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dc.contributor.authorMomeni, Saba
dc.contributor.authorFazlollahi, Amir
dc.contributor.authorYates, Paul
dc.contributor.authorRowe, Christopher
dc.contributor.authorGao, Yongsheng
dc.contributor.authorLiew, Alan Wee-Chung
dc.contributor.authorSalvado, Olivier
dc.date.accessioned2021-06-08T05:50:26Z
dc.date.available2021-06-08T05:50:26Z
dc.date.issued2021
dc.identifier.issn0169-2607
dc.identifier.doi10.1016/j.cmpb.2021.106127
dc.identifier.urihttp://hdl.handle.net/10072/405004
dc.description.abstractBACKGROUND AND OBJECTIVE: Cerebral microbleeds (CMB) are important biomarkers of cerebrovascular diseases and cognitive dysfunctions. Susceptibility weighted imaging (SWI) is a common MRI sequence where CMB appear as small hypointense blobs. The prevalence of CMB in the population and in each scan is low, resulting in tedious and time-consuming visual assessment. Automated detection methods would be of value but are challenged by the CMB low prevalence, the presence of mimics such as blood vessels, and the difficulty to obtain sufficient ground truth for training and testing. In this paper, synthetic CMB (sCMB) generation using an analytical model is proposed for training and testing machine learning methods. The main aim is creating perfect synthetic ground truth as similar as reals, in high number, with a high diversity of shape, volume, intensity, and location to improve training of supervised methods. METHOD: sCMB were modelled with a random Gaussian shape and added to healthy brain locations. We compared training on our synthetic data to standard augmentation techniques. We performed a validation experiment using sCMB and report result for whole brain detection using a 10-fold cross validation design with an ensemble of 10 neural networks. RESULTS: Performance was close to state of the art (~9 false positives per scan), when random forest was trained on synthetic only and tested on real lesion. Other experiments showed that top detection performance could be achieved when training on synthetic CMB only. Our dataset is made available, including a version with 37,000 synthetic lesions, that could be used for benchmarking and training. CONCLUSION: Our proposed synthetic microbleeds model is a powerful data augmentation approach for CMB classification with and should be considered for training automated lesion detection system from MRI SWI.
dc.description.peerreviewedYes
dc.languageeng
dc.publisherElsevier BV
dc.relation.ispartofpagefrom106127
dc.relation.ispartofjournalComputer Methods and Programs in Biomedicine
dc.relation.ispartofvolume207
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchBiomedical Engineering
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0903
dc.subject.fieldofresearchcode0906
dc.subject.keywordsData augmentation
dc.subject.keywordsGaussian modeling
dc.subject.keywordsMicrobleeds detection
dc.subject.keywordsNeural network
dc.subject.keywordsSynthetic data generation
dc.titleSynthetic microbleeds generation for classifier training without ground truth
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationMomeni, S; Fazlollahi, A; Yates, P; Rowe, C; Gao, Y; Liew, AW-C; Salvado, O, Synthetic microbleeds generation for classifier training without ground truth, Computer Methods and Programs in Biomedicine, 2021, 207, pp. 106127
dcterms.dateAccepted2021-04-21
dc.date.updated2021-06-08T03:08:27Z
gro.hasfulltextNo Full Text
gro.griffith.authorLiew, Alan Wee-Chung
gro.griffith.authorGao, Yongsheng
gro.griffith.authorMomeni, Saba


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