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Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis [[electronic resource] ] : First International Workshop, DATRA 2018 and Third International Workshop, PIPPI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / / edited by Andrew Melbourne, Roxane Licandro, Matthew DiFranco, Paolo Rota, Melanie Gau, Martin Kampel, Rosalind Aughwane, Pim Moeskops, Ernst Schwartz, Emma Robinson, Antonios Makropoulos
| Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis [[electronic resource] ] : First International Workshop, DATRA 2018 and Third International Workshop, PIPPI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / / edited by Andrew Melbourne, Roxane Licandro, Matthew DiFranco, Paolo Rota, Melanie Gau, Martin Kampel, Rosalind Aughwane, Pim Moeskops, Ernst Schwartz, Emma Robinson, Antonios Makropoulos |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XI, 180 p. 74 illus.) |
| Disciplina | 616.07540285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Optical data processing Health informatics Arithmetic and logic units, Computer Artificial Intelligence Image Processing and Computer Vision Health Informatics Arithmetic and Logic Structures |
| ISBN | 3-030-00807-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | DeepCS: Deep Convolutional Neural Network and SVM based Single Image Super-Resolution -- Automatic Segmentation of Thigh Muscle in Longitudinal 3D T1-Weighted Magnetic Resonance (MR) Images -- Detecting Bone Lesions in Multiple Myeloma Patient Using Transfer Learning -- Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response -- Optimizing External Surface Sensor Locations for Respiratory Tumor Motion Prediction -- Segmentation of Fetal Adipose Tissue Using Efficient CNNs for Portable Ultrasound -- Automatic Shadow Detection in 2D Ultrasound Images -- Multi-Channel Groupwise Registration to Construct and Ultrasound-Specific Fetal Brain Atlas -- Investigating Brain Age Deviation in Preterm Infants: A Deep Learning Approach -- Segmentation of Pelvic Vessels in Pediatric MRI Using a Patch-Based Deep Learning Approach -- Multi-View Image Reconstruction: Application to Fetal Ultrasound Compounding -- EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging Without External Trackers -- Better Feature Matching for Placental Panorama Construction -- Combining Deep Learning and Multi-Atlas Label Fusion for Automated Placenta Segmentation from 3DUS -- LSTM Spatial Co-transformer Networks for Registration of 3D Fetal US and MR Brain Images -- Automatic and Efficient Standard Plane Recognition in Fetal Ultrasound Images via Multi-Scale Dense Networks -- Paediatric Liver Segmentation for Low-Contrast CT Images. |
| Record Nr. | UNISA-996466200903316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis : First International Workshop, DATRA 2018 and Third International Workshop, PIPPI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / / edited by Andrew Melbourne, Roxane Licandro, Matthew DiFranco, Paolo Rota, Melanie Gau, Martin Kampel, Rosalind Aughwane, Pim Moeskops, Ernst Schwartz, Emma Robinson, Antonios Makropoulos
| Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis : First International Workshop, DATRA 2018 and Third International Workshop, PIPPI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / / edited by Andrew Melbourne, Roxane Licandro, Matthew DiFranco, Paolo Rota, Melanie Gau, Martin Kampel, Rosalind Aughwane, Pim Moeskops, Ernst Schwartz, Emma Robinson, Antonios Makropoulos |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XI, 180 p. 74 illus.) |
| Disciplina |
616.07540285
616.0757 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Optical data processing Health informatics Arithmetic and logic units, Computer Artificial Intelligence Image Processing and Computer Vision Health Informatics Arithmetic and Logic Structures |
