  Neuroimaging is essential in brain studies for the diagnosis and
identification of disease, structure, and function of the brain in its healthy
and disease states. Literature shows that there are advantages of multitasking
with some deep learning (DL) schemes in challenging neuroimaging applications.
This study examines the feasibility of using multitasking in three different
applications, including tissue segmentation, cross-modality conversion, and
bias-field correction. These applications reflect five different scenarios in
which multitasking is explored and 280 training and testing sessions conducted
for empirical evaluations. Two well-known networks, U-Net as a well-known
convolutional neural network architecture, and a closed architecture based on
the conditional generative adversarial network are implemented. Different
metrics such as the normalized cross-correlation coefficient and Dice scores
are used for comparison of methods and results of the different experiments.
Statistical analysis is also provided by paired t-test. The present study
explores the pros and cons of these methods and their practical impacts on
multitasking in different implementation scenarios. This investigation shows
that bias correction and cross-modality conversion applications are
significantly easier than the segmentation application, and having multitasking
with segmentation is not reasonable if one of them is identified as the main
target application. However, when the main application is the segmentation of
tissues, multitasking with cross-modality conversion is beneficial, especially
for the U-net architecture.
