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MS UNet: Multi-scale 3D UNet for Brain Tumor Segmentation

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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 12963))

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Abstract

A deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates multi-scale features from image data. The multi-scaled features play an essential role in brain tumor segmentation. Researchers presented numerous multi-scale strategies that have been excellent for the segmentation task. This paper proposes a multi-scale strategy that can further improve the final segmentation accuracy. We propose three multi-scale strategies in MS UNet. Firstly, we utilize densely connected blocks in the encoder and decoder for multi-scale features. Next, the proposed residual-inception blocks extract local and global information by merging features of different kernel sizes. Lastly, we utilize the idea of deep supervision for multiple depths at the decoder. We validate the MS UNet on the BraTS 2021 validation dataset. The dice (DSC) scores of the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) are \(91.938\%\), \(86.268\%\), and \(82.409\%\), respectively.

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Acknowledgment

This work is supported by the National Natural Science Foundation of China under Grant No. 91959108.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

    Parvez Ahmad

  2. Department of Computer Applications, Madanapalle Institute of Technology and Science, Madanapalle, India

    Saqib Qamar

  3. AI Research Center for Medical Image Analysis and Diagnosis, Shenzhen University, Shenzhen, China

    Linlin Shen

  4. Computer Vision Institute, School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Linlin Shen

  5. Guangzhou University, Guangzhou, China

    Syed Qasim Afser Rizvi

  6. Central University of Punjab, Bathinda, India

    Aamir Ali

  7. School of IT and Systems Faculty of Science and Technology, University of Canberra, Bruce, Australia

    Girija Chetty

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  1. Parvez Ahmad
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  2. Saqib Qamar
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Corresponding author

Correspondence to Parvez Ahmad .

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Editors and Affiliations

  1. Sano Centre for Computational Personaliz, Kraków, Poland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

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Ahmad, P., Qamar, S., Shen, L., Rizvi, S.Q.A., Ali, A., Chetty, G. (2022). MS UNet: Multi-scale 3D UNet for Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_3

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  • DOI: https://doi.org/10.1007/978-3-031-09002-8_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09001-1

  • Online ISBN: 978-3-031-09002-8

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