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Image Processing and Analysis for Preclinical and Clinical Applications

Edited by
August 2022
228 pages
  • ISBN978-3-0365-5013-8 (Hardback)
  • ISBN978-3-0365-5014-5 (PDF)
https://doi.org/10.3390/books978-3-0365-5014-5
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This book is a reprint of the Special Issue Image Processing and Analysis for Preclinical and Clinical Applications that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
deep learning; segmentation; prostate; MRI; ENet; UNet; ERFNet; radiomics; gamma knife; imaging quantification; [11C]-methionine positron emission tomography; cancer; atrial fibrillation; 4D-flow; stasis; pulmonary vein ablation; convolutional neural network; transfer learning; maxillofacial fractures; computed tomography images; radiography; xenotransplant; cancer cells; zebrafish image analysis; in vivo assay; convolutional neural network (CNN); magnetic resonance imaging (MRI); neoadjuvant chemoradiation therapy (nCRT); pathologic complete response (pCR); radiomics; rectal cancer; radiomics feature robustness; imaging quantification; [11C]-methionine positron emission tomography; PET/MRI co-registration; image registration; fundus image; feature extraction; glomerular filtration rate; Gate’s method; renal depth; computed tomography; computer-aided diagnosis; medical-image analysis; automated prostate-volume estimation; abdominal ultrasound images; image-patch voting; deep learning; soft tissue sarcoma; volume estimation; segmentation; artificial intelligence; Basal Cell Carcinoma; deep learning; convolutional neural network; skin lesion; segmentation; classification; colon; cancer; radiomics; artificial intelligence; positron emission tomography-computed tomography; nuclear medicine; computed tomography; image pre-processing; high-level synthesis; X-ray pre-processing; pipelined architecture; n/a