Image Processing and Analysis for Preclinical and Clinical Applications

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 p...

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Other Authors: Stefano, Alessandro (Editor), Comelli, Albert (Editor), Vernuccio, Federica (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
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DOAB: description of the publication
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520 |a 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. 
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650 7 |a Chemistry  |2 bicssc 
653 |a deep learning 
653 |a segmentation 
653 |a prostate 
653 |a MRI 
653 |a ENet 
653 |a UNet 
653 |a ERFNet 
653 |a radiomics 
653 |a gamma knife 
653 |a imaging quantification 
653 |a [11C]-methionine positron emission tomography 
653 |a cancer 
653 |a atrial fibrillation 
653 |a 4D-flow 
653 |a stasis 
653 |a pulmonary vein ablation 
653 |a convolutional neural network 
653 |a transfer learning 
653 |a maxillofacial fractures 
653 |a computed tomography images 
653 |a radiography 
653 |a xenotransplant 
653 |a cancer cells 
653 |a zebrafish image analysis 
653 |a in vivo assay 
653 |a convolutional neural network (CNN) 
653 |a magnetic resonance imaging (MRI) 
653 |a neoadjuvant chemoradiation therapy (nCRT) 
653 |a pathologic complete response (pCR) 
653 |a rectal cancer 
653 |a radiomics feature robustness 
653 |a PET/MRI co-registration 
653 |a image registration 
653 |a fundus image 
653 |a feature extraction 
653 |a glomerular filtration rate 
653 |a Gate's method 
653 |a renal depth 
653 |a computed tomography 
653 |a computer-aided diagnosis 
653 |a medical-image analysis 
653 |a automated prostate-volume estimation 
653 |a abdominal ultrasound images 
653 |a image-patch voting 
653 |a soft tissue sarcoma 
653 |a volume estimation 
653 |a artificial intelligence 
653 |a Basal Cell Carcinoma 
653 |a skin lesion 
653 |a classification 
653 |a colon 
653 |a positron emission tomography-computed tomography 
653 |a nuclear medicine 
653 |a image pre-processing 
653 |a high-level synthesis 
653 |a X-ray pre-processing 
653 |a pipelined architecture 
653 |a n/a 
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