Generative Adversarial Networks for Brain MRI Synthesis: Impact of Training Set Size on Clinical Application.
We evaluated the impact of training set size on generative adversarial networks (GANs) to synthesize brain MRI sequences. We compared three sets of GANs trained to generate pre-contrast T1 (gT1) from post-contrast T1 and FLAIR (gFLAIR) from T2. The baseline models were trained on 135 cases; for this...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 3; pp. 1228 - 1239 |
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| Autores principales: | , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178678181&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178678181 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2024 vid: 37 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178678181 178678181 178678181 10.1007/s10278-024-00976-4 178678181 ppf: 1228 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Generative Adversarial Networks for Brain MRI Synthesis: Impact of Training Set Size on Clinical Application. aug: au: Zoghby, MM Erickson, BJ Conte, GM affil: https://ror.org/02qp3tb03 Department of Radiology, Mayo Clinic, Rochester, MN, USA sug: subj: Brain Physiology Magnetic Resonance Imaging Methods Generative Adversarial Networks Evaluation Image Interpretation, Computer Assisted Education, Clinical Learning Methods Models, Theoretical Human Prospective Studies Glioma Comparative Studies Descriptive Statistics Diagnostic Errors Artificial Intelligence, Generative Data Analysis Software Mann-Whitney U Test Image Enhancement Coefficient alpha Funding Source ab: We evaluated the impact of training set size on generative adversarial networks (GANs) to synthesize brain MRI sequences. We compared three sets of GANs trained to generate pre-contrast T1 (gT1) from post-contrast T1 and FLAIR (gFLAIR) from T2. The baseline models were trained on 135 cases; for this study, we used the same model architecture but a larger cohort of 1251 cases and two stopping rules, an early checkpoint (early models) and one after 50 epochs (late models). We tested all models on an independent dataset of 485 newly diagnosed gliomas. We compared the generated MRIs with the original ones using the structural similarity index (SSI) and mean squared error (MSE). We simulated scenarios where either the original T1, FLAIR, or both were missing and used their synthesized version as inputs for a segmentation model with the original post-contrast T1 and T2. We compared the segmentations using the dice similarity coefficient (DSC) for the contrast-enhancing area, non-enhancing area, and the whole lesion. For the baseline, early, and late models on the test set, for the gT1, median SSI was.957,.918, and.947; median MSE was.006,.014, and.008. For the gFLAIR, median SSI was.924,.908, and.915; median MSE was.016,.016, and.019. The range DSC was.625–.955,.420–.952, and.610–.954. Overall, GANs trained on a relatively small cohort performed similarly to those trained on a cohort ten times larger, making them a viable option for rare diseases or institutions with limited resources. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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