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Diagnostics
Special Issues
Quantitative Biomedical Imaging for Personalized Diagnosis
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Quantitative Biomedical Imaging for Personalized Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 5424

Special Issue Editor

Prof. Dr. Isabella Castiglioni

E-Mail Website
Guest Editor
Physics Applied to Medicine and Biology, University of Milan-Bicocca, Milan, Italy
Interests: medical imaging diagnostics; radiomics; radiogenomics

Special Issue Information

Dear colleagues,

Modern medical imaging techniques, including ultrasound, radiographs, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), have now evolved to accommodate an unprecedented level of spatial and functional information. In parallel, the acknowledged importance of disease biomarkers for diagnosis is leading current medical imaging to request quantitative methods to support a variety of clinical and research goals, including early diagnosis, differential diagnosis, and diagnostic classification of disease subtypes.

We invite authors to contribute to this Special Issue with original research or review articles that illustrate and stimulate the use of quantitative methods and biomarkers in medical imaging for the early and/or differential diagnosis of multifactorial diseases, such as cancer, neurodegeneration, cardiovascular diseases, and viral diseases.

Topics of particular interest to this Special Issue include, but are not limited to:

  • development, standardization, optimization, and application of methods for image quantitation, image analytics, and image classification for diagnosis;
  • application of numerical/statistical features from medical images for quantitative image diagnostic biomarkers; and
  • validation of quantitative image biomarkers against relevant biological and clinical data.

Prof. Dr. Isabella Castiglioni
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. Diagnostics 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 2600 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.

Published Papers (2 papers)

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Research

16 pages, 2455 KiB  
Article
Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation
by Amirreza Mahbod, Gerald Schaefer, Christine Löw, Georg Dorffner, Rupert Ecker and Isabella Ellinger
Diagnostics 2021, 11(6), 967; https://doi.org/10.3390/diagnostics11060967 - 27 May 2021
Cited by 4 | Viewed by 2821
Abstract
Nuclei instance segmentation can be considered as a key point in the computer-mediated analysis of histological fluorescence-stained (FS) images. Many computer-assisted approaches have been proposed for this task, and among them, supervised deep learning (DL) methods deliver the best performances. An important criterion [...] Read more.
Nuclei instance segmentation can be considered as a key point in the computer-mediated analysis of histological fluorescence-stained (FS) images. Many computer-assisted approaches have been proposed for this task, and among them, supervised deep learning (DL) methods deliver the best performances. An important criterion that can affect the DL-based nuclei instance segmentation performance of FS images is the utilised image bit depth, but to our knowledge, no study has been conducted so far to investigate this impact. In this work, we released a fully annotated FS histological image dataset of nuclei at different image magnifications and from five different mouse organs. Moreover, by different pre-processing techniques and using one of the state-of-the-art DL-based methods, we investigated the impact of image bit depth (i.e., eight bits vs. sixteen bits) on the nuclei instance segmentation performance. The results obtained from our dataset and another publicly available dataset showed very competitive nuclei instance segmentation performances for the models trained with 8 bit and 16 bit images. This suggested that processing 8 bit images is sufficient for nuclei instance segmentation of FS images in most cases. The dataset including the raw image patches, as well as the corresponding segmentation masks is publicly available in the published GitHub repository. Full article
(This article belongs to the Special Issue Quantitative Biomedical Imaging for Personalized Diagnosis)
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17 pages, 1596 KiB  
Article
Ultrasonography in the Diagnosis of Adnexal Lesions: The Role of Texture Analysis
by Paul-Andrei Ștefan, Roxana-Adelina Lupean, Carmen Mihaela Mihu, Andrei Lebovici, Mihaela Daniela Oancea, Liviu Hîțu, Daniel Duma and Csaba Csutak
Diagnostics 2021, 11(5), 812; https://doi.org/10.3390/diagnostics11050812 - 29 Apr 2021
Cited by 16 | Viewed by 2032
Abstract
The classic ultrasonographic differentiation between benign and malignant adnexal masses encounters several limitations. Ultrasonography-based texture analysis (USTA) offers a new perspective, but its role has been incompletely evaluated. This study aimed to further investigate USTA’s capacity in differentiating benign from malignant adnexal tumors, [...] Read more.
The classic ultrasonographic differentiation between benign and malignant adnexal masses encounters several limitations. Ultrasonography-based texture analysis (USTA) offers a new perspective, but its role has been incompletely evaluated. This study aimed to further investigate USTA’s capacity in differentiating benign from malignant adnexal tumors, as well as comparing the workflow and the results with previously-published research. A total of 123 adnexal lesions (benign, 88; malignant, 35) were retrospectively included. The USTA was performed on dedicated software. By applying three reduction techniques, 23 features with the highest discriminatory potential were selected. The features’ ability to identify ovarian malignancies was evaluated through univariate, multivariate, and receiver operating characteristics analyses, and also by the use of the k-nearest neighbor (KNN) classifier. Three parameters were independent predictors for ovarian neoplasms (sum variance, and two variations of the sum of squares). Benign and malignant lesions were differentiated with 90.48% sensitivity and 93.1% specificity by the prediction model (which included the three independent predictors), and with 71.43–80% sensitivity and 87.5–89.77% specificity by the KNN classifier. The USTA shows statistically significant differences between the textures of the two groups, but it is unclear whether the parameters can reflect the true histopathological characteristics of adnexal lesions. Full article
(This article belongs to the Special Issue Quantitative Biomedical Imaging for Personalized Diagnosis)
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