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Lingxiao Luo

function2

  •  China
  •  Tsinghua University
  •  Computer Science and Technology
Statistics
  • Member for 2 years
  • 42 challenge submissions
  • 1 algorithms run

Activity Overview

VESSEL12 Logo
VESSEL12
Challenge User

The VESSEL12 challenge compares methods for automatic (and semi-automatic) segmentation of blood vessels in the lungs from CT images.

CAUSE07 Logo
CAUSE07
Challenge User

The goal of CAUSE07 is to compare different algorithms to segment the caudate nucleaus from brain MRI scans.

LUNA16 Logo
LUNA16
Challenge User

The LUNA16 challenge: automatic nodule detection on chest CT

SLIVER07 Logo
SLIVER07
Challenge User

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

IDRiD Logo
IDRiD
Challenge User

This challenge evaluates automated techniques for analysis of fundus photographs. We target segmentation of retinal lesions like exudates, microaneurysms, and hemorrhages and detection of the optic disc and fovea. Also, we seek grading of fundus images according to the severity level of DR and DME.

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DRIVE
Challenge User

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

REFUGE Logo
REFUGE
Challenge User

The goal of the Retinal Fundus Glaucoma Challenge (REFUGE) is to evaluate and compare automated algorithms for glaucoma detection and optic disc/cup segmentation on a common dataset of retinal fundus images.

PROSTATEx Logo
PROSTATEx
Challenge User

Classification of clinical significance of prostate lesions using multi-parametric MRI data

CHAOS Logo
CHAOS
Challenge User

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

iChallenges Logo
iChallenges
Challenge User

We organized a serial of challenges on different eye image modalities, such as REFUGE, PALM, RETOUCH, etc.

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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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iChallenge-AMD
Challenge User

Age-related Macular Degeneration Challenge

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RibFrac
Challenge User

Rib Fracture Detection and Classification Challenge: A large-scale benchmark of 660 CT scans with ~5,000 rib fractures (around 80Gb)

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COVID-19 LUNG CT LESION SEGMENTATION CHALLENGE - 2020
Challenge User

This challenge will create the platform to evaluate emerging methods for the segmentation and quantification of lung lesions caused by SARS-CoV-2 infection from CT images.

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Cross-Modality Domain Adaptation Image Segmentation - 2021
Challenge User

The CrossMoDA challenge 2021 introduces the first large and multi-class medical dataset for unsupervised cross-modality Domain Adaptation.

BrainPTM-2021 Logo
BrainPTM 2021
Challenge User

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

FLARE Logo
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

QUBIQ21 Logo
QUBIQ2021
Challenge User

Quantification of Uncertainties in Biomedical Image Segmentation Challenge 2021

STOIC2021 Logo
STOIC2021 - COVID-19 AI Challenge
Challenge User

COVID-19 Artificial Intelligence Challenge: automated diagnosis, and prognostic evaluation based on computed tomography

PI-CAI Logo
The PI-CAI Challenge
Challenge User

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI

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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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MICCAI FLARE 2022
Challenge User

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

AMOS22 Logo
Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022
Challenge User

vessel-wall-segmentation-2022 Logo
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.

ATM22 Logo
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.

isles22 Logo
Ischemic Stroke Lesion Segmentation Challenge
Challenge User

2023PAIP Logo
PAIP 2023: TC prediction in pancreatic and colon cancer
Challenge User

Tumor cellularity prediction in pancreatic cancer (supervised learning) and colon cancer (transfer learning)

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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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.

ARCADE Logo
ARCADE-MICCAI2023
Challenge User

MultiCenterAorta Logo
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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Universal Lesion Segmentation Challenge '23
Challenge User