One-shot generative adversarial learning for MRI segmentation of craniomaxillofacial bony structures

Xu Chen, James J. Xia, Dinggang Shen, Chunfeng Lian, Li Wang, Hannah Deng, Steve H. Fung, Dong Nie, Kim Han Thung, Pew Thian Yap, Jaime Gateno

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Compared to computed tomography (CT), magnetic resonance imaging (MRI) delineation of craniomaxillofacial (CMF) bony structures can avoid harmful radiation exposure. However, bony boundaries are blurry in MRI, and structural information needs to be borrowed from CT during the training. This is challenging since paired MRI-CT data are typically scarce. In this paper, we propose to make full use of unpaired data, which are typically abundant, along with a single paired MRI-CT data to construct a one-shot generative adversarial model for automated MRI segmentation of CMF bony structures. Our model consists of a cross-modality image synthesis sub-network, which learns the mapping between CT and MRI, and an MRI segmentation sub-network. These two sub-networks are trained jointly in an end-to-end manner. Moreover, in the training phase, a neighbor-based anchoring method is proposed to reduce the ambiguity problem inherent in cross-modality synthesis, and a feature-matching-based semantic consistency constraint is proposed to encourage segmentation-oriented MRI synthesis. Experimental results demonstrate the superiority of our method both qualitatively and quantitatively in comparison with the state-of-the-art MRI segmentation methods.

Original languageEnglish
Article number8798866
Pages (from-to)787-796
Number of pages10
JournalIEEE Transactions on Medical Imaging
Volume39
Issue number3
DOIs
Publication statusPublished - 2020 Mar

Keywords

  • Craniomaxillofacial bone segmentation
  • MRI
  • generative adversarial learning
  • one-shot learning

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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