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Dinggen Dai

DaiDuncan_3721

  •  China
  •  Shanghai Haohua Technology Co., Ltd
  •  R&D Department
Statistics
  • Member for 2 years, 3 months

Activity Overview

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SLIVER07
Challenge User

The goal of this competition is to compare different algorithms to segment the liver from clinical 3D computed tomography (CT) scans.

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DRIVE
Challenge User

Develop a system to automatically segment vessels in human retina fundus images.

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HC18
Challenge User

Automated measurement of fetal head circumference using 2D ultrasound images

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Learn2Reg
Challenge User

Challenge on medical image registration addressing: learning from small datasets; estimating large deformations; dealing with multi-modal scans; and learning from noisy annotations

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BrainPTM 2021
Challenge User

Brain Pre-surgical Tractography Mapping (BrainPTM) in real clinical scans.

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FLARE21
Challenge User

Fast and Low GPU memory Abdominal oRgan sEgmentation Challenge

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FeTA - Fetal Tissue Annotation Challenge
Challenge User

Fetal Tissue Annotation Challenge

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The PI-CAI Challenge
Challenge User

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI

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MELA2022
Challenge User

MICCAI 2022 MELA Challenge: A Large-Scale Detection Benchmark of 1,100 CT Scans for Mediastinal Lesion Analysis

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KiPA22 (Regular Challenge)
Challenge User

The challenge is aimed to segment kidney, renal tumors, arteries, and veins from computed tomography angiography (CTA) images in one inference.

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INSTANCE2022
Challenge User

The 2022 Intracranial Hemorrhage Segmentation Challenge on Non-Contrast head CT (NCCT)

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ATLAS R2.0 - Stroke Lesion Segmentation
Challenge User

Anatomical Tracings of Lesions After Stroke

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3D Teeth Scan Segmentation and Labeling Challenge MICCAI2022
Challenge User

Computer-aided design (CAD) tools have become increasingly popular in modern dentistry for highly accurate treatment planning. In particular, in orthodontic CAD systems, advanced intraoral scanners (IOSs) are now widely used as they provide precise digital surface models of the dentition. Such models can dramatically help dentists simulate teeth extraction, move, deletion, and rearrangement and therefore ease the prediction of treatment outcomes. Although IOSs are becoming widespread in clinical dental practice, there are only few contributions on teeth segmentation/labeling available in the literature and no publicly available database. A fundamental issue that appears with IOS data is the ability to reliably segment and identify teeth in scanned observations. Teeth segmentation and labelling is difficult as a result of the inherent similarities between teeth shapes as well as their ambiguous positions on jaws.

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MICCAI FLARE 2022
Challenge User

MICCAI 2022 Fast and Low-resource semi-supervised Abdominal oRgan sEgmentation (FLARE) Challenge

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ACROBAT 2023
Challenge User

The ACROBAT challenge aims to advance the development of WSI registration algorithms that can align WSIs of IHC-stained breast cancer tissue sections to corresponding tissue regions that were stained with H&E. All WSIs originate from routine diagnostic workflows.

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Preoperative to Intraoperative Laparoscopy Fusion
Challenge User

Preoperative to Intraoperative Laparoscopy Fusion

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MICCAI HECKTOR 2022
Challenge User

Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

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Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022
Challenge User

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Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis
Challenge User

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Cross-Modality Domain Adaptation: Segmentation & Classification
Challenge User

The CrossMoDA 2022 challenge is the second edition of the first large and multi-class medical dataset for unsupervised cross-modality Domain Adaptation.

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Multi-site, Multi-Domain Airway Tree Modeling (ATM’22)
Challenge User

Airway segmentation is a crucial step for the analysis of pulmonary diseases including asthma, bronchiectasis, and emphysema. The accurate segmentation based on X-Ray computed tomography (CT) enables the quantitative measurements of airway dimensions and wall thickness, which can reveal the abnormality of patients with chronic obstructive pulmonary disease (COPD). Besides, the extraction of patient-specific airway models from CT images is required for navigatiisted surgery.

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Ischemic Stroke Lesion Segmentation Challenge
Challenge User

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SPIDER
Challenge User

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LNQ2023
Challenge User

Accurate lymph node size estimation is critical for staging cancer patients, initial therapeutic management, and in longitudinal scans, assessing response to therapy. Current standard practice for quantifying lymph node size is based on a variety of criteria that use unidirectional or bidirectional measurements on just one or a few nodes, typically on just one axial slice. But humans have hundreds of lymph nodes, any number of which may be enlarged to various degrees due to disease or immune response. While a normal lymph node may be approximately 5mm in diameter, a diseased lymph node may be several cm in diameter. The mediastinum, the anatomical area between the lungs and around the heart, may contain ten or more lymph nodes, often with three or more enlarged greater than 1cm. Accurate segmentation in 3D would provide more information to evaluate lymph node disease.

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Universal Lesion Segmentation Challenge '23
Challenge User

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Ischemic Stroke Lesion Segmentation Challenge 2024
Challenge User