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Produktbild: Supervised and Semi-supervised Multi-structure Segmentation and Landmark Detection in Dental Data
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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

Aus der Reihe Lecture Notes in Computer Science

136,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

17.05.2025

Abbildungen

XVII, 242 p. 77 illus., 72 illus. in color.

Herausgeber

Yaqi Wang + weitere

Verlag

Springer

Seitenzahl

242

Maße (L/B/H)

23,5/15,5/1,5 cm

Gewicht

400 g

Sprache

Englisch

ISBN

978-3-031-88976-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

17.05.2025

Abbildungen

XVII, 242 p. 77 illus., 72 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

242

Maße (L/B/H)

23,5/15,5/1,5 cm

Gewicht

400 g

Sprache

Englisch

ISBN

978-3-031-88976-9

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Supervised and Semi-supervised Multi-structure Segmentation and Landmark Detection in Dental Data

  • ToothFairy2: Multi-Structure Segmentation in CBCT Volumes
    .-


    Inferior Alveolar Nerve Segmentation in CBCT Images Using Connectivity-based Selective Re-training.- Scaling nnU-Net for CBCT Segmentation.- DiENTeS: Dynamic ENTity Segmentation with Local-Global Transformers.- Enhanced Multi-Structure Segmentation in CBCT Images with Adaptive Structure Optimization.- Weakly-Supervised Convolutional Neural Networks for Inferior Alveolar Nerve Segmentation in CBCT images.- A Multi-Axial Network for Oral Structural Segmentation.- Automatic Multi-Structure Segmentation in Cone Beam Computed Tomography Volumes Using Deep Encoder-Decoder Architectures.- Video Foundation Model for Medical 3D Segmentation.-


    STS: Semi-supervised Teeth Segmentation
    .-


    A Two-Stage Semi-Supervised nnU-Net Model for Automated Tooth Segmentation in Panoramic X-ray Images.- Two-Stage Semi-Supervised nnU-Net Framework for Tooth Segmentation in CBCT Images.- SemiT-SAM: Building a Visual Foundation Model for Tooth Instance Segmentation on Panoramic Radiographs.- Multi-stage Dental Visual Detection Based on YOLOv8: Dental 3D CBCT.- Efficient Semi-Supervised Tooth Instance Segmentation in Panoramic X-rays Using ResUnet50 and SAM Networks.- DAE-Net: Dual Attention Embedding-based Tooth Instance Segmentation Approach for Panoramic X-ray Images.- A Self-Training Pipeline for Semi-Supervised 2D Teeth Instance Segmentation.- Deformable Inherent Consistent Learning Network for Accurate Tooth Segmentation in Dental Panoramic Radiographs.- Semi-Supervised 2D Dental Image Segmentation via Cross Teaching Network.- A Novel Two-Stage Approach for 3D Dental Tooth Instance Segmentation.- 


    3DTeethLand24: 3D Teeth Landmarks Detection Challenge.-


    A Two-Stage Framework with Dual-Branch Network for End-to-End 3D Tooth Landmark Detection.- Leveraging Point Transformers for Detecting Anatomical Landmarks in Digital Dentistry.- ToothInstanceNet: Comprehensive Information from Intra-Oral Scans by Integration of Large-Context and High-Resolution Predictions.