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Biomedical image registration, domain generalisation and out-of-distribution analysis : MICCAI 2021 Challenges, MIDOG 2021, MOOD 2021, and Learn2Reg 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27-October 1, 2021, proceedings / / Marc Aubreville, David Zimmerer, Mattias P. Heinrich, editors
| Biomedical image registration, domain generalisation and out-of-distribution analysis : MICCAI 2021 Challenges, MIDOG 2021, MOOD 2021, and Learn2Reg 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27-October 1, 2021, proceedings / / Marc Aubreville, David Zimmerer, Mattias P. Heinrich, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (201 pages) |
| Disciplina | 616.07540285 |
| Collana | Lecture notes in computer science |
| Soggetto topico | Diagnostic imaging - Data processing |
| ISBN | 3-030-97281-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464536003316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Biomedical image registration, domain generalisation and out-of-distribution analysis : MICCAI 2021 Challenges, MIDOG 2021, MOOD 2021, and Learn2Reg 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27-October 1, 2021, proceedings / / Marc Aubreville, David Zimmerer, Mattias P. Heinrich, editors
| Biomedical image registration, domain generalisation and out-of-distribution analysis : MICCAI 2021 Challenges, MIDOG 2021, MOOD 2021, and Learn2Reg 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27-October 1, 2021, proceedings / / Marc Aubreville, David Zimmerer, Mattias P. Heinrich, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (201 pages) |
| Disciplina | 616.07540285 |
| Collana | Lecture notes in computer science |
| Soggetto topico | Diagnostic imaging - Data processing |
| ISBN | 3-030-97281-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910551826103321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Segmentation, classification, and registration of multi-modality medical imaging data : miccai 2020 challenges, abcs 2020, l2r 2020, tn-scui 2020, held in conjunction with miccai 2020, lima, peru, october 4-8, 2020, proceedings / / edited by Nadya Shusharina, Mattias P. Heinrich, Ruobing Huang
| Segmentation, classification, and registration of multi-modality medical imaging data : miccai 2020 challenges, abcs 2020, l2r 2020, tn-scui 2020, held in conjunction with miccai 2020, lima, peru, october 4-8, 2020, proceedings / / edited by Nadya Shusharina, Mattias P. Heinrich, Ruobing Huang |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (XIX, 156 p. 57 illus., 54 illus. in color.) |
| Disciplina | 616.0754 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico | Optical data processing |
| ISBN | 3-030-71827-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | ABCs – Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images -- Cross-modality Brain Structures Image Segmentation for the Radiotherapy Target Definition and Plan Optimization -- Domain Knowledge Driven Multi-modal Segmentation of Anatomical Brain Barriers to Cancer Spread -- Ensembled ResUnet for Anatomical Brain Barriers Segmentation -- An Enhanced Coarse-to-_ne Framework for the segmentation of clinical target volume -- Automatic Segmentation of brain structures for treatment planning optimization and target volume definition -- A Bi-Directional, Multi-Modality Framework for Segmentation of Brain Structures -- L2R – Learn2Reg: Multitask and Multimodal 3D Medical Image Registration -- Large Deformation Image Registration with Anatomy-aware Laplacian Pyramid Networks -- Discrete Unsupervised 3D Registration Methods for the Learn2Reg Challenge -- Variable Fraunhofer MEVIS RegLib comprehensively applied to Learn2Reg Challenge -- Learning a deformable registration pyramid -- Deep learning based registration using spatial gradients and noisy segmentation labels -- Multi-step, Learning-based, Semi-supervised Image Registration Algorithm -- Using Elastix to register inhale/exhale intrasubject thorax CT: a unsupervised baseline to the task 2 of the Learn2Reg challenge -- TN-SCUI – Thyroid Nodule Segmentation and Classification in Ultrasound Images -- Cascade Unet and CH-Unet for thyroid nodule segmenation and benign and malignant classification -- Identifying Thyroid Nodules in Ultrasound Images through Segmentation-guided Discriminative Localization -- Cascaded Networks for Thyroid Nodule Diagnosis from Ultrasound Images -- Automatic Segmentation and Classification of Thyroid Nodules in Ultrasound Images with Convolutional Neural Networks -- LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to Iteratively Refine the Segmentation of Thyroid Nodule in Ultrasound Images -- Coarse to Fine Ensemble Network for Thyroid Nodule Segmentation. |
| Record Nr. | UNINA-9910484610403321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Segmentation, classification, and registration of multi-modality medical imaging data : miccai 2020 challenges, abcs 2020, l2r 2020, tn-scui 2020, held in conjunction with miccai 2020, lima, peru, october 4-8, 2020, proceedings / / edited by Nadya Shusharina, Mattias P. Heinrich, Ruobing Huang
| Segmentation, classification, and registration of multi-modality medical imaging data : miccai 2020 challenges, abcs 2020, l2r 2020, tn-scui 2020, held in conjunction with miccai 2020, lima, peru, october 4-8, 2020, proceedings / / edited by Nadya Shusharina, Mattias P. Heinrich, Ruobing Huang |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (XIX, 156 p. 57 illus., 54 illus. in color.) |
| Disciplina | 616.0754 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico | Optical data processing |
| ISBN | 3-030-71827-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | ABCs – Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images -- Cross-modality Brain Structures Image Segmentation for the Radiotherapy Target Definition and Plan Optimization -- Domain Knowledge Driven Multi-modal Segmentation of Anatomical Brain Barriers to Cancer Spread -- Ensembled ResUnet for Anatomical Brain Barriers Segmentation -- An Enhanced Coarse-to-_ne Framework for the segmentation of clinical target volume -- Automatic Segmentation of brain structures for treatment planning optimization and target volume definition -- A Bi-Directional, Multi-Modality Framework for Segmentation of Brain Structures -- L2R – Learn2Reg: Multitask and Multimodal 3D Medical Image Registration -- Large Deformation Image Registration with Anatomy-aware Laplacian Pyramid Networks -- Discrete Unsupervised 3D Registration Methods for the Learn2Reg Challenge -- Variable Fraunhofer MEVIS RegLib comprehensively applied to Learn2Reg Challenge -- Learning a deformable registration pyramid -- Deep learning based registration using spatial gradients and noisy segmentation labels -- Multi-step, Learning-based, Semi-supervised Image Registration Algorithm -- Using Elastix to register inhale/exhale intrasubject thorax CT: a unsupervised baseline to the task 2 of the Learn2Reg challenge -- TN-SCUI – Thyroid Nodule Segmentation and Classification in Ultrasound Images -- Cascade Unet and CH-Unet for thyroid nodule segmenation and benign and malignant classification -- Identifying Thyroid Nodules in Ultrasound Images through Segmentation-guided Discriminative Localization -- Cascaded Networks for Thyroid Nodule Diagnosis from Ultrasound Images -- Automatic Segmentation and Classification of Thyroid Nodules in Ultrasound Images with Convolutional Neural Networks -- LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to Iteratively Refine the Segmentation of Thyroid Nodule in Ultrasound Images -- Coarse to Fine Ensemble Network for Thyroid Nodule Segmentation. |
| Record Nr. | UNISA-996464400803316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||