Abstract:
Image segmentation is a powerful tool for image analysis that has applications in autonomous
vehicles, face recognition and healthcare. The basic functionality is to transform
a given image into something comprising of more meaning and clarity. Segmentation has
been extensively used for the analysis of cell images, which play an important role in
tracking disease progression and identifying pathologies. To address the problem of cell
segmentation, a popular encoder-decoder based convolution network in the literature has
been increasingly used, that is known as UNet. Its architecture has been updated by using
residual networks (ResNet) in the encoder part, resulting into a deeper segmentation
network which leads to an improved accuracy. In this research, we have used ResNet
with identity mappings as the encoder in U-Net and its performance is evaluated on the
basis of comparison with previously used encoder-decoder based networks. After training
the networks, it was seen that the proposed network outperformed U-Net and U-Net
with ResNet encoder in terms of the dice score and IoU score. The results produced in
this work can be used as a baseline for medical image segmentation.
Keywords: Image Segmentation, Cell Images, U-Net.
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