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Zhendong Yang

lemonSoda

  •  China
  •  South Medical University
  •  Faculty of Biomedical Engineering
Statistics
  • Member for 2 years, 8 months

Activity Overview

HaN-Seg2023 Logo
The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge
Challenge User

A semantic multimodal segmentation challenge comprising 30 organs at risk

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autoPET-II
Challenge User

Automated Lesion Segmentation in PET/CT - Domain Generalization

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ToothFairy: Cone-Beam Computed Tomography Segmentation Challenge
Challenge User

This is the first edition of the ToothFairy challenge organized by the University of Modena and Reggio Emilia with the collaboration of Raudboud University. This challenge aims at pushing the development of deep learning frameworks to segment the Inferior Alveolar Canal (IAC) by incrementally extending the amount of publicly available 3D-annotated Cone Beam Computed Tomography (CBCT) scans. CBCT modality is becoming increasingly important for treatment planning and diagnosis in implant dentistry and maxillofacial surgery. The three-dimensional information acquired with CBCT can be crucial to plan a vast number of surgical interventions with the aim of preserving noble anatomical structures such as the Inferior Alveolar Canal (IAC), which contains the homonymous nerve (Inferior Alveolar Nerve, IAN). Deep learning models can support medical personnel in surgical planning procedures by providing a voxel-level segmentation of the IAN automatically extracted from CBCT scans.

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SEG.A. - Segmentation of the Aorta
Challenge User

Segmentation, modeling and visualization of the arterial tree are still a challenge in medical image analysis. The main track of this challenge deals with the fully automatic segmentation of the aortic vessel tree in computed tomography images. Optionally, teams can submit tailored solutions for meshing and visualization of the vessel tree.

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DENTEX - MICCAI23
Challenge User

Dental Enumeration and Diagnosis on Panoramic X- rays Challenge

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SegRap 2023
Challenge User

A segmentation challenge with 200 patients (two modalities of CT images, 45 OARs and 2 GTVs).

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CL-Detection 2023
Challenge User

Cephalometric landmark detection in lateral x-ray images

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Universal Lesion Segmentation Challenge '23
Challenge User

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AutoPET III
Challenge User

ISLES-24 Logo
Ischemic Stroke Lesion Segmentation Challenge 2024
Challenge User

PENGWIN Logo
Pelvic Bone Fragments with Injuries Segmentation Challenge
Challenge User

Pelvic fracture segmentation in CT and X-ray