  Brain tumor imaging has been part of the clinical routine for many years to
perform non-invasive detection and grading of tumors. Tumor segmentation is a
crucial step for managing primary brain tumors because it allows a volumetric
analysis to have a longitudinal follow-up of tumor growth or shrinkage to
monitor disease progression and therapy response. In addition, it facilitates
further quantitative analysis such as radiomics. Deep learning models, in
particular CNNs, have been a methodology of choice in many applications of
medical image analysis including brain tumor segmentation. In this study, we
investigated the main design aspects of CNN models for the specific task of
MRI-based brain tumor segmentation. Two commonly used CNN architectures (i.e.
DeepMedic and U-Net) were used to evaluate the impact of the essential
parameters such as learning rate, batch size, loss function, and optimizer. The
performance of CNN models using different configurations was assessed with the
BraTS 2018 dataset to determine the most performant model. Then, the
generalization ability of the model was assessed using our in-house dataset.
For all experiments, U-Net achieved a higher DSC compared to the DeepMedic.
However, the difference was only statistically significant for whole tumor
segmentation using FLAIR sequence data and tumor core segmentation using T1w
sequence data. Adam and SGD both with the initial learning rate set to 0.001
provided the highest segmentation DSC when training the CNN model using U-Net
and DeepMedic architectures, respectively. No significant difference was
observed when using different normalization approaches. In terms of loss
functions, a weighted combination of soft Dice and cross-entropy loss with the
weighting term set to 0.5 resulted in an improved segmentation performance and
training stability for both DeepMedic and U-Net models.
