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

Semantic Segmentation of highly class imbalanced fully labelled 3D  volumetric biomedical images and unsupervised Domain Adaptation of the  pre-trained Segmentation Network to segment another fully unlabelled  Biomedical 3D Image stack