- Grand Challenge

Sergi Pujades

spujades

France
INRIA / Université Grenoble Alpes
MORPHEO
Website

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Member for 4 years, 8 months

Activity Overview

VerSe`19

Challenge User

Vertebrae labelling and segmentation on a spine dataset on an unprecedented 150 CT scans with voxel-level vertebral annotations.

LNDb Challenge

Challenge User

Lung cancer screening and Fleischner follow-up determination in chest CT through nodule detection, segmentation and characterization

VerSe'20

Challenge User

Vertebrae labelling and segmentation on a multi-centre, multi-scanner, and anatomically-diverse CT dataset.

RibFrac

Challenge User

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

CT diagnosis of COVID-19

Challenge User

Coronavirus disease 2019 (COVID-19) has infected more than 1.3 million individuals all over the world and caused more than 106,000 deaths. One major hurdle in controlling the spreading of this disease is the inefficiency and shortage of medical tests. To mitigate the inefficiency and shortage of existing tests for COVID-19, we propose this competition to encourage the development of effective Deep Learning techniques to diagnose COVID-19 based on CT images. The problem we want to solve is to classify each CT image into positive COVID-19 (the image has clinical findings of COVID-19) or negative COVID-19 ( the image does not have clinical findings of COVID-19). It’s a binary classification problem based on CT images.

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.

AutoImplant 2021

Challenge User

Second MICCAI Challenge on Automatic Cranial Implant Design (AutoImplant 2021)

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.

Vertebra segmentation and labeling

Algorithm User

Segmentation and labeling of the vertebrae in CT scans with arbitrary field of view.