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BioMedInformatics
Special Issues
Advances in Quantitative Imaging Analysis: From Theory to Practice
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Special Issue "Advances in Quantitative Imaging Analysis: From Theory to Practice"

A special issue of BioMedInformatics (ISSN 2673-7426). This special issue belongs to the section "Imaging Informatics".

Deadline for manuscript submissions: 30 November 2023 | Viewed by 1924

Special Issue Editors

Dr. Federico Mastroleo

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Guest Editor
1. Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
2. Department of Translational Medicine, University of Piemonte Orientale (UPO), Via Solaroli 17, 28100 Novara, Italy
Interests: radiotherapy; artificial intelligence; machine learning; process mining; radiomics
Special Issues, Collections and Topics in MDPI journals
Dr. Angela Ammirabile

E-Mail Website
Guest Editor
1. Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
2. Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
Interests: radiology; abdominal radiology; thoracic radiology; radiomics; quantitative imaging analysis; machine learning
Dr. Giulia Marvaso

E-Mail Website
Guest Editor
Department of Radiotherapy, European Institute of Oncology (IEO) IRCCS, 20141 Milan, Italy
Interests: urological malignancies; radiation oncology; new fractionation protocols; treatment accuracy; patient’s quality of life; prognostic and predictive factors; SBRT hypofractionation; oligometastatic disease
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The special issue on Quantitative Analysis of Imaging seeks to bring together the latest innovations and advancements in the field of imaging analysis. With a focus on quantitative methods for the extraction of meaningful information from images, this special issue will cover a wide range of imaging modalities, including microscopy, medical imaging, and remote sensing.

The articles in this special issue will showcase the latest developments in the field of quantitative imaging analysis, covering both theoretical and practical aspects. Experts in the field will present their work on various topics, including image segmentation, registration, feature extraction, pattern recognition, and image-based modeling. The articles will highlight the challenges and opportunities in this rapidly evolving field, while promoting interdisciplinary collaboration among researchers from different fields.

The special issue on Quantitative Analysis of Imaging will provide a comprehensive overview of the current state of the art in quantitative imaging analysis, and aims to promote further research and development in this field. By presenting the latest advancements in imaging analysis, this special issue will serve as a valuable resource for researchers, practitioners, and students interested in this field.

Dr. Federico Mastroleo
Dr. Angela Ammirabile
Dr. Giulia Marvaso
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. BioMedInformatics is an international peer-reviewed open access quarterly 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 1000 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

  • quantitative analysis
  • imaging
  • image processing
  • computer vision
  • biomedical engineering
  • image segmentation
  • pattern recognition
  • radiomics

Published Papers (2 papers)

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Article
Multimodal Deep Learning Methods on Image and Textual Data to Predict Radiotherapy Structure Names
by
Priyankar Bose
,
Pratip Rana
,
William C. Sleeman IV
,
Sriram Srinivasan
,
Rishabh Kapoor
,
Jatinder Palta
and
Preetam Ghosh
BioMedInformatics 2023, 3(3), 493-513; https://doi.org/10.3390/biomedinformatics3030034 - 25 Jun 2023
Viewed by 761
Abstract
Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs), and ‘Other’ organs is a vital problem. This [...] Read more.
Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs), and ‘Other’ organs is a vital problem. This paper presents novel deep learning methods on structure sets by integrating multimodal data compiled from the radiotherapy centers of the US Veterans Health Administration (VHA) and Virginia Commonwealth University (VCU). These de-identified data comprise 16,290 prostate structures. Our method integrates the multimodal textual and imaging data with Convolutional Neural Network (CNN)-based deep learning approaches such as CNN, Visual Geometry Group (VGG) network, and Residual Network (ResNet) and shows improved results in prostate radiotherapy structure name standardization. Evaluation with macro-averaged F1 score shows that our model with single-modal textual data usually performs better than previous studies. The models perform well on textual data alone, while the addition of imaging data shows that deep neural networks achieve better performance using information present in other modalities. Additionally, using masked images and masked doses along with text leads to an overall performance improvement with the CNN-based architectures than using all the modalities together. Undersampling the majority class leads to further performance enhancement. The VGG network on the masked image-dose data combined with CNNs on the text data performs the best and presents the state-of-the-art in this domain. Full article
(This article belongs to the Special Issue Advances in Quantitative Imaging Analysis: From Theory to Practice)
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Article
Generation of Musculoskeletal Ultrasound Images with Diffusion Models
by
Sofoklis Katakis
,
Nikolaos Barotsis
,
Alexandros Kakotaritis
,
Panagiotis Tsiganos
,
George Economou
,
Elias Panagiotopoulos
and
George Panayiotakis
BioMedInformatics 2023, 3(2), 405-421; https://doi.org/10.3390/biomedinformatics3020027 - 23 May 2023
Viewed by 850
Abstract
The recent advances in deep learning have revolutionised computer-aided diagnosis in medical imaging. However, deep learning approaches to unveil their full potential require significant amounts of data, which can be a challenging task in some scientific fields, such as musculoskeletal ultrasound imaging, in [...] Read more.
The recent advances in deep learning have revolutionised computer-aided diagnosis in medical imaging. However, deep learning approaches to unveil their full potential require significant amounts of data, which can be a challenging task in some scientific fields, such as musculoskeletal ultrasound imaging, in which data privacy and security reasons can lead to important limitations in the acquisition and the distribution process of patients’ data. For this reason, different generative methods have been introduced to significantly reduce the required amount of real data by generating synthetic images, almost indistinguishable from the real ones. In this study, the power of the diffusion models is incorporated for the generation of realistic data from a small set of musculoskeletal ultrasound images in four different muscles. Afterwards, the similarity of the generated and real images is assessed with different types of qualitative and quantitative metrics that correspond well with human judgement. In particular, the histograms of pixel intensities of the two sets of images have demonstrated that the two distributions are statistically similar. Additionally, the well-established LPIPS, SSIM, FID, and PSNR metrics have been used to quantify the similarity of these sets of images. The two sets of images have achieved extremely high similarity scores in all these metrics. Subsequently, high-level features are extracted from the two types of images and visualized in a two-dimensional space for inspection of their structure and to identify patterns. From this representation, the two sets of images are hard to distinguish. Finally, we perform a series of experiments to assess the impact of the generated data for training a highly efficient Attention-UNet for the important clinical application of muscle thickness measurement. Our results depict that the synthetic data play a significant role in the model’s final performance and can lead to the improvement of the deep learning systems in musculoskeletal ultrasound. Full article
(This article belongs to the Special Issue Advances in Quantitative Imaging Analysis: From Theory to Practice)
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