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 (2004.02748v1)

Abstract: 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.