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TU‐FG‐207A‐04: Overview of the Low Dose CT Grand Challenge
Author(s) -
McCollough C.
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4957556
Subject(s) - computer science , medical imaging , medical physics , implementation , artificial intelligence , data set , data science , machine learning , medicine , programming language
Peer‐reviewed journals and conference proceedings publish hundreds of papers that describe new medical imaging algorithms, including, for example, techniques for computer‐aided diagnosis or characterization, segmentation, image registration, image reconstruction, and radiomics. It is difficult, if not impossible, to fairly compare the performance of these algorithms as investigators must either use different data sets, or if using the same data, use different implementations of competing algorithms. Grand Challenges facilitate the fair comparison of algorithms by providing a common data set to all participants and by having each participant be responsible for implementation of their own algorithm. The dissemination of findings from Grand Challenges provides important information to the scientific community and helps to determine which approaches have the greatest promise for successful translation to clinical practice. In this session we will review the outcomes and lessons learned from the 2015 SPIE‐AAPM‐NCI Lung Nodule Classification Challenge. We will then turn to the 2016 NIH‐AAPM‐Mayo Clinic Low Dose CT Grand Challenge, providing an overview of denoising and iterative reconstruction approaches and a description of the Challenge. The top 3 performing participants will be announced, and each will give a short presentation on their technique. Learning Objectives: 1. Understand the role of Grand Challenges in the field of medical imaging 2. Be able to summarize the outcomes of the 2015 lung nodule classification challenge 3. Be able to review the primary types of noise reduction techniques used in CT 4. Be familiar with a library of patient CT projection data available to researchers 5. Learn which techniques performed best in the Low Dose CT Grand ChallengePelc: GE Healthcare, Philips Healthcare; McCollough: Research grant, Siemens Healthcare; Low Dose CT Grand Challenge supported by the AAPM Science Council and NIH (grant EB 017185), and hosted by the Mayo Clinic; Giger: stockholder R2 technology/Hologic, royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi/Toshiba. Cofounder/stockholder Quantitative Insights.