IOUC-3DSFCNN: Segmentation of Brain Tumors via IOU Constraint 3D Symmetric Full Convolution Network with Multimodal Auto-context

Sci Rep. 2020 Apr 10;10(1):6256. doi: 10.1038/s41598-020-63242-x.


Accurate segmentation of brain tumors from magnetic resonance (MR) images play a pivot role in assisting diagnoses, treatments and postoperative evaluations. However, due to its structural complexities, e.g., fuzzy tumor boundaries with irregular shapes, accurate 3D brain tumor delineation is challenging. In this paper, an intersection over union (IOU) constraint 3D symmetric full convolutional neural network (IOUC-3DSFCNN) model fused with multimodal auto-context is proposed for the 3D brain tumor segmentation. IOUC-3DSFCNN incorporates 3D residual groups into the classic 3DU-Net to further deepen the network structure to obtain more abstract voxel features under a five-layer cohesion architecture to ensure the model stability. The IOU constraint is used to address the issue of extremely unbalanced tumor foreground and background regions in MR images. In addition, to obtain more comprehensive and stable 3D brain tumor profiles, the multimodal auto-context information is fused into the IOUC-3DSFCNN model to achieve end-to-end 3D brain tumor profiles. Extensive confirmatory and comparative experiments conducted on the benchmark BRATS 2017 dataset demonstrate that the proposed segmentation model is superior to classic 3DU-Net-relevant and other state-of-the-art segmentation models, which can achieve accurate 3D tumor profiles on multimodal MRI volumes even with blurred tumor boundaries and big noise.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Brain Neoplasms / diagnostic imaging*
  • Datasets as Topic
  • Deep Learning*
  • Humans
  • Imaging, Three-Dimensional / methods*
  • Magnetic Resonance Imaging / methods*
  • Models, Statistical
  • Reproducibility of Results