Image segmentation of plexiform neurofibromas from a deep neural network using multiple b-value diffusion data

Ho, Chang Y.Kindler, John M.Persohn, ScottKralik, Stephen F.Robertson, Kent A.Territo, Paul R.2021-07-282021-07-282020-10-20Ho, 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/s41598-020-74920-12045-2322https://hdl.handle.net/1805/26296We 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.Attribution 4.0 InternationalDiagnostic markersComputational biology and bioinformaticsPeripheral neuropathiesCancerCancer imagingImage segmentation of plexiform neurofibromas from a deep neural network using multiple b-value diffusion dataArticle