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Perinatal, Preterm and Paediatric Image Analysis [[electronic resource] ] : 8th International Workshop, PIPPI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings / / edited by Daphna Link-Sourani, Esra Abaci Turk, Christopher Macgowan, Jana Hutter, Andrew Melbourne, Roxane Licandro
| Perinatal, Preterm and Paediatric Image Analysis [[electronic resource] ] : 8th International Workshop, PIPPI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings / / edited by Daphna Link-Sourani, Esra Abaci Turk, Christopher Macgowan, Jana Hutter, Andrew Melbourne, Roxane Licandro |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (128 pages) |
| Disciplina | 618.9200754 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Artificial intelligence Application software Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence Computer and Information Systems Applications |
| ISBN | 3-031-45544-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Fetal Brain Image Analysis -- FetMRQC: Automated Quality Control for Fetal Brain MRI -- 1 Introduction -- 2 Methodology -- 2.1 Data -- 2.2 Manual QA of Fetal MRI Stacks -- 2.3 IQMs Extraction and Learning -- 3 Results and Discussion -- 4 Conclusion -- References -- A Deep Learning Approach for Segmenting the Subplate and Proliferative Zones in Fetal Brain MRI -- 1 Introduction -- 2 Methods -- 2.1 Cohort, Datasets and Preprocessing -- 2.2 Neuroanatomical Parcellation of Transient Regions -- 2.3 Automated Segmentation of Transient Regions -- 2.4 Qualitative and Quantitative Analyses of Transient Regions -- 3 Results and Discussion -- 3.1 Qualitative Analysis of Transient Regions in Atlas Space -- 3.2 Quantitative Analyses of Transient Regions in Subject Space -- 3.3 Quantitative Comparison of Transient Volumes in Fetuses with Ventriculomegaly and Controls -- 4 Conclusion and Future Work -- References -- Combined Quantitative T2* Map and Structural T2-Weighted Tissue-Specific Analysis for Fetal Brain MRI: Pilot Automated Pipeline -- 1 Introduction -- 2 Methods -- 2.1 Datasets, Acquisition and Pre-processing -- 2.2 Automated 3D T2* Fetal Brain Reconstruction in T2w Space -- 2.3 Automated 3D T2* Fetal Brain Tissue Segmentation -- 2.4 Analysis of Brain Development in Combined T2w+T2* Datasets -- 2.5 Implementation Details -- 3 Experiments and Results -- 3.1 Automated 3D T2* Fetal Brain Reconstruction in T2w Space -- 3.2 Automated 3D T2* Fetal Brain Tissue Segmentation -- 3.3 Analysis of Brain Development in T2w+T2* Datasets -- 4 Discussion and Conclusions -- References -- Quantitative T2 Relaxometry in Fetal Brain: Validation Using Modified FaBiaN Fetal Brain MRI Simulator -- 1 Introduction -- 2 Methodology -- 2.1 Quantitative T2 Measurement Framework for Fetal MRI -- 2.2 Overview of the FaBiAN Phantom.
