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弈 吕

ly547361952

Statistics
  • Member for 5 years, 1 month
  • 35 challenge submissions
  • 1 algorithms run

Activity Overview

KiTS21 Sanity Check Logo
KiTS21 Sanity Check
Archive User

The three "sanity check" cases for the KiTS21 submission period

PROMISE12 Logo
PROMISE12
Challenge User

The goal of this challenge is to compare interactive and (semi)-automatic segmentation algorithms for MRI of the prostate.

CHAOS Logo
CHAOS
Challenge User

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

KiTS19 Logo
KiTS19
Challenge User

2019 Kidney and Kidney Tumor Segmentation Challenge

PAIP2019 Logo
PAIP 2019
Challenge User

PAIP2019: Liver Cancer Segmentation Task 1: Liver Cancer Segmentation Task 2: Viable Tumor Burden Estimation

curious2019 Logo
curious2019
Challenge User

MICCAI Challenge 2019 for Correction of Brainshift with Intra-Operative Ultrasound. Taks 1: Register pre-operative MRI to iUS before tumor resection;Taks 2: Register iUS after tumor resection to iUS before tumor resection

odir2019 Logo
ODIR-2019
Challenge User

北京大学国际眼底图像智能识别竞赛 Peking University International Competition on Ocular Disease Intelligent Recognition

kits21 Logo
KiTS21
Challenge User

The 2021 MICCAI Kidney and Kidney Tumor Segmentation challenge

3DTeethSeg Logo
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.

FLARE22 Logo
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

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

SegRap2023 Logo
SegRap 2023
Challenge User

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

Vertebral Abnormality Scoring Logo
Vertebral Abnormality Scoring
Algorithm User

Score from 0 to 100 that expresses how abnormal the shape of a vertebra is

Automatic segmentation of kidney, tumor and cysts Logo
Automatic segmentation of kidney, tumor and cysts
Algorithm Editor