  The goal of our work is to perform pixel label semantic segmentation on 3D
biomedical volumetric data. Manual annotation is always difficult for a large
bio-medical dataset. So, we consider two cases where one dataset is fully
labeled and the other dataset is assumed to be fully unlabelled. We first
perform Semantic Segmentation on the fully labeled isotropic biomedical source
data (FIBSEM) and try to incorporate the the trained model for segmenting the
target unlabelled dataset(SNEMI3D)which shares some similarities with the
source dataset in the context of different types of cellular bodies and other
cellular components. Although, the cellular components vary in size and shape.
So in this paper, we have proposed a novel approach in the context of
unsupervised domain adaptation while classifying each pixel of the target
volumetric data into cell boundary and cell body. Also, we have proposed a
novel approach to giving non-uniform weights to different pixels in the
training images while performing the pixel-level semantic segmentation in the
presence of the corresponding pixel-wise label map along with the training
original images in the source domain. We have used the Entropy Map or a
Distance Transform matrix retrieved from the given ground truth label map which
has helped to overcome the class imbalance problem in the medical image data
where the cell boundaries are extremely thin and hence, extremely prone to be
misclassified as non-boundary.
