cancer has one of the highest mortality rates because it is often caught too late. Therefore, early detection is essential to reduce the risk of death. Pulmonary nodules are considered key indicators of primary lung cancer. Developing an efficient and accurate computer-aided diagnosis system for pulmonary nodule detection is an important goal. Typically, a computer-aided diagnosis system for pulmonary nodule detection consists of two parts: candidate nodule extraction and false-positive reduction of candidate nodules. The reduction of false positives (FPs) of candidate nodules remains an important challenge due to morphological characteristics of nodule height changes and similar characteristics to other organs. In this study, we propose a novel multi-scale heterogeneous three-dimensional (3D) convolutional neural network (MSH-CNN) based on chest computed tomography (CT) images. There are three main strategies of the design: (1) using multi-scale 3D nodule blocks with different levels of contextual information as inputs; (2) using two different branches of 3D CNN to extract the expression features; (3) using a set of weights which are determined by back propagation to fuse the expression features produced by step 2. In order to test the performance of the algorithm, we trained and tested on the Lung Nodule Analysis 2016 (LUNA16) dataset, achieving an average competitive performance metric (CPM) score of 0.874 and a sensitivity of 91.7% at two FPs/scan. Moreover, our framework is universal and can be easily extended to other candidate false-positive reduction tasks in 3D object detection, as well as 3D object classification. Full article
(This article belongs to the Special Issue Computer-aided Biomedical Imaging 2020: Advances and Prospects)
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