2.3 The Fetal Brain Model -- 2.4 Modelling Fetal Motion -- 2.5 Modelling Signals with Slice Profiles -- 2.6 Sampling K-Space -- 2.7 Modelling the Signals for the Dictionary -- 2.8 Simulated Experiments -- 2.9 Fetal Brain Measurements -- 3 Results -- 3.1 Simulated Fetal MRI -- 3.2 Reconstruction of T2 Maps from Simulated Fetal Data -- 3.3 Fetal Measurements -- 4 Discussion -- 5 Conclusion -- References -- Fetal Cardiac Image Analysis -- Towards Automatic Risk Prediction of Coarctation of the Aorta from Fetal CMR Using Atlas-Based Segmentation and Statistical Shape Modelling -- 1 Introduction -- 1.1 Contributions -- 2 Methods -- 2.1 Dataset Description -- 2.2 Automated Segmentation -- 2.3 Statistical Shape Analysis -- 3 Results -- 3.1 Segmentation -- 3.2 Statistical Shape Analysis -- 4 Discussion -- 5 Conclusion -- References -- The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Placental and Cervical Image Analysis -- Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series -- 1 Introduction -- 2 Methods -- 2.1 Consistency Regularization Loss -- 2.2 Siamese Neural Network -- 2.3 Implementation Details -- 3 Experiments -- 3.1 Dataset -- 3.2 Baseline Methods -- 3.3 Evaluation -- 3.4 Results -- 4 Limitations and Future Work -- 5 Conclusions -- References -- Visualization and Quantification of Placental Vasculature Using MRI -- 1 Introduction -- 1.1 MRI Acquisitions and Differing Contrasts -- 2 Methods -- 2.1 Data Acquisition -- 2.2 Image Quantification Metrics -- 2.3 Quantification of Vessel Segmentation -- 2.4 Statistics -- 2.5 Segmentation Performance Evaluation -- 3 Results -- 3.1 Validation from Micro-CT -- 4 Discussion -- 5 Conclusion -- References. The Comparison Analysis of the Cervical Features Between Second-and Third-Trimester Pregnancy in Ultrasound Images Using eXplainable AI -- 1 Introduction -- 2 Method -- 2.1 eXplainable Artificial Intelligence(XAI) - CAM Based Methods -- 2.2 Deep Neural Network Model for Classification Task -- 2.3 Dataset and Preprocessing -- 2.4 Experimental Design -- 3 Results -- 3.1 Comparison of Heatmap Between Second- And Third-Trimester -- 3.2 Difference in Heatmap with and Without Fetal Head -- 3.3 Cross Validation Using Another Institution -- 4 Discussion and Conclusion -- References -- Infant Video Analysis -- Automatic Infant Respiration Estimation from Video: A Deep Flow-Based Algorithm and a Novel Public Benchmark -- 1 Introduction -- 2 Related Work -- 3 AIR-125: An Annotated Infant Respiration Dataset -- 4 Methodology -- 4.1 AirFlowNet Architecture -- 4.2 Spectral Bandpass Loss -- 5 Evaluation and Results -- 5.1 Experimental Setup -- 5.2 Results and Analysis -- 6 Conclusion -- References -- Author Index. |
| Record Nr. | UNISA-996558468803316 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Perinatal, Preterm and Paediatric Image Analysis : 8th International Workshop, PIPPI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings / / edited by Daphna Link-Sourani, Esra Abaci Turk, Christopher Macgowan, Jana Hutter, Andrew Melbourne, Roxane Licandro
| Perinatal, Preterm and Paediatric Image Analysis : 8th International Workshop, PIPPI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings / / edited by Daphna Link-Sourani, Esra Abaci Turk, Christopher Macgowan, Jana Hutter, Andrew Melbourne, Roxane Licandro |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (128 pages) |
| Disciplina | 618.9200754 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Artificial intelligence Application software Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence Computer and Information Systems Applications |
| ISBN | 3-031-45544-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Fetal Brain Image Analysis -- FetMRQC: Automated Quality Control for Fetal Brain MRI -- 1 Introduction -- 2 Methodology -- 2.1 Data -- 2.2 Manual QA of Fetal MRI Stacks -- 2.3 IQMs Extraction and Learning -- 3 Results and Discussion -- 4 Conclusion -- References -- A Deep Learning Approach for Segmenting the Subplate and Proliferative Zones in Fetal Brain MRI -- 1 Introduction -- 2 Methods -- 2.1 Cohort, Datasets and Preprocessing -- 2.2 Neuroanatomical Parcellation of Transient Regions -- 2.3 Automated Segmentation of Transient Regions -- 2.4 Qualitative and Quantitative Analyses of Transient Regions -- 3 Results and Discussion -- 3.1 Qualitative Analysis of Transient Regions in Atlas Space -- 3.2 Quantitative Analyses of Transient Regions in Subject Space -- 3.3 Quantitative Comparison of Transient Volumes in Fetuses with Ventriculomegaly and Controls -- 4 Conclusion and Future Work -- References -- Combined Quantitative T2* Map and Structural T2-Weighted Tissue-Specific Analysis for Fetal Brain MRI: Pilot Automated Pipeline -- 1 Introduction -- 2 Methods -- 2.1 Datasets, Acquisition and Pre-processing -- 2.2 Automated 3D T2* Fetal Brain Reconstruction in T2w Space -- 2.3 Automated 3D T2* Fetal Brain Tissue Segmentation -- 2.4 Analysis of Brain Development in Combined T2w+T2* Datasets -- 2.5 Implementation Details -- 3 Experiments and Results -- 3.1 Automated 3D T2* Fetal Brain Reconstruction in T2w Space -- 3.2 Automated 3D T2* Fetal Brain Tissue Segmentation -- 3.3 Analysis of Brain Development in T2w+T2* Datasets -- 4 Discussion and Conclusions -- References -- Quantitative T2 Relaxometry in Fetal Brain: Validation Using Modified FaBiaN Fetal Brain MRI Simulator -- 1 Introduction -- 2 Methodology -- 2.1 Quantitative T2 Measurement Framework for Fetal MRI -- 2.2 Overview of the FaBiAN Phantom.
