Inception U-Net Architecture for Semantic Segmentation to Identify Nuclei in Microscopy Cell Images

Abstract

With the increasing applications of deep learning in biomedical image analysis, in this article we introduce an inception U-Net architecture for automating nuclei detection in microscopy cell images of varying size and modality to help unlock faster cures, inspired from Kaggle Data Science Bowl Challenge 2018 (KDSB18). This study follows from the fact that most of the analysis requires nuclei detection as the starting phase for getting an insight into the underlying biological process and further diagnosis. The proposed architecture consists of a switch normalization layer, convolution layers, and inception layers (concatenated 1x1, 3x3, and 5x5 convolution and the hybrid of a max and Hartley spectral pooling layer) connected in the U-Net fashion for generating the image masks. This article also illustrates the model perception of image masks using activation maximization and filter map visualization techniques. A novel objective function segmentation loss is proposed based on the binary cross entropy, dice coefficient, and intersection over union loss functions. The intersection over union score, loss value, and pixel accuracy metrics evaluate the model over the KDSB18 dataset. The proposed inception U-Net architecture exhibits quite significant results as compared to the original U-Net and recent U-Net++ architecture.