'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Precise automatic liver segmentation plays an important
role in computer-aided diagnosis of liver pathology.
Despite many years of research, this is still a challenging task,
especially when processing heterogeneous volumetric data from
different sources. This study focuses on automatic liver segmentation
on CT volumes proposing a fusion approach of traditional
methods and neural network prediction masks. First, a region
growing based method is proposed, which also applies active
contour and thresholding based probability density function.
Then the obtained binary mask is combined with the results of
the 3D U-Net neural network improved by GrowCut approach.
Extensive quantitative evaluation is carried out on three
different CT datasets, representing varying image characteristics.
The proposed fusion method compensates for the drawbacks of
the traditional and U-Net based approach, performs uniformly
stable for heterogeneous CT data and its performance is comparable
to the state-of-the-art, therefore it provides a promising
segmentation alternative