LEADER 04316nam 2201081z- 450 001 9910595073603321 005 20231214133444.0 035 $a(CKB)5680000000080790 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/92116 035 $a(EXLCZ)995680000000080790 100 $a20202209d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aImage Processing and Analysis for Preclinical and Clinical Applications 210 $aBasel$cMDPI Books$d2022 215 $a1 electronic resource (228 p.) 311 $a3-0365-5014-3 311 $a3-0365-5013-5 330 $aRadiomics 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. 606 $aResearch & information: general$2bicssc 606 $aChemistry$2bicssc 610 $adeep learning 610 $asegmentation 610 $aprostate 610 $aMRI 610 $aENet 610 $aUNet 610 $aERFNet 610 $aradiomics 610 $agamma knife 610 $aimaging quantification 610 $a[11C]-methionine positron emission tomography 610 $acancer 610 $aatrial fibrillation 610 $a4D-flow 610 $astasis 610 $apulmonary vein ablation 610 $aconvolutional neural network 610 $atransfer learning 610 $amaxillofacial fractures 610 $acomputed tomography images 610 $aradiography 610 $axenotransplant 610 $acancer cells 610 $azebrafish image analysis 610 $ain vivo assay 610 $aconvolutional neural network (CNN) 610 $amagnetic resonance imaging (MRI) 610 $aneoadjuvant chemoradiation therapy (nCRT) 610 $apathologic complete response (pCR) 610 $arectal cancer 610 $aradiomics feature robustness 610 $aPET/MRI co-registration 610 $aimage registration 610 $afundus image 610 $afeature extraction 610 $aglomerular filtration rate 610 $aGate's method 610 $arenal depth 610 $acomputed tomography 610 $acomputer-aided diagnosis 610 $amedical-image analysis 610 $aautomated prostate-volume estimation 610 $aabdominal ultrasound images 610 $aimage-patch voting 610 $asoft tissue sarcoma 610 $avolume estimation 610 $aartificial intelligence 610 $aBasal Cell Carcinoma 610 $askin lesion 610 $aclassification 610 $acolon 610 $apositron emission tomography-computed tomography 610 $anuclear medicine 610 $aimage pre-processing 610 $ahigh-level synthesis 610 $aX-ray pre-processing 610 $apipelined architecture 615 7$aResearch & information: general 615 7$aChemistry 700 $aStefano$b Alessandro$4edt$01322442 702 $aComelli$b Albert$4edt 702 $aVernuccio$b Federica$4edt 702 $aStefano$b Alessandro$4oth 702 $aComelli$b Albert$4oth 702 $aVernuccio$b Federica$4oth 906 $aBOOK 912 $a9910595073603321 996 $aImage Processing and Analysis for Preclinical and Clinical Applications$93035007 997 $aUNINA