2.3 The Fetal Brain Model -- 2.4 Modelling Fetal Motion -- 2.5 Modelling Signals with Slice Profiles -- 2.6 Sampling K-Space -- 2.7 Modelling the Signals for the Dictionary -- 2.8 Simulated Experiments -- 2.9 Fetal Brain Measurements -- 3 Results -- 3.1 Simulated Fetal MRI -- 3.2 Reconstruction of T2 Maps from Simulated Fetal Data -- 3.3 Fetal Measurements -- 4 Discussion -- 5 Conclusion -- References -- Fetal Cardiac Image Analysis -- Towards Automatic Risk Prediction of Coarctation of the Aorta from Fetal CMR Using Atlas-Based Segmentation and Statistical Shape Modelling -- 1 Introduction -- 1.1 Contributions -- 2 Methods -- 2.1 Dataset Description -- 2.2 Automated Segmentation -- 2.3 Statistical Shape Analysis -- 3 Results -- 3.1 Segmentation -- 3.2 Statistical Shape Analysis -- 4 Discussion -- 5 Conclusion -- References -- The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Placental and Cervical Image Analysis -- Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series -- 1 Introduction -- 2 Methods -- 2.1 Consistency Regularization Loss -- 2.2 Siamese Neural Network -- 2.3 Implementation Details -- 3 Experiments -- 3.1 Dataset -- 3.2 Baseline Methods -- 3.3 Evaluation -- 3.4 Results -- 4 Limitations and Future Work -- 5 Conclusions -- References -- Visualization and Quantification of Placental Vasculature Using MRI -- 1 Introduction -- 1.1 MRI Acquisitions and Differing Contrasts -- 2 Methods -- 2.1 Data Acquisition -- 2.2 Image Quantification Metrics -- 2.3 Quantification of Vessel Segmentation -- 2.4 Statistics -- 2.5 Segmentation Performance Evaluation -- 3 Results -- 3.1 Validation from Micro-CT -- 4 Discussion -- 5 Conclusion -- References. The Comparison Analysis of the Cervical Features Between Second-and Third-Trimester Pregnancy in Ultrasound Images Using eXplainable AI -- 1 Introduction -- 2 Method -- 2.1 eXplainable Artificial Intelligence(XAI) - CAM Based Methods -- 2.2 Deep Neural Network Model for Classification Task -- 2.3 Dataset and Preprocessing -- 2.4 Experimental Design -- 3 Results -- 3.1 Comparison of Heatmap Between Second- And Third-Trimester -- 3.2 Difference in Heatmap with and Without Fetal Head -- 3.3 Cross Validation Using Another Institution -- 4 Discussion and Conclusion -- References -- Infant Video Analysis -- Automatic Infant Respiration Estimation from Video: A Deep Flow-Based Algorithm and a Novel Public Benchmark -- 1 Introduction -- 2 Related Work -- 3 AIR-125: An Annotated Infant Respiration Dataset -- 4 Methodology -- 4.1 AirFlowNet Architecture -- 4.2 Spectral Bandpass Loss -- 5 Evaluation and Results -- 5.1 Experimental Setup -- 5.2 Results and Analysis -- 6 Conclusion -- References -- Author Index. |
| Record Nr. | UNINA-9910751388203321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||