Attention deep residual networks for MR image analysis

Mengqing Mei, Fazhi He*, Shan Xue

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Number of pages10
JournalNeural Computing and Applications
Early online date16 Jun 2020
DOIs
Publication statusE-pub ahead of print - 16 Jun 2020

Keywords

  • Channel attention
  • Convergence
  • Deep residual networks
  • MR image segmentation

Fingerprint

Dive into the research topics of 'Attention deep residual networks for MR image analysis'. Together they form a unique fingerprint.

Cite this