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RECOMIA-a cloud-bas...
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Trägårdh, ElinLund University,Lunds universitet,Nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,WCMM- Wallenberg center för molekylär medicinsk forskning,Medicinska fakulteten,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Nuclear medicine, Malmö,Lund University Research Groups,WCMM-Wallenberg Centre for Molecular Medicine,Faculty of Medicine,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Skåne University Hospital
(författare)
RECOMIA-a cloud-based platform for artificial intelligence research in nuclear medicine and radiology
- Artikel/kapitelEngelska2020
Förlag, utgivningsår, omfång ...
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2020-08-04
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Springer Science and Business Media LLC,2020
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LIBRIS-ID:oai:gup.ub.gu.se/295867
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https://gup.ub.gu.se/publication/295867URI
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https://doi.org/10.1186/s40658-020-00316-9DOI
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https://research.chalmers.se/publication/518468URI
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https://lup.lub.lu.se/record/04c6e6dc-233b-483e-8342-3cd93d98e0e7URI
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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 atfor research purposes.
Ämnesord och genrebeteckningar
Biuppslag (personer, institutioner, konferenser, titlar ...)
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Borrelli, P.Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
(författare)
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Kaboteh, R.Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
(författare)
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Gillberg, T.RECOMIA AB
(författare)
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Ulén, JohannesEigenvision AB(Swepub:lu)math-jsu
(författare)
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Enqvist, Olof,1981Chalmers University of Technology,Eigenvision AB(Swepub:lu)math-ooe
(författare)
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Edenbrandt, Lars,1957Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,University of Gothenburg,Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital(Swepub:lu)klfy-led
(författare)
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Nuklearmedicin, MalmöForskargrupper vid Lunds universitet
(creator_code:org_t)
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Ingår i:Ejnmmi Physics: Springer Science and Business Media LLC7:12197-7364
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