LEADER 06417nam 22006495 450 001 9910595031203321 005 20251225202122.0 010 $a9783031171178 010 $a3031171179 024 7 $a10.1007/978-3-031-17117-8 035 $a(CKB)5850000000078621 035 $a(MiAaPQ)EBC7102049 035 $a(Au-PeEL)EBL7102049 035 $a(DE-He213)978-3-031-17117-8 035 $a(PPN)264953452 035 $a(EXLCZ)995850000000078621 100 $a20220921d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$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, Andrew Melbourne, Esra Abaci Turk, Christopher Macgowan, Jana Hutter 205 $a1st ed. 2022. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2022. 215 $a1 online resource (127 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v13575 311 08$a9783031171161 311 08$a3031171160 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. 330 $aThis book constitutes the refereed proceedings of the First International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2022, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022, in Singapore, Singapore, in September 2021. The 10 full papers and 1 short papers presented at PIPPI 2022 were carefully reviewed and selected from 12 submissions. PIPPI 2022 workshop complements the main MICCAI conference by providing a focused discussion of perinatal and paediatric image analysis, including the application of sophisticated analysis tools to fetal, neonatal and paediatric imaging data. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v13575 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aArtificial intelligence 606 $aApplication software 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aArtificial Intelligence 606 $aComputer and Information Systems Applications 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aApplication software. 615 14$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aArtificial Intelligence. 615 24$aComputer and Information Systems Applications. 676 $a943.005 702 $aLicandro$b Roxane 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910595031203321 996 $aPerinatal, preterm and paediatric image analysis$93039147 997 $aUNINA