Image segmentation of plexiform neurofibromas from a deep neural network using multiple b-value diffusion data
dc.contributor.author | Ho, Chang Y. | |
dc.contributor.author | Kindler, John M. | |
dc.contributor.author | Persohn, Scott | |
dc.contributor.author | Kralik, Stephen F. | |
dc.contributor.author | Robertson, Kent A. | |
dc.contributor.author | Territo, Paul R. | |
dc.contributor.department | Radiology and Imaging Sciences, School of Medicine | en_US |
dc.date.accessioned | 2021-07-28T09:08:50Z | |
dc.date.available | 2021-07-28T09:08:50Z | |
dc.date.issued | 2020-10-20 | |
dc.description.abstract | We assessed the accuracy of semi-automated tumor volume maps of plexiform neurofibroma (PN) generated by a deep neural network, compared to manual segmentation using diffusion weighted imaging (DWI) data. NF1 Patients were recruited from a phase II clinical trial for the treatment of PN. Multiple b-value DWI was imaged over the largest PN. All DWI datasets were registered and intensity normalized prior to segmentation with a multi-spectral neural network classifier (MSNN). Manual volumes of PN were performed on 3D-T2 images registered to diffusion images and compared to MSNN volumes with the Sørensen-Dice coefficient. Intravoxel incoherent motion (IVIM) parameters were calculated from resulting volumes. 35 MRI scans were included from 14 subjects. Sørensen-Dice coefficient between the semi-automated and manual segmentation was 0.77 ± 0.016. Perfusion fraction (f) was significantly higher for tumor versus normal tissue (0.47 ± 0.42 vs. 0.30 ± 0.22, p = 0.02), similarly, true diffusion (D) was significantly higher for PN tumor versus normal (0.0018 ± 0.0003 vs. 0.0012 ± 0.0002, p < 0.0001). By contrast, the pseudodiffusion coefficient (D*) was significantly lower for PN tumor versus normal (0.024 ± 0.01 vs. 0.031 ± 0.005, p < 0.0001). Volumes generated by a neural network from multiple diffusion data on PNs demonstrated good correlation with manual volumes. IVIM analysis of multiple b-value diffusion data demonstrates significant differences between PN and normal tissue. | en_US |
dc.identifier.citation | Ho, C. Y., Kindler, J. M., Persohn, S., Kralik, S. F., Robertson, K. A., & Territo, P. R. (2020). Image segmentation of plexiform neurofibromas from a deep neural network using multiple b-value diffusion data. Scientific Reports, 10(1), 17857. https://doi.org/10.1038/s41598-020-74920-1 | en_US |
dc.identifier.issn | 2045-2322 | en_US |
dc.identifier.uri | https://hdl.handle.net/1805/26296 | |
dc.publisher | Nature Publishing Group | en_US |
dc.relation.isversionof | 10.1038/s41598-020-74920-1 | en_US |
dc.relation.journal | Scientific Reports | en_US |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | * |
dc.source | PMC | en_US |
dc.subject | Diagnostic markers | en_US |
dc.subject | Computational biology and bioinformatics | en_US |
dc.subject | Peripheral neuropathies | en_US |
dc.subject | Cancer | en_US |
dc.subject | Cancer imaging | en_US |
dc.title | Image segmentation of plexiform neurofibromas from a deep neural network using multiple b-value diffusion data | en_US |
dc.type | Article | en_US |
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