Abstract:
Advancments in imaging field have evolved enough to make the detection of
tumor task more accurate through 3D MRI but at the same time more time consuming
and complex for medical experts. Therefore, the need for a computational logic unit
which never fails, process the information fast and never gets tired arises. This thesis
will cover a whole mechanism of brain tumor severity determination starting from
segmentation process till evaluation of eccentricity and volume. Segmentation step is
performed with a 3D U-net whith some tweaked hyperparameters such as dropout
values and learning rates to achieve better performance for the segmented parts. The
accuracy of segmentation is reported to be 99%. Eccentricity and volume are measured
over the segmented region. Estimation of eccentricity is calculated based on the energy
values from the decomposition of segmented tumor in SVD where the sigma, or the
energy matrix holds the values of which their ratio combined gives the eccentricity
value. The dataset is part of the BRATS challenge 2020.
