LEADER 04951nam 2200469 450 001 996490353203316 005 20230226101854.0 010 $a3-031-17117-9 035 $a(CKB)5850000000078621 035 $a(MiAaPQ)EBC7102049 035 $a(Au-PeEL)EBL7102049 035 $a(PPN)264953452 035 $a(EXLCZ)995850000000078621 100 $a20230226d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aPerinatal, preterm and paediatric image analysis $e7th international workshop, PIPPI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, proceedings /$fedited by Roxane Licandro [and four others] 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$dİ2022 215 $a1 online resource (127 pages) 225 1 $aLecture Notes in Computer Science Ser. ;$vv.13575 311 $a3-031-17116-0 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Organization -- Contents -- Automatic Segmentation of the Placenta in BOLD MRI Time Series -- 1 Introduction -- 2 Methods -- 2.1 Model -- 2.2 Additive Boundary Loss -- 2.3 Implementation Details -- 3 Model Evaluation -- 3.1 Data -- 3.2 Evaluation -- 3.3 Results -- 4 Discussion and Conclusion -- References -- A Fast Anatomical and Quantitative MRI Fetal Exam at Low Field -- 1 Introduction -- 2 Methods -- 2.1 Evaluation -- 2.2 Analysis -- 3 Results -- 4 Discussion and Conclusions -- References -- Automatic Fetal Fat Quantification from MRI -- 1 Introduction -- 2 Methodology -- 2.1 Semi-automatic Fetal AT Segmentation -- 2.2 Automatic Fetal Fat Segmentation -- 3 Experimental Results -- 3.1 Study 1: Manual and Semi-automatic Observer Variability -- 3.2 Study 2: Automatic Fetal AT Segmentation -- 3.3 Study 3: Analysis of Manual Corrections Following Automatic Segmentation -- 4 Discussion -- 5 Conclusion -- References -- Continuous Longitudinal Fetus Brain Atlas Construction via Implicit Neural Representation -- 1 Introduction -- 2 Method -- 2.1 Pre-train Stage -- 2.2 Refine Stage -- 2.3 Inference Stage -- 3 Experiments -- 3.1 Setup -- 3.2 Results -- 4 Conclusion -- References -- Automated Segmentation of Cervical Anatomy to Interrogate Preterm Birth -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 Model Architecture -- 3 Results -- 4 Conclusion -- References -- Deep Learning Framework for Real-Time Fetal Brain Segmentation in MRI -- 1 Introduction -- 2 Materials and Methods -- 2.1 Proposed Network Architecture -- 2.2 Alternative Methods and Evaluation Metrics -- 2.3 Data, Implementation, and Training -- 3 Results -- 4 Analysis and Discussion -- 5 Conclusion -- References -- Attention-Driven Multi-channel Deformable Registration of Structural and Microstructural Neonatal Data -- 1 Introduction -- 2 Method -- 3 Results. 327 $a4 Conclusion -- References -- Automated Multi-class Fetal Cardiac Vessel Segmentation in Aortic Arch Anomalies Using T2-Weighted 3D Fetal MRI -- 1 Introduction -- 1.1 Deep Learning Segmentation -- 1.2 Label Propagation -- 1.3 Contribution -- 2 Methods -- 2.1 Data Specifications -- 2.2 Deep Learning Segmentation Framework -- 2.3 Label Propagation -- 2.4 Attention U-Net Segmentation -- 3 Results -- 3.1 Preliminary Network Architecture Experiments -- 3.2 Test Set and Experiments -- 3.3 Quantitative Results -- 3.4 Visual Inspection -- 4 Discussion -- 5 Conclusion -- References -- Segmentation of Periventricular White Matter in Neonatal Brain MRI: Analysis of Brain Maturation in Term and Preterm Cohorts -- 1 Introduction -- 2 Methods -- 2.1 Cohort, Datasets and Preprocessing -- 2.2 Parcellation Map of Periventricular WM ROIs in the Atlas Space -- 2.3 Automated Segmentation of Periventricular WM ROIs -- 2.4 Quantitative Analysis of PWM in Term and Preterm Cohorts -- 3 Results and Discussion -- 3.1 Parcellation Map of Periventricular WM ROIs in the Atlas Space -- 3.2 Automated Segmentation of Periventricular WM ROIs -- 3.3 Quantitative Analysis of PWM in Term and Preterm Cohorts -- 4 Conclusions -- References -- Knowledge-Guided Segmentation of Isointense Infant Brain -- 1 Introduction -- 2 Methodology -- 2.1 Dataset and Atlas -- 2.2 Data Preparation -- 2.3 Deep Learning Network -- 2.4 Implementation Details -- 3 Experiments and Results -- 3.1 iSeg19 Validation Dataset -- 4 Discussion and Conclusions -- References -- Author Index. 410 0$aLecture Notes in Computer Science Ser. 606 $aElectronic data processing 606 $aPunched card systems 615 0$aElectronic data processing. 615 0$aPunched card systems. 676 $a943.005 702 $aLicandro$b Roxane 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996490353203316 996 $aPerinatal, preterm and paediatric image analysis$93039147 997 $aUNISA