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Chanwoong Lee

cwlg102

  •  South Korea
  •  Yonsei University College of Medicine
  •  Dept. of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute
Statistics
  • Member for 1 year, 10 months
  • 98 challenge submissions
  • 114 algorithms run

Activity Overview

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CHAOS
Challenge User

In this challenge, you segment the liver in CT data, and segment liver, spleen, and kidneys in MRI data.

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Decathlon
Challenge User

The Medical Segmentation Decathlon challenge tests the generalisability of machine learning algorithms when applied to 10 different semantic segmentation task.

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SynthRAD2023
Challenge User

SynthRAD is the first challenge on automatic generation of synthetic computed tomography (sCT) for radiotherapy

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autoPET
Challenge User

Automatic lesion segmentation in whole-body FDG-PET/CT

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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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SPIDER
Challenge User

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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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PANORAMA
Challenge User

Artificial Intelligence and Radiologists at Pancreatic Cancer Diagnosis in CT

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Universal Lesion Segmentation Challenge '23
Challenge User

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AutoPET III
Challenge User

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Ischemic Stroke Lesion Segmentation Challenge 2024
Challenge User

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Pelvic Bone Fragments with Injuries Segmentation Challenge
Challenge User

Pelvic fracture segmentation in CT and X-ray

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Multi-Class Segmentation of Aortic Branches and Zones in CTA
Challenge User

3D Segmentation of Aortic Branches and Zones on Computed Tomography Angiography (CTA)

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Automated Identification of Mod-Sev TBI Lesions
Challenge User

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SynthRAD2025
Challenge User

SynthRAD is the first challenge on automatic generation of synthetic computed tomography (sCT) for radiotherapy

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Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation
Challenge User

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Calibration and Uncertainty for multiRater Volume Assessment in
Challenge User