Perinatal, preterm and paediatric image analysis : 7th international workshop, PIPPI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, proceedings / / edited by Roxane Licandro [and four others]

• Pubblicazione: Cham, Switzerland : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (127 pages)
• Disciplina: 943.005
• Soggetto topico: Electronic data processing; Punched card systems
• Persona (resp. second.): LicandroRoxane
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- 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. ; 4 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.
• Titolo autorizzato: Perinatal, preterm and paediatric image analysis
• ISBN: 3-031-17117-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996490353203316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Computer Science Ser.