Training and assessing convolutional neural network performance in automatic vascular segmentation using Ga-68 DOTATATE PET/CT.

R Parry, K Wright, J W Bellinge, M A Ebert, P Rowshanfarzad, R J Francis, C J Schultz

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In one paragraph

Article in The international journal of cardiovascular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

R ParrySchool of Medicine, The University of Western Australia, Perth, Australia. reece.parry@health.wa.gov.au.

K WrightSchool of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia.

J W BellingeSchool of Medicine, The University of Western Australia, Perth, Australia.

M A EbertSchool of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia.

P RowshanfarzadSchool of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia.

R J FrancisSchool of Medicine, The University of Western Australia, Perth, Australia.

C J SchultzSchool of Medicine, The University of Western Australia, Perth, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To evaluate a convolutional neural network's performance (nnU-Net) in the assessment of vascular contours, calcification and PET tracer activity using Ga-68 DOTATATE PET/CT. Patients who underwent Ga-68 DOTATATE PET/CT imaging over a 12-month period for neuroendocrine investigation were included. Manual cardiac and aortic segmentations were performed by an experienced observer. Scans were randomly allocated in ratio 64:16:20 for training, validation and testing of the nnU-Net model. PET tracer uptake and calcium scoring were compared between segmentation methods and different observers. 116 patients (53.5% female) with a median age of 64.5 years (range 23-79) were included. There were strong, positive correlations between all segmentations (mostly r > 0.98). There were no significant differences between manual and AI segmentation of SUV

Indexed as

Positron Emission Tomography Computed TomographyPredictive Value of TestsRadiopharmaceuticalsVascular CalcificationAdultAgedAortic DiseasesAutomationDeep LearningFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedNeural Networks, ComputerObserver VariationOrganometallic CompoundsRadiopharmaceuticalsArtificial intelligenceAutomatic segmentationCardiovascular inflammationCoronary artery diseaseDeep learningGallium-68 DOTATATE positron emission tomographyNeural network

Identifiers

PMID38967895

DOI10.3390/life13020507

PMCPMC11473569

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.