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Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site Traumatic Brain Injury Images

  • Yilin Liu
  • Gregory R. Kirk
  • Brendon M. Nacewicz
  • Martin A. Styner
  • Mingren Shen
  • Dong Nie
  • Nagesh Adluru
  • Benjamin Yeske
  • Peter A. Ferrazzano
  • Andrew L. AlexanderEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11795)

Abstract

While learning based methods have brought extremely promising results in medical imaging, a major bottleneck is the lack of generalizability. Medical images are often collected from multiple sites and/or protocols for increasing statistical power, while CNN trained on one site typically cannot be well-transferred to others. Further, expert-defined manual labels for medical images are typically rare, making training a dedicated CNN for each site unpractical, so it is important to make best use of the limited labeled source data. To address this problem, we harmonize the target data using adversarial learning, and propose targeted feature dropout (TFD) to enhance the robustness of the model to variations in target images. Specifically, TFD is guided by attention to stochastically remove some of the most discriminative features. Essentially, this technique combines the benefits of attention mechanism and dropout, while it does not increase parameters and computational costs, making it well-suited for small neuroimaging datasets. We evaluated our method on a challenging Traumatic Brain Injury (TBI) dataset collected from 13 sites, using labeled source data of only 14 healthy subjects. Experimental results confirmed the feasibility of using the Cycle-consistent adversarial network for harmonizing multi-site MR images, and demonstrated that TFD further improved the generalization of the vanilla segmentation model on TBI data, reaching comparable accuracy with that of the supervised learning. The code is available at https://github.com/YilinLiu97/Targeted-Feature-Dropout.git.

Notes

Acknowledgement

This work was supported by NARSAD: Brain and Behavior grant 24103 (to BN) and National Institutes of Health grant funding NINDS R01 NS092870, NIMH P50 MH100031 and a core grant to the Waisman Center from the National Institute of Child Health and Human Development (U54 HD090256). Disclosure Statement: A Alexander is part owner of Thervoyant, Inc. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp and the Telsa K80 used for this research.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yilin Liu
    • 1
  • Gregory R. Kirk
    • 1
  • Brendon M. Nacewicz
    • 1
  • Martin A. Styner
    • 2
    • 4
  • Mingren Shen
    • 3
  • Dong Nie
    • 4
  • Nagesh Adluru
    • 1
  • Benjamin Yeske
    • 1
  • Peter A. Ferrazzano
    • 1
  • Andrew L. Alexander
    • 1
    Email author
  1. 1.Waisman Laboratory for Brain Imaging and BehaviorUniversity of Wisconsin-MadisonMadisonUSA
  2. 2.Department of PsychiatryUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Department of Materials Science and EngineeringUniversity of Wisconsin-MadisonMadisonUSA
  4. 4.Department of Computer ScienceUniversity of North Carolina at Chapel ChillChapel HillUSA

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