iSTAPLE: Improved label fusion for segmentation by combining STAPLE with image intensity

Xiaofeng Liu, Albert Montillo, Ek T. Tan, John F. Schenck

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

8 Scopus citations

Abstract

Multi-atlas based methods have been a trend for robust and automated image segmentation. In general these methods first transfer prior manual segmentations, i.e., label maps, on a set of atlases to a given target image through image registration. These multiple label maps are then fused together to produce segmentations of the target image through voting strategy or statistical fusing, e.g., STAPLE. STAPLE simultaneously estimates the true segmentation and the label map performance level, but has been shown inaccurate for multi-atlas segmentation because it is determined completely on the propagated label maps without considering the target image intensity. We develop a new method, called iSTAPLE, that combines target image intensity into a similar maximum likelihood estimate (MLE) framework as in STAPLE to take advantage of both intensity-based segmentation and statistical label fusion based on atlas consensus and performance level. The MLE framework is then solved using a modified EM algorithm to simultaneously estimate the intensity profiles of structures of interest as well as the true segmentation and atlas performance level. Unlike other methods, iSTAPLE does not require the target image to have same image contrast and intensity range as the atlas images, which greatly extends the use of atlases. Experiments on whole brain segmentation showed that iSTAPLE performed consistently better than STAPLE.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2013
Subtitle of host publicationImage Processing
DOIs
StatePublished - Jun 3 2013
EventMedical Imaging 2013: Image Processing - Lake Buena Vista, FL, United States
Duration: Feb 10 2013Feb 12 2013

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume8669
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2013: Image Processing
CountryUnited States
CityLake Buena Vista, FL
Period2/10/132/12/13

Keywords

  • Brain segmentation
  • Label fusion
  • STAPLE

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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    Liu, X., Montillo, A., Tan, E. T., & Schenck, J. F. (2013). iSTAPLE: Improved label fusion for segmentation by combining STAPLE with image intensity. In Medical Imaging 2013: Image Processing [86692O] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 8669). https://doi.org/10.1117/12.2006447