Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Medical Digital Image: Technologies and Applications
share announcement

Medical Digital Image: Technologies and Applications

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 13375

Special Issue Editor

Prof. Miguel Souto-Bayarri

E-Mail Website
Guest Editor
Complejo Hospitalario Universitario de Santiago, Santiago de Compostela, Spain
Interests: digital image processing; digital radiology; Computer aided diagnosis (CAD); chest computed tomography imaging; cardiac magnetic resonance

Special Issue Information

Dear Colleagues,

For most people, talking about the digitization of diagnosis by images may seem a little far, but the truth is that we are dealing with an important issue that affects decisively the health care of the majority of the people. Namely, in any city, without it being necessary to visit the large capitals, there are in each hospital several scanners of computed tomography, magnetic resonances, ultrasounds, telemedicine…. A multitude of new equipment has emerged in unprecedented growth, unless we go back to the time immediately after the discovery of X-rays in 1895.

Digitization of both the radiologic images and the clinical and demographic data provides an increase in the quality of health care and procedures. Digitization strategy in the health environment rests on two strength ideas: the logical integration of the image data (PACS) with the clinical and demographic data of the patient; and the transmission of data and images. The advantages of the digital format (processing, reconstruction of the images in the different planes of the space, in three dimensions and video (4D), transmission to remote places and through the network of the medical images accompanied by the pertinent information of the patients, etc), explain the primacy of the process.

As a result, what are the main surprises that will come to us from technology in the coming years?

It is impossible to know. But we may venture to point out three innovations with character, and potentially transformative. One is in the field of archiving and communications, where there will be interesting news related to storage and also to the improvement in the speed of transmission and recovery of images, as well as technologies at lower costs. The second is the increasing interconnection of all types of devices and objects through the Network. Medical reports can be written in a support (tablet, telephone...) that will not necessarily have to be located in either a hospital or other health center. A third group of innovations will be related to technologies that may improve diagnosis and treatment, either by lowering the price of medical services themselves, by helping people with disabilities, by improving the quality of life of the elderly, or by developing  algorithms for computer-aided diagnosis and quantitative extraction of imaging features from medical scans (“Radiomics”).

Prof. Miguel Souto-Bayarri
Guest Editor

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. 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 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

  • CAD (Computer-aided diagnosis)
  • Artificial intelligence
  • Radiomics
  • Machine learning
  • Neural networks
  • CT
  • Deep learning based computer-aided diagnosis (DL-CAD)

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

8 pages, 876 KiB  
Article
Evaluation of Automated Segmentation Algorithm for Macular Volumetric Measurements of Eight Individual Retinal Layer Thickness
by Ori Zahavi, Alberto Domínguez-Vicent, Rune Brautaset and Abinaya Priya Venkataraman
Appl. Sci. 2021, 11(3), 1250; https://0-doi-org.brum.beds.ac.uk/10.3390/app11031250 - 29 Jan 2021
Cited by 4 | Viewed by 1369
Abstract
Background: We evaluated the performance of an automated algorithm available on a clinical OCT (Canon-HS100) for macular volumetric measurements of eight individual retinal layers. Methods and Analysis: Two consecutive three-dimensional scans were acquired on 29 subjects with healthy retinas. Thickness measurements were obtained [...] Read more.
Background: We evaluated the performance of an automated algorithm available on a clinical OCT (Canon-HS100) for macular volumetric measurements of eight individual retinal layers. Methods and Analysis: Two consecutive three-dimensional scans were acquired on 29 subjects with healthy retinas. Thickness measurements were obtained from eight individual retinal layers in nine macular sectors based on Early Treatment Diabetic Retinopathy Study (ETDRS) protocol. The repeatability was evaluated using the within-subject standard deviation from which the repeatability limits (Rlimit) and coefficient of variation (CoV) were calculated. Results: The repeatability metrics varied among different layers and sectors. The variation among the sectors was larger in two of the outer layers (plexiform and nuclear layer) and the retinal nerve fiber layer. For the other five layers, the repeatability limit was less than 5µm and CoV was less than 7.5% in all nine ETDRS sectors. Conclusions: The repeatability of the OCT-HS100 to measure eight individual retinal layers is good in general. Nevertheless, the repeatability is not homogeneous among different layers and sectors. This needs to be taken into account while designing clinical measurement protocols. Full article
(This article belongs to the Special Issue Medical Digital Image: Technologies and Applications)
►▼ Show Figures

Figure 1

15 pages, 1465 KiB  
Article
CT Radiomics in Colorectal Cancer: Detection of KRAS Mutation Using Texture Analysis and Machine Learning
by Víctor González-Castro, Eva Cernadas, Emilio Huelga, Manuel Fernández-Delgado, Jacobo Porto, José Ramón Antunez and Miguel Souto-Bayarri
Appl. Sci. 2020, 10(18), 6214; https://0-doi-org.brum.beds.ac.uk/10.3390/app10186214 - 07 Sep 2020
Cited by 13 | Viewed by 2528
Abstract
In this work, by using descriptive techniques, the characteristics of the texture of the CT (computed tomography) image of patients with colorectal cancer were extracted and, subsequently, classified in KRAS+ or KRAS-. This was accomplished by using different classifiers, such as Support Vector [...] Read more.
In this work, by using descriptive techniques, the characteristics of the texture of the CT (computed tomography) image of patients with colorectal cancer were extracted and, subsequently, classified in KRAS+ or KRAS-. This was accomplished by using different classifiers, such as Support Vector Machine (SVM), Grading Boosting Machine (GBM), Neural Networks (NNET), and Random Forest (RF). Texture analysis can provide a quantitative assessment of tumour heterogeneity by analysing both the distribution and relationship between the pixels in the image. The objective of this research is to demonstrate that CT-based Radiomics can predict the presence of mutation in the KRAS gene in colorectal cancer. This is a retrospective study, with 47 patients from the University Hospital, with a confirmatory pathological analysis of KRAS mutation. The highest accuracy and kappa achieved were 83% and 64.7%, respectively, with a sensitivity of 88.9% and a specificity of 75.0%, achieved by the NNET classifier using the texture feature vectors combining wavelet transform and Haralick coefficients. The fact of being able to identify the genetic expression of a tumour without having to perform either a biopsy or a genetic test is a great advantage, because it prevents invasive procedures that involve complications and may present biases in the sample. As well, it leads towards a more personalized and effective treatment. Full article
(This article belongs to the Special Issue Medical Digital Image: Technologies and Applications)
►▼ Show Figures

Figure 1

Review

Jump to: Research

28 pages, 3293 KiB  
Review
Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
by Yuliana Jiménez-Gaona, María José Rodríguez-Álvarez and Vasudevan Lakshminarayanan
Appl. Sci. 2020, 10(22), 8298; https://doi.org/10.3390/app10228298 - 23 Nov 2020
Cited by 50 | Viewed by 8941
Abstract
This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize [...] Read more.
This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies. Full article
(This article belongs to the Special Issue Medical Digital Image: Technologies and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop