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Computation
Special Issues
Computational Medical Image Analysis—2nd Edition
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Computational Medical Image Analysis—2nd Edition

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Biology".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 3493

Special Issue Editor

Dr. Anando Sen

E-Mail Website
Guest Editor
John Walton Muscular Dystrophy Research Centre, Translational and Clinical Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne NE1 3BZ, UK
Interests: medical imaging; mathematical modelling; image quality; pathology correlation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is my great pleasure to invite you to contribute to this Special Issue of Computation, titled “Computational Medical Image Analysis—2nd Edition”. It is devoted to understanding the modern methodologies used in a variety of medical imaging applications. Colleagues from all over the world are invited to submit their manuscripts. These papers will follow a rigorous peer-review process to satisfy a high standard of publication.

Computational methods are extensively used in medical image analysis. With the development of high-performance systems as well as methodologies that can harness the power of these systems (e.g., machine learning and deep learning), this is an exciting era for imaging research. With novel methodologies, it has been possible to provide previously unfathomable solutions to important problems. In this Special Issue, we hope to put together a collection of such methods.

The scope of this Special Issue is vast. The application must be clinically relevant and patient-oriented. The use of both synthetic and real data is acceptable. Applications from a diverse range of imaging modalities, including CT, MR, SPECT, PET, ultrasound, photoacoustic, and digital pathology, are encouraged. Topics for this Special Issue include, but are not limited to, the following:

  • Image processing;
  • Dual and multi-modality imaging;
  • Image segmentation;
  • Image registration;
  • Tomographic reconstruction;
  • Image quality assessment;
  • Digital pathology applications;
  • Dosimetry;
  • Radiation oncology applications;
  • Machine learning;
  • Neural networks.

Dr. Anando Sen
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. Computation is an international peer-reviewed open access monthly 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 1800 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

  • computational imaging
  • medical imaging
  • anatomical imaging
  • functional imaging
  • machine learning
  • neural networks
  • digital pathology

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Related Special Issue

Published Papers (2 papers)

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Research

22 pages, 13050 KiB  
Article
A Deep Learning Model for Detecting Fake Medical Images to Mitigate Financial Insurance Fraud
by Muhammad Asad Arshed, Shahzad Mumtaz, Ștefan Cristian Gherghina, Neelam Urooj, Saeed Ahmed and Christine Dewi
Computation 2024, 12(9), 173; https://doi.org/10.3390/computation12090173 - 29 Aug 2024
Viewed by 1807
Abstract
Artificial Intelligence and Deepfake Technologies have brought a new dimension to the generation of fake data, making it easier and faster than ever before—this fake data could include text, images, sounds, videos, etc. This has brought new challenges that require the faster development [...] Read more.
Artificial Intelligence and Deepfake Technologies have brought a new dimension to the generation of fake data, making it easier and faster than ever before—this fake data could include text, images, sounds, videos, etc. This has brought new challenges that require the faster development of tools and techniques to avoid fraudulent activities at pace and scale. Our focus in this research study is to empirically evaluate the use and effectiveness of deep learning models such as Convolutional Neural Networks (CNNs) and Patch-based Neural Networks in the context of successful identification of real and fake images. We chose the healthcare domain as a potential case study where the fake medical data generation approach could be used to make false insurance claims. For this purpose, we obtained publicly available skin cancer data and used recently introduced stable diffusion approaches—a more effective technique than prior approaches such as Generative Adversarial Network (GAN)—to generate fake skin cancer images. To the best of our knowledge, and based on the literature review, this is one of the few research studies that uses images generated using stable diffusion along with real image data. As part of the exploratory analysis, we analyzed histograms of fake and real images using individual color channels and averaged across training and testing datasets. The histogram analysis demonstrated a clear change by shifting the mean and overall distribution of both real and fake images (more prominent in blue and green) in the training data whereas, in the test data, both means were different from the training data, so it appears to be non-trivial to set a threshold which could give better predictive capability. We also conducted a user study to observe where the naked eye could identify any patterns for classifying real and fake images, and the accuracy of the test data was observed to be 68%. The adoption of deep learning predictive approaches (i.e., patch-based and CNN-based) has demonstrated similar accuracy (~100%) in training and validation subsets of the data, and the same was observed for the test subset with and without StratifiedKFold (k = 3). Our analysis has demonstrated that state-of-the-art exploratory and deep-learning approaches are effective enough to detect images generated from stable diffusion vs. real images. Full article
(This article belongs to the Special Issue Computational Medical Image Analysis—2nd Edition)
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9 pages, 5096 KiB  
Article
Ultrashort Echo Time and Fast Field Echo Imaging for Spine Bone Imaging with Application in Spondylolysis Evaluation
by Diana Vucevic, Vadim Malis, Yuichi Yamashita, Anya Mesa, Tomosuke Yamaguchi, Suraj Achar, Mitsue Miyazaki and Won C. Bae
Computation 2024, 12(8), 152; https://doi.org/10.3390/computation12080152 - 24 Jul 2024
Cited by 1 | Viewed by 1173
Abstract
Isthmic spondylolysis is characterized by a stress injury to the pars interarticularis bones of the lumbar spines and is often missed by conventional magnetic resonance imaging (MRI), necessitating a computed tomography (CT) for accurate diagnosis. We compare MRI techniques suitable for producing CT-like [...] Read more.
Isthmic spondylolysis is characterized by a stress injury to the pars interarticularis bones of the lumbar spines and is often missed by conventional magnetic resonance imaging (MRI), necessitating a computed tomography (CT) for accurate diagnosis. We compare MRI techniques suitable for producing CT-like images. Lumbar spines of asymptomatic and low back pain (LBP) subjects were imaged at 3-Tesla with multi-echo ultrashort echo time (UTE) and field echo (FE) sequences followed by simple post-processing of averaging and inverting to depict spinal bones with a CT-like appearance. The contrast-to-noise ratio (CNR) for bone was determined to compare UTE vs. FE and single-echo vs. multi-echo data. Visually, both sequences depicted cortical bone with good contrast; UTE-processed sequences provided a flatter contrast for soft tissues that made them easy to distinguish from bone, while FE-processed images had better resolution and bone–muscle contrast, which are important for fracture detection. Additionally, multi-echo images provided significantly (p = 0.03) greater CNR compared with single-echo images. Using these techniques, progressive spondylolysis was detected in an LBP subject. This study demonstrates the feasibility of using spine bone MRI to yield CT-like contrast. Through the employment of multi-echo UTE and FE sequences combined with simple processing, we observe sufficient enhancements in image quality and contrast to detect pars fractures. Full article
(This article belongs to the Special Issue Computational Medical Image Analysis—2nd Edition)
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