RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiolog...
RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology
About this item
Full title
Author / Creator
Trägårdh, Elin , Borrelli, Pablo , Kaboteh, Reza , Gillberg, Tony , Ulén, Johannes , Enqvist, Olof , Edenbrandt, Lars , Övriga starka forskningsmiljöer , Faculty of Medicine , Medicinska fakulteten , Other Strong Research Environments , LUCC: Lund University Cancer Centre , WCMM-Wallenberg Centre for Molecular Medicine , Nuclear medicine, Malmö , Lunds universitet , Nuklearmedicin, Malmö , Profilområden och andra starka forskningsmiljöer , Lund University , Department of Translational Medicine , Profile areas and other strong research environments , WCMM- Wallenberg center för molekylär medicinsk forskning , LUCC: Lunds universitets cancercentrum and Institutionen för translationell medicin
Publisher
Cham: Springer International Publishing
Journal title
Language
English
Formats
Publication information
Publisher
Cham: Springer International Publishing
Subjects
More information
Scope and Contents
Contents
Background
Artificial intelligence (AI) is about to transform medical imaging. The Research Consortium for Medical Image Analysis (RECOMIA), a not-for-profit organisation, has developed an online platform to facilitate collaboration between medical researchers and AI researchers. The aim is to minimise the time and effort researchers need to spend on technical aspects, such as transfer, display, and annotation of images, as well as legal aspects, such as de-identification. The purpose of this article is to present the RECOMIA platform and its AI-based tools for organ segmentation in computed tomography (CT), which can be used for extraction of standardised uptake values from the corresponding positron emission tomography (PET) image.
Results
The RECOMIA platform includes modules for (1) local de-identification of medical images, (2) secure transfer of images to the cloud-based platform, (3) display functions available using a standard web browser, (4) tools for manual annotation of organs or pathology in the images, (5) deep learning-based tools for organ segmentation or other customised analyses, (6) tools for quantification of segmented volumes, and (7) an export function for the quantitative results. The AI-based tool for organ segmentation in CT currently handles 100 organs (77 bones and 23 soft tissue organs). The segmentation is based on two convolutional neural networks (CNNs): one network to handle organs with multiple similar instances, such as vertebrae and ribs, and one network for all other organs. The CNNs have been trained using CT studies from 339 patients. Experienced radiologists annotated organs in the CT studies. The performance of the segmentation tool, measured as mean Dice index on a manually annotated test set, with 10 representative organs, was 0.93 for all foreground voxels, and the mean Dice index over the organs were 0.86 (0.82 for the soft tissue organs and 0.90 for the bones).
Conclusion
The paper presents a platform that provides deep learning-based tools that can perform basic organ segmentations in CT, which can then be used to automatically obtain the different measurement in the corresponding PET image. The RECOMIA platform is available on request at
www.recomia.org
for research purposes....
Alternative Titles
Full title
RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology
Authors, Artists and Contributors
Author / Creator
Borrelli, Pablo
Kaboteh, Reza
Gillberg, Tony
Ulén, Johannes
Enqvist, Olof
Edenbrandt, Lars
Övriga starka forskningsmiljöer
Faculty of Medicine
Medicinska fakulteten
Other Strong Research Environments
LUCC: Lund University Cancer Centre
WCMM-Wallenberg Centre for Molecular Medicine
Nuclear medicine, Malmö
Lunds universitet
Nuklearmedicin, Malmö
Profilområden och andra starka forskningsmiljöer
Lund University
Department of Translational Medicine
Profile areas and other strong research environments
WCMM- Wallenberg center för molekylär medicinsk forskning
LUCC: Lunds universitets cancercentrum
Institutionen för translationell medicin
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_c19f3102d9564d28ba2fee536765c0cd
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_c19f3102d9564d28ba2fee536765c0cd
Other Identifiers
ISSN
2197-7364
E-ISSN
2197-7364
DOI
10.1186/s40658-020-00316-9
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