Literature DB >> 22075810

Towards a repository for standardized medical image and signal case data annotated with ground truth.

Thomas M Deserno1, Petra Welter, Alexander Horsch.   

Abstract

Validation of medical signal and image processing systems requires quality-assured, representative and generally acknowledged databases accompanied by appropriate reference (ground truth) and clinical metadata, which are composed laboriously for each project and are not shared with the scientific community. In our vision, such data will be stored centrally in an open repository. We propose an architecture for a standardized case data and ground truth information repository supporting the evaluation and analysis of computer-aided diagnosis based on (a) the Reference Model for an Open Archival Information System (OAIS) provided by the NASA Consultative Committee for Space Data Systems (ISO 14721:2003), (b) the Dublin Core Metadata Initiative (DCMI) Element Set (ISO 15836:2009), (c) the Open Archive Initiative (OAI) Protocol for Metadata Harvesting, and (d) the Image Retrieval in Medical Applications (IRMA) framework. In our implementation, a portal bunches all of the functionalities that are needed for data submission and retrieval. The complete life cycle of the data (define, create, store, sustain, share, use, and improve) is managed. Sophisticated search tools make it easier to use the datasets, which may be merged from different providers. An integrated history record guarantees reproducibility. A standardized creation report is generated with a permanent digital object identifier. This creation report must be referenced by all of the data users. Peer-reviewed e-publishing of these reports will create a reputation for the data contributors and will form de-facto standards regarding image and signal datasets. Good practice guidelines for validation methodology complement the concept of the case repository. This procedure will increase the comparability of evaluation studies for medical signal and image processing methods and applications.

Mesh:

Year:  2012        PMID: 22075810      PMCID: PMC3295968          DOI: 10.1007/s10278-011-9428-4

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  19 in total

1.  How to identify and assess tasks and challenges of Medical Image Processing.

Authors:  Alexander Horsch; Rudolf Thurmayr
Journal:  Stud Health Technol Inform       Date:  2003

2.  A reference data set for the evaluation of medical image retrieval systems.

Authors:  Henning Müller; Antoine Rosset; Jean-Paul Vallée; François Terrier; Antoine Geissbuhler
Journal:  Comput Med Imaging Graph       Date:  2004-09       Impact factor: 4.790

3.  Establishing an international reference image database for research and development in medical image processing.

Authors:  A Horsch; M Prinz; S Schneider; O Sipilä; K Spinnler; J-P Vallée; I Verdonck-de Leeuw; R Vogl; T Wittenberg; G Zahlmann
Journal:  Methods Inf Med       Date:  2004       Impact factor: 2.176

Review 4.  Content-based image retrieval in radiology: current status and future directions.

Authors:  Ceyhun Burak Akgül; Daniel L Rubin; Sandy Napel; Christopher F Beaulieu; Hayit Greenspan; Burak Acar
Journal:  J Digit Imaging       Date:  2011-04       Impact factor: 4.056

Review 5.  Advanced image processing in the clinical arena: issues to consider.

Authors:  Katherine P Andriole; Matthew A Barish; Ramin Khorasani
Journal:  J Am Coll Radiol       Date:  2006-04       Impact factor: 5.532

6.  Ontology of gaps in content-based image retrieval.

Authors:  Thomas M Deserno; Sameer Antani; Rodney Long
Journal:  J Digit Imaging       Date:  2008-02-01       Impact factor: 4.056

Review 7.  Medical multimedia retrieval 2.0.

Authors:  H Müller
Journal:  Yearb Med Inform       Date:  2008

8.  Bridging the integration gap between imaging and information systems: a uniform data concept for content-based image retrieval in computer-aided diagnosis.

Authors:  Petra Welter; Jörg Riesmeier; Benedikt Fischer; Christoph Grouls; Christiane Kuhl; Thomas M Deserno
Journal:  J Am Med Inform Assoc       Date:  2011 Jul-Aug       Impact factor: 4.497

Review 9.  Needs assessment for next generation computer-aided mammography reference image databases and evaluation studies.

Authors:  Alexander Horsch; Alexander Hapfelmeier; Matthias Elter
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-03-30       Impact factor: 2.924

10.  The caBIG annotation and image Markup project.

Authors:  David S Channin; Pattanasak Mongkolwat; Vladimir Kleper; Kastubh Sepukar; Daniel L Rubin
Journal:  J Digit Imaging       Date:  2009-03-18       Impact factor: 4.056

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Journal:  J Digit Imaging       Date:  2016-12       Impact factor: 4.056

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Authors:  Wei Xiang Lim; ZhiYuan Chen; Amr Ahmed
Journal:  Med Biol Eng Comput       Date:  2022-01-27       Impact factor: 3.079

4.  Ten simple rules for annotating sequencing experiments.

Authors:  Irene Stevens; Abdul Kadir Mukarram; Matthias Hörtenhuber; Terrence F Meehan; Johan Rung; Carsten O Daub
Journal:  PLoS Comput Biol       Date:  2020-10-05       Impact factor: 4.475

  4 in total

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