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Data from TCIA collections have and continue to be used for image analysis challenges or competitions, e.g., image segmentation or tumor classification.  Specific challenges leveraging our data are described below. Please note that the challenges are not managed by TCIA staff, and in many cases you will be sent to web sites that are not affiliated with TCIA to learn more about them.

ISBI 2018 - Lung Nodule Malignancy Prediction Challenge

This challenge intends to advance methods development on the current clinical impediment to assess nodules status for lung cancer screening subjects with consecutive scans.

IEEE VIP Cup 2018: Lung Cancer Radiomics-Tumor Region Segmentation

Segmentation and prediction are considered as critical steps among different processing tasks within the Radiomics pipeline, and are the focus of this competition. The 2018 VIP-CUP challenge is on segmentation and prediction of Lung Cancer Tumor region via screening Computed Tomography (CT) scans. Images from several patients along with the annotations will be provided for training and validation purposes. The evaluation will be performed based on test sets provided closer to the submission deadline.

Multimodal Brain Tumor Segmentation Challenge 2018 (BraTS)

BraTS 2018 utilizes multi-institutional pre-operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Furthemore, to pinpoint the clinical relevance of this segmentation task, BraTS’18 also focuses on the prediction of patient overall survival, via integrative analyses of radiomic features and machine learning algorithms.  More information can be found at http://www.med.upenn.edu/sbia/brats2018.html.

MICCAI 2018 – Computational Precision Medicine

The Computational Precision Medicine (CPM) 2018 will be held on September 16, in Granada (Spain), in conjunction with MICCAI 2018. It will consist of a morning workshop and afternoon challenges. (further details will be provided in early June)

Data Science Bowl 2017

In the United States, lung cancer strikes 225,000 people every year, and accounts for $12 billion in health care costs. Early detection is critical to give patients the best chance at recovery and survival. Using a data set of thousands of high-resolution lung scans provided by the National Cancer Institute, participants developed algorithms that accurately determine when lesions in the lungs are cancerous. This will dramatically reduce the false positive rate that plagues the current detection technology, get patients earlier access to life-saving interventions, and give radiologists more time to spend with their patients. The challenge was hosted on Kaggle at: https://www.kaggle.com/c/data-science-bowl-2017.

PROSTATEx-2 Challenge 2017

The American Association of Physicists in Medicine (AAPM), along with the SPIE (the international society for optics and photonics) and the National Cancer Institute (NCI), will conduct a part 2 “Grand Challenge” on the development of quantitative multi-parametric magnetic resonance imaging (MRI) biomarkers for the determination of Gleason Grade Group in prostate cancer. As part of the 2017 AAPM Annual Meeting, the PROSTATEx-2 Challenge will provide a unique opportunity for participants to compare their algorithms with those of others from academia, industry, and government in a structured, direct way using the same data sets.  To learn more about the challenge please visit http://www.aapm.org/GrandChallenge/PROSTATEx-2/default.asp. To register for the challenge visit http://spiechallenges.cloudapp.net/.

PROSTATEx Challenge 2017

SPIE, along with the support of the American Association of Physicists in Medicine (AAPM) and the National Cancer Institute (NCI), will conduct a “Grand Challenge” on quantitative image analysis methods for the diagnostic classification of clinically significant prostate lesions. As part of the 2017 SPIE Medical Imaging Symposium, the PROSTATEx Challenge will provide a unique opportunity for participants to compare their algorithms with those of others from academia, industry, and government in a structured, direct way using the same data sets. To learn more about the challenge please visit http://spie.org/x115569.xml?wt.mc_id=rmi17gb.  

MICCAI 2016 – Computational Precision Medicine

The Computational Precision Medicine (CPM) will be a full-day satellite event held on October 21 in Athens, Greece at MICCAI 2016, composed of short workshops on advances in radio-path-omics and radiomics, and innovative challenges in CT radiomics, classification and nuclei segmentation in digital pathology, and mammographic CAD detection.

MICCAI 2015 – Computational Brain Tumor Cluster of Events (CBTC)

The Computational Brain Tumor Cluster of Event (CBTC) 2015 will be held on Oct 9 in Munich, Germany, in conjunction with MICCAI 2015. It will consist of a morning workshop and afternoon challenges. (see preliminary program here)

LUNGx SPIE-AAPM-NCI Lung Nodule Classification Challenge

As part of the 2015 SPIE Medical Imaging Conference, SPIE – with the support of American Association of Physicists in Medicine (AAPM) and the National Cancer Institute (NCI) – will conduct a “Grand Challenge” on quantitative image analysis methods for the diagnostic classification of malignant and benign lung nodules. The LUNGx Challenge will provide a unique opportunity for participants to compare their algorithms to those of others from academia, industry, and government in a structured, direct way using the same data sets.

QIN Lung CT Segmentation Challenge

The goal of the CT segmentation challenge was to compare the bias (where possible) and repeatability of automatic, semi-automatic and manual segmentations for lung CT studies. Investigators from Columbia, MGH, Moffitt and Stanford identified 52 lung CT nodules and made available the data in DICOM format. Algorithm developers and users were requested to submit at least 4 repetitions of their algorithm for each nodule. A variety of image formats for the segmentation volumes were utilized including NIFTI, NRRD, JPG, PNG, DICOM-SEG, DICOM-RT, AIM, and LIDC-XML.  The results were ultimately converted into DICOM-SEG format and uploaded back to TCIA.

MICCAI 2014 Grand Challenges

MICCAI 2014 will provide an excellent opportunity for a day long cluster of events in brain tumor computation (September 14, 2014). It will be composed of a workshop and radiologic and pathology image processing challenges that discuss and showcase the value of open science in addressing some of the challenges of Big Data in the context of brain cancer.

NCI-MICCAI 2013 Grand Challenges in Image Segmentation

The National Cancer Institute’s (NCI’s) Cancer Imaging Program in collaboration with the 16th international conference on Medical Image Computing and Computer Assisted Interventions (MICCAI) 2013 has launched two grand segmentation challenges involving clinically relevant prostate structures and brain tumor components based on magnetic resonance imaging (MRI) data. The event will take place at MICCAI 2013 meeting (http://www.miccai2013.org/) on September 22 in Nagoya,Japan.

NCI-ISBI 2013 Challenge - Automated Segmentation of Prostate Structures

The National Cancer Institute’s (NCI's) Cancer Imaging Program in collaboration with the International Society for Biomedical Imaging (ISBI) has launched a grand challenge involving prostate gland magnetic resonance imaging (MRI) data. The challenge will take place at the ISBI Symposium, April 7-11, 2013 in San Francisco, CA.

 

 

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