Frontiers in Computational Neuroscience (Mar 2020)

Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation

  • Théo Estienne,
  • Théo Estienne,
  • Théo Estienne,
  • Théo Estienne,
  • Marvin Lerousseau,
  • Marvin Lerousseau,
  • Marvin Lerousseau,
  • Marvin Lerousseau,
  • Maria Vakalopoulou,
  • Maria Vakalopoulou,
  • Maria Vakalopoulou,
  • Emilie Alvarez Andres,
  • Emilie Alvarez Andres,
  • Emilie Alvarez Andres,
  • Enzo Battistella,
  • Enzo Battistella,
  • Enzo Battistella,
  • Enzo Battistella,
  • Alexandre Carré,
  • Alexandre Carré,
  • Alexandre Carré,
  • Siddhartha Chandra,
  • Stergios Christodoulidis,
  • Mihir Sahasrabudhe,
  • Roger Sun,
  • Roger Sun,
  • Roger Sun,
  • Roger Sun,
  • Charlotte Robert,
  • Charlotte Robert,
  • Charlotte Robert,
  • Hugues Talbot,
  • Nikos Paragios,
  • Eric Deutsch,
  • Eric Deutsch,
  • Eric Deutsch

DOI
https://doi.org/10.3389/fncom.2020.00017
Journal volume & issue
Vol. 14

Abstract

Read online

Image registration and segmentation are the two most studied problems in medical image analysis. Deep learning algorithms have recently gained a lot of attention due to their success and state-of-the-art results in variety of problems and communities. In this paper, we propose a novel, efficient, and multi-task algorithm that addresses the problems of image registration and brain tumor segmentation jointly. Our method exploits the dependencies between these tasks through a natural coupling of their interdependencies during inference. In particular, the similarity constraints are relaxed within the tumor regions using an efficient and relatively simple formulation. We evaluated the performance of our formulation both quantitatively and qualitatively for registration and segmentation problems on two publicly available datasets (BraTS 2018 and OASIS 3), reporting competitive results with other recent state-of-the-art methods. Moreover, our proposed framework reports significant amelioration (p < 0.005) for the registration performance inside the tumor locations, providing a generic method that does not need any predefined conditions (e.g., absence of abnormalities) about the volumes to be registered. Our implementation is publicly available online at https://github.com/TheoEst/joint_registration_tumor_segmentation.

Keywords