Learning-based multimodal image registration for prostate cancer radiation therapy

Xiaohuan Cao, Yaozong Gao, Jianhua Yang, Guorong Wu, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

Abstract

Computed tomography (CT) is widely used for dose planning in the radiotherapy of prostate cancer. However,CT has low tissue contrast,thus making manual contouring difficult. In contrast,magnetic resonance (MR) image provides high tissue contrast and is thus ideal for manual contouring. If MR image can be registered to CT image of the same patient,the contouring accuracy of CT could be substantially improved,which could eventually lead to high treatment efficacy. In this paper,we propose a learning-based approach for multimodal image registration. First,to fill the appearance gap between modalities,a structured random forest with auto-context model is learnt to synthesize MRI from CT and vice versa. Then,MRI-to-CT registration is steered in a dual manner of registering images with same appearances,i.e.,(1) registering the synthesized CT with CT,and (2) also registering MRI with the synthesized MRI. Next,a dual-core deformation fusion framework is developed to iteratively and effectively combine these two registration results. Experiments on pelvic CT and MR images have shown the improved registration performance by our proposed method,compared with the existing nonlearning based registration methods.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsLeo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal, Sebastian Ourselin
PublisherSpringer Verlag
Pages1-9
Number of pages9
ISBN (Print)9783319467252
DOIs
Publication statusPublished - 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9902 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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