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Attention deep residual networks for MR image analysis

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Abstract

Prostate diseases often occur in men. For further clinical treatment and diagnosis, we need to do accurate segmentation on prostate. There are already many methods that concentrate on solving the problem of automatic prostate MR image segmentation. However, the design of some hyperparameters of these methods is migrated from the models that are used for nature images which do not consider the difference between medical image and nature image. Besides, there is trend that researchers are likely to use deeper and more complicated networks to achieve high accuracy. The improvement is limited with surging parameters, computations, training time, and inference time. In this paper, we propose an efficient attention residual U-Net to segment the prostate MR image. We analyze the property of prostate MR image and fine-tune the architecture of U-Net. To accelerate the convergence of our method, residual connection and channel attention are added to our network. A set of experiments suggest our method can achieve a similar accuracy of state of the art with less parameters, less computations, shorter training time, and shorter inference time.

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Funding

This work is supported by the Science and Technology Major Project of Hubei Province (Next-Generation AI Technologies) under Grant No. 2019AEA170.

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Bayi Road, Wuhan, Hubei, China

    Mengqing Mei & Fazhi He

  2. Department of Computing, Macquarie University, Sydney, Australia

    Shan Xue

Authors
  1. Mengqing Mei
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  2. Fazhi He
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  3. Shan Xue
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Correspondence to Fazhi He.

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Mei, M., He, F. & Xue, S. Attention deep residual networks for MR image analysis. Neural Comput & Applic 35, 12957–12966 (2023). https://doi.org/10.1007/s00521-020-05083-3

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