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

Chang Y Ho1,2, John M Kindler3, Scott Persohn3,4

  • 1Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA. cyho@iu.edu.

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|October 21, 2020
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Summary
This summary is machine-generated.

A deep neural network accurately segmented plexiform neurofibroma (PN) tumor volumes using diffusion-weighted imaging (DWI). Intravoxel incoherent motion (IVIM) analysis revealed significant differences between PN tumor and normal tissue.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Plexiform neurofibromas (PN) are benign tumors associated with Neurofibromatosis type 1 (NF1).
  • Accurate tumor volume assessment is crucial for monitoring PN progression and treatment response.
  • Manual segmentation of tumors from medical imaging can be time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To evaluate the accuracy of semi-automated tumor volume segmentation for plexiform neurofibromas (PN) using a deep neural network (DNN).
  • To compare DNN-generated tumor volumes with manual segmentation using diffusion-weighted imaging (DWI).
  • To investigate the utility of intravoxel incoherent motion (IVIM) parameters derived from DWI for characterizing PN.

Main Methods:

  • A multi-spectral neural network classifier (MSNN) was employed for semi-automated segmentation of PN from DWI data.
  • Manual segmentation of PN was performed on registered 3D-T2 images.
  • The Sørensen-Dice coefficient was used to quantify the agreement between semi-automated and manual segmentations.
  • Intravoxel incoherent motion (IVIM) parameters (perfusion fraction (f), true diffusion (D), and pseudodiffusion coefficient (D*)) were calculated.

Main Results:

  • The Sørensen-Dice coefficient between semi-automated and manual segmentation was 0.77 ± 0.016, indicating good agreement.
  • Perfusion fraction (f) and true diffusion (D) were significantly higher in PN tumor tissue compared to normal tissue (p < 0.02).
  • The pseudodiffusion coefficient (D*) was significantly lower in PN tumor tissue compared to normal tissue (p < 0.0001).

Conclusions:

  • Semi-automated segmentation of PN using a DNN from multi-b-value DWI data demonstrates good correlation with manual segmentation.
  • IVIM analysis of DWI data reveals significant differences in perfusion and diffusion characteristics between PN tumor and normal tissues.
  • This DNN-based approach offers a promising tool for accurate and efficient tumor volume assessment in NF1 patients.