Multi-scale segmentation in GBM treatment using diffusion tensor imaging

Roushanak Rahmat

doi:10.1016/j.compbiomed.2020.103815

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Multi-scale segmentation in GBM treatment using diffusion tensor imaging

2020-08-01

Roushanak Rahmat *

Khadijeh Saednia

Mohammad Reza Haji Hosseini Khani

Mohammad Rahmati

R. Jena

Stephen J. Price

DOI：10.1016/j.compbiomed.2020.103815

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Abstract

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Glioblastoma (GBM) is the commonest primary malignant brain tumor in adults, and despite advances in multimodality therapy, the outlook for patients has changed little in the last 10 years. Local recurrence is the predominant pattern of treatment failure, hence improved local therapies (surgery and radiotherapy) are needed to improve patient outcomes. Currently segmentation of GBM for surgery or radiotherapy (RT) planning is labor intensive, especially for high-dimensional MR imaging methods that may provide more sensitive indicators of tumor phenotype. Automating processing and segmentation of these images will aid treatment planning. Diffusion tensor magnetic resonance imaging is a recently developed technique (DTI) that is exquisitely sensitive to the ordered diffusion of water in white matter tracts. Our group has shown that decomposition of the tensor information into the isotropic component (p - shown to represent tumor invasion) and the anisotropic component (q - shown to represent the tumor bulk) can provide valuable prognostic information regarding tumor infiltration and patient survival. However, tensor decomposition of DTI data is not commonly used for neurosurgery or radiotherapy treatment planning due to difficulties in segmenting the resultant image maps. For this reason, automated techniques for segmentation of tensor decomposition maps would have significant clinical utility. In this paper, we modified a well-established convolutional neural network architecture (CNN) for medical image segmentation and used it as an automatic multi-sequence GBM segmentation based on both DTI image maps (p and q maps) and conventional MRI sequences (T2-FLAIR and T1 weighted post contrast (T1c)). In this proof-of-concept work, we have used multiple MRI sequences, each with individually defined ground truths for better understanding of the contribution of each image sequence to the segmentation performance. The high accuracy and efficiency of our proposed model demonstrates the potential of utilizing diffusion MR images for target definition in precision radiation treatment planning and surgery in routine clinical practice.

Keywords:

Image segmentation

Deep learning

GBM

DTI-MRI

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