| ISBN | 3-030-00807-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | DeepCS: Deep Convolutional Neural Network and SVM based Single Image Super-Resolution -- Automatic Segmentation of Thigh Muscle in Longitudinal 3D T1-Weighted Magnetic Resonance (MR) Images -- Detecting Bone Lesions in Multiple Myeloma Patient Using Transfer Learning -- Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response -- Optimizing External Surface Sensor Locations for Respiratory Tumor Motion Prediction -- Segmentation of Fetal Adipose Tissue Using Efficient CNNs for Portable Ultrasound -- Automatic Shadow Detection in 2D Ultrasound Images -- Multi-Channel Groupwise Registration to Construct and Ultrasound-Specific Fetal Brain Atlas -- Investigating Brain Age Deviation in Preterm Infants: A Deep Learning Approach -- Segmentation of Pelvic Vessels in Pediatric MRI Using a Patch-Based Deep Learning Approach -- Multi-View Image Reconstruction: Application to Fetal Ultrasound Compounding -- EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging Without External Trackers -- Better Feature Matching for Placental Panorama Construction -- Combining Deep Learning and Multi-Atlas Label Fusion for Automated Placenta Segmentation from 3DUS -- LSTM Spatial Co-transformer Networks for Registration of 3D Fetal US and MR Brain Images -- Automatic and Efficient Standard Plane Recognition in Fetal Ultrasound Images via Multi-Scale Dense Networks -- Paediatric Liver Segmentation for Low-Contrast CT Images. |
| Record Nr. | UNINA-9910349404603321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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]
| 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] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (127 pages) |
| Disciplina | 943.005 |
| Collana | Lecture Notes in Computer Science Ser. |
| Soggetto topico |
Electronic data processing
Punched card systems |
| ISBN | 3-031-17117-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNISA-996490353203316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
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, Andrew Melbourne, Esra Abaci Turk, Christopher Macgowan, Jana Hutter
| 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, Andrew Melbourne, Esra Abaci Turk, Christopher Macgowan, Jana Hutter |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (127 pages) |
| Disciplina | 943.005 |
| 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 | 3-031-17117-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNINA-9910595031203321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis [[electronic resource] ] : First International Workshop, SUSI 2019, and 4th International Workshop, PIPPI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings / / edited by Qian Wang, Alberto Gomez, Jana Hutter, Kristin McLeod, Veronika Zimmer, Oliver Zettinig, Roxane Licandro, Emma Robinson, Daan Christiaens, Esra Abaci Turk, Andrew Melbourne
| Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis [[electronic resource] ] : First International Workshop, SUSI 2019, and 4th International Workshop, PIPPI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings / / edited by Qian Wang, Alberto Gomez, Jana Hutter, Kristin McLeod, Veronika Zimmer, Oliver Zettinig, Roxane Licandro, Emma Robinson, Daan Christiaens, Esra Abaci Turk, Andrew Melbourne |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XVII, 190 p. 97 illus., 68 illus. in color.) |
| Disciplina | 618.207543 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Optical data processing Application software Computer organization Artificial Intelligence Computer Imaging, Vision, Pattern Recognition and Graphics Computer Applications Computer Systems Organization and Communication Networks |
| ISBN | 3-030-32875-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | First Workshop on Smart UltraSound Imaging -- Straight to the point: reinforcement learning for user guidance in ultrasound -- Registration of Untracked 2D Laparoscopic Ultrasound Liver Images to CT using Content-based Retrieval and Kinematic Priors -- Direct Detection and Measurement of Nuchal Translucency with Neural Networks from Ultrasound Images -- Automated left ventricle dimension measurement in 2D cardiac ultrasound via an anatomically meaningful CNN approach -- SPRNet: Automatic Fetal Standard Plane Recognition Network for Ultrasound Images -- Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound -- Adversarial Learning for Deformable Image Registration: Application to 3D Ultrasound Image Fusion -- Monitoring Achilles tendon healing progress in ultrasound imaging with convolutional neural networks -- Deep Learning-based Pneumothorax Detection in Ultrasound Videos -- Deep Learning Based Minimum Variance Beamforming for Ultrasound Imaging -- 4th