Learning statistical correlation of prostate deformations for fast registration

Yonghong Shi, Shu Liao, Dinggang Shen

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

Abstract

This paper presents a novel fast registration method for aligning the planning image onto each treatment image of a patient for adaptive radiation therapy of the prostate cancer. Specifically, an online correspondence interpolation method is presented to learn the statistical correlation of the deformations between prostate boundary and non-boundary regions from a population of training patients, as well as from the online-collected treatment images of the same patient. With this learned statistical correlation, the estimated boundary deformations can be used to rapidly predict regional deformations between prostates in the planning and treatment images. In particular, the population-based correlation can be initially used to interpolate the dense correspondences when the number of available treatment images from the current patient is small. With the acquisition of more treatment images from the current patient, the patient-specific information gradually plays a more important role to reflect the prostate shape changes of the current patient during the treatment. Eventually, only the patient-specific correlation is used to guide the regional correspondence prediction, once a sufficient number of treatment images have been acquired and segmented from the current patient. Experimental results show that the proposed method can achieve much faster registration speed yet with comparable registration accuracy compared with the thin plate spline (TPS) based interpolation approach.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings
Pages1-9
Number of pages9
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2nd International Workshop on Machine Learning in Medical Imaging, MLMI 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: 2011 Sep 182011 Sep 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7009 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Workshop on Machine Learning in Medical Imaging, MLMI 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011
CountryCanada
CityToronto, ON
Period11/9/1811/9/18

Keywords

  • Adaptive radiation therapy
  • Canonical correlation analysis
  • Fast registration
  • Patient-specific statistical correlation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Shi, Y., Liao, S., & Shen, D. (2011). Learning statistical correlation of prostate deformations for fast registration. In Machine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings (pp. 1-9). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7009 LNCS). https://doi.org/10.1007/978-3-642-24319-6_1