  Medical imaging has been employed to support medical diagnosis and treatment.
It may also provide crucial information to surgeons to facilitate optimal
surgical preplanning and perioperative management. Essentially, semi-automatic
organ and tumor segmentation has been studied by many researchers. Recently,
with the development of Deep Learning (DL) algorithms, automatic organ
segmentation has been gathered lots of attention from the researchers. This
paper addresses to propose the most efficient DL architectures for Liver
segmentation by adapting and comparing state-of-the-art DL frameworks, studied
in different disciplines. These frameworks are implemented and adapted into a
Commercial software, 'LiverVision'. It is aimed to reveal the most effective
and accurate DL architecture for fully automatic liver segmentation. Equal
conditions were provided to all architectures in the experiments so as to
measure the effectiveness of algorithms accuracy, and Dice coefficient metrics
were also employed to support comparative analysis. Experimental results prove
that 'U-Net' and 'SegNet' have been superior in line with the experiments
conducted considering the concepts of time, cost, and effectiveness.
Considering both architectures, 'SegNet' was observed to be more successful in
eliminating false-positive values. Besides, it was seen that the accuracy
metric used to measure effectiveness in image segmentation alone was not
enough. Results reveal that DL algorithms are able to automate organ
segmentation from DICOM images with high accuracy. This contribution is
critical for surgical preplanning and motivates author to apply this approach
to the different organs and field of medicine.