Workshop on Perinatal, Preterm and Paediatric Image Analysis -- Estimation of preterm birth markers with U-Net segmentation network -- Investigating Image Registration Impact on Preterm Birth Classification: An Interpretable Deep Learning Approach -- Dual Network Generative Adversarial Networks for Pediatric Echocardiography Segmentation -- Reproducibility of Functional Connectivity Estimates in Motion Corrected Fetal fMRI -- Plug-and-Play Priors for Reconstruction-based Placental Image Registration -- A Longitudinal Study of the Evolution of the Central Sulcus’ Shape in Preterm Infants using Manifold Learning -- Prediction of failure of induction of labor (IOL) from ultrasound images using radioman features -- Longitudinal analysis of fetal MRI in patients with prenatal spina bifida repair -- Quantifying Residual Motion Artifacts in Fetal fMRI Data -- Topology-preserving augmentation for CNN-based segmentation of congenital heart defects from 3D paediatric CMR. |
| Record Nr. | UNISA-996466302703316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis : First International Workshop, SUSI 2019, and 4th International Workshop, PIPPI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings / / edited by Qian Wang, Alberto Gomez, Jana Hutter, Kristin McLeod, Veronika Zimmer, Oliver Zettinig, Roxane Licandro, Emma Robinson, Daan Christiaens, Esra Abaci Turk, Andrew Melbourne
| Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis : First International Workshop, SUSI 2019, and 4th International Workshop, PIPPI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings / / edited by Qian Wang, Alberto Gomez, Jana Hutter, Kristin McLeod, Veronika Zimmer, Oliver Zettinig, Roxane Licandro, Emma Robinson, Daan Christiaens, Esra Abaci Turk, Andrew Melbourne |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XVII, 190 p. 97 illus., 68 illus. in color.) |
| Disciplina |
618.207543
616.07543 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Image processing - Digital techniques Computer vision Application software Computer engineering Computer networks Artificial Intelligence Computer Imaging, Vision, Pattern Recognition and Graphics Computer and Information Systems Applications Computer Engineering and Networks |
| ISBN | 3-030-32875-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | First Workshop on Smart UltraSound Imaging -- Straight to the point: reinforcement learning for user guidance in ultrasound -- Registration of Untracked 2D Laparoscopic Ultrasound Liver Images to CT using Content-based Retrieval and Kinematic Priors -- Direct Detection and Measurement of Nuchal Translucency with Neural Networks from Ultrasound Images -- Automated left ventricle dimension measurement in 2D cardiac ultrasound via an anatomically meaningful CNN approach -- SPRNet: Automatic Fetal Standard Plane Recognition Network for Ultrasound Images -- Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound -- Adversarial Learning for Deformable Image Registration: Application to 3D Ultrasound Image Fusion -- Monitoring Achilles tendon healing progress in ultrasound imaging with convolutional neural networks -- Deep Learning-based Pneumothorax Detection in Ultrasound Videos -- Deep Learning Based Minimum Variance Beamforming for Ultrasound Imaging -- 4th Workshop on Perinatal, Preterm and Paediatric Image Analysis -- Estimation of preterm birth markers with U-Net segmentation network -- Investigating Image Registration Impact on Preterm Birth Classification: An Interpretable Deep Learning Approach -- Dual Network Generative Adversarial Networks for Pediatric Echocardiography Segmentation -- Reproducibility of Functional Connectivity Estimates in Motion Corrected Fetal fMRI -- Plug-and-Play Priors for Reconstruction-based Placental Image Registration -- A Longitudinal Study of the Evolution of the Central Sulcus’ Shape in Preterm Infants using Manifold Learning -- Prediction of failure of induction of labor (IOL) from ultrasound images using radioman features -- Longitudinal analysis of fetal MRI in patients with prenatal spina bifida repair -- Quantifying Residual Motion Artifacts in Fetal fMRI Data -- Topology-preserving augmentation for CNN-based segmentation of congenital heart defects from 3D paediatric CMR. |
| Record Nr. | UNINA-9910349272603321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||