1,591 research outputs found
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans
The magnetic resonance (MR) analysis of brain tumors is widely used for
diagnosis and examination of tumor subregions. The overlapping area among the
intensity distribution of healthy, enhancing, non-enhancing, and edema regions
makes the automatic segmentation a challenging task. Here, we show that a
convolutional neural network trained on high-contrast images can transform the
intensity distribution of brain lesions in its internal subregions.
Specifically, a generative adversarial network (GAN) is extended to synthesize
high-contrast images. A comparison of these synthetic images and real images of
brain tumor tissue in MR scans showed significant segmentation improvement and
decreased the number of real channels for segmentation. The synthetic images
are used as a substitute for real channels and can bypass real modalities in
the multimodal brain tumor segmentation framework. Segmentation results on
BraTS 2019 dataset demonstrate that our proposed approach can efficiently
segment the tumor areas. In the end, we predict patient survival time based on
volumetric features of the tumor subregions as well as the age of each case
through several regression models
Radiotherapy planning for glioblastoma based on a tumor growth model: Improving target volume delineation
Glioblastoma are known to infiltrate the brain parenchyma instead of forming
a solid tumor mass with a defined boundary. Only the part of the tumor with
high tumor cell density can be localized through imaging directly. In contrast,
brain tissue infiltrated by tumor cells at low density appears normal on
current imaging modalities. In clinical practice, a uniform margin is applied
to account for microscopic spread of disease.
The current treatment planning procedure can potentially be improved by
accounting for the anisotropy of tumor growth: Anatomical barriers such as the
falx cerebri represent boundaries for migrating tumor cells. In addition, tumor
cells primarily spread in white matter and infiltrate gray matter at lower
rate. We investigate the use of a phenomenological tumor growth model for
treatment planning. The model is based on the Fisher-Kolmogorov equation, which
formalizes these growth characteristics and estimates the spatial distribution
of tumor cells in normal appearing regions of the brain. The target volume for
radiotherapy planning can be defined as an isoline of the simulated tumor cell
density.
A retrospective study involving 10 glioblastoma patients has been performed.
To illustrate the main findings of the study, a detailed case study is
presented for a glioblastoma located close to the falx. In this situation, the
falx represents a boundary for migrating tumor cells, whereas the corpus
callosum provides a route for the tumor to spread to the contralateral
hemisphere. We further discuss the sensitivity of the model with respect to the
input parameters. Correct segmentation of the brain appears to be the most
crucial model input.
We conclude that the tumor growth model provides a method to account for
anisotropic growth patterns of glioblastoma, and may therefore provide a tool
to make target delineation more objective and automated
- …