Supervised semi supervised multi structure segmentation (6 résultats)
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Taschenbuch. Etat : Neu. Supervised and Semi-supervised Multi-structure Segmentation and Landmark Detection in Dental Data | MICCAI 2024 Challenges: ToothFairy 2024, 3DTeethLand 2024, and STS 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings | Yaqi Wang (u. a.) | Taschenbuch | Lecture N…otes in Computer Science | xvii | Englisch | 2025 | Springer | EAN 9783031889769 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes three challenges that were held in conjunction with the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, on October 6, 2024: ToothFairy challenge(Toot…hFairy2: Multi-Structure Segmentation in CBCT Volumes), Semi-supervised Teeth Segmentation (STS 2024), and the 3DTeethLand (3D Teeth Landmarks Detection Challenge).The 21 papers presented in this volume were carefully reviewed and selected from 28 submissions. ToothFairy challenges focused on the development of deep learning frameworks to segment anatomical structures in CBCTs by incrementally extending the amount of publicly available 3D-annotated CBCT scans and providing the first publicly available fully annotated datasets. The STS Challenge promoted the development of teeth segmentation in panoramic X-ray images and CBCT scans. It also provided instance annotations for different teeth, including pertinent category information. The 3DTeethLand24 Challenge played a key role in advancing automation and leveraging AI to optimize orthodontic treatments. It also aims to tackle the challenge of limited access to data, providing a valuable resource that encourages community engagement in this vital area with potential clinical implications.
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book constitutes three challenges that were held in conjunction with the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, on October 6, 2024: ToothFair…y challenge(ToothFairy2: Multi-Structure Segmentation in CBCT Volumes), Semi-supervised Teeth Segmentation (STS 2024), and the 3DTeethLand (3D Teeth Landmarks Detection Challenge).The 21 papers presented in this volume were carefully reviewed and selected from 28 submissions. ToothFairy challenges focused on the development of deep learning frameworks to segment anatomical structures in CBCTs by incrementally extending the amount of publicly available 3D-annotated CBCT scans and providing the first publicly available fully annotated datasets. The STS Challenge promoted the development of teeth segmentation in panoramic X-ray images and CBCT scans. It also provided instance annotations for different teeth, including pertinent category information. The 3DTeethLand24 Challenge played a key role in advancing automation and leveraging AI to optimize orthodontic treatments. It also aims to tackle the challenge of limited access to data, providing a valuable resource that encourages community engagement in this vital area with potential clinical implications. 260 pp. Englisch.
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book constitutes three challenges that were held in conjunction with the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, on October 6, 2024: ToothFairy ch…allenge(ToothFairy2: Multi-Structure Segmentation in CBCT Volumes), Semi-supervised Teeth Segmentation (STS 2024), and the 3DTeethLand (3D Teeth Landmarks Detection Challenge).The 21 papers presented in this volume were carefully reviewed and selected from 28 submissions. ToothFairy challenges focused on the development of deep learning frameworks to segment anatomical structures in CBCTs by incrementally extending the amount of publicly available 3D-annotated CBCT scans and providing the first publicly available fully annotated datasets. The STS Challenge promoted the development of teeth segmentation in panoramic X-ray images and CBCT scans. It also provided instance annotations for different teeth, including pertinent category information. The 3DTeethLand24 Challenge played a key role in advancing automation and leveraging AI to optimize orthodontic treatments. It also aims to tackle the challenge of limited access to data, providing a valuable resource that encourages community engagement in this vital area with potential clinical implications.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 260 pp. Englisch.
