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Global U-net with amalgamation of inception model and improved kernel variation for MRI brain image segmentation

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Abstract

In this work, we propound the Global Inception U-Nets, that consist of suppled non-local accretion blocks, that could be used in clinical image segmentation especially for MRI segmentation of brain tumour images. The non-local accretion blocks can be positioned in U-Net as image size-conservation blocks along with the down-sampling and up-sampling layers. The fundamental research in this work involved use of the inception model to assort brain images. We have also used variant sized filters. The idea behind using these types of filters, was that the variant sized filters helps the neural network become sturdy towards the change in the scale. We start by drawing out deep activation features. This is applied on the overall image as well as on the image patches of distinct scales. The image in the encoder side is filtered in parallel by multi sized kernels. The output from every single filter undergoes batch normalisation (BN) and the outputs are summed up with a Maxout unit. This approach shrinks the spatial information of the outputs and introduces competition among the kernels. Then activations of image patches are brought together by using extra Maxpool operation with the convolution layer and after convolution layer too. Thus, we get a contemporary image representation by merging both the Maxpool and inception model activations.

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Authors and Affiliations

  1. Department of Computer Science, BIT Mesra, Noida, India

    Annu Mishra

  2. Department of Computer Science, BIT Mesra, Ranchi, India

    Pankaj Gupta

  3. Department of Mathematics, BIT Mesra, Ranchi, India

    Peeyush Tewari

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  1. Annu Mishra
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  2. Pankaj Gupta
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  3. Peeyush Tewari
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Correspondence to Annu Mishra.

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Mishra, A., Gupta, P. & Tewari, P. Global U-net with amalgamation of inception model and improved kernel variation for MRI brain image segmentation. Multimed Tools Appl 81, 23339–23354 (2022). https://doi.org/10.1007/s11042-022-12094-w

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  • DOI: https://doi.org/10.1007/s11042-022-12094-w

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