Automatic outlier detection using hidden Markov model for cerebellar lobule segmentation

Lianrui Zuo, Aaron Carass, Shuo Han, Jerry Ladd Prince

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

Abstract

The cerebellum plays an important role in both motor control and cognitive functions. Several methods to automatically segment different regions of the cerebellum have been recently proposed. Usually, the performance of the segmentation algorithms is evaluated by comparing with expert delineations. However, this is a laboratory approach and is not applicable in real scenarios where expert delineations are not available. In this paper, we propose a method that can automatically detect cerebellar lobule segmentation outliers. Instead of only evaluating the final segmentation result, the intermediate output of each segmentation step is evaluated and considered using a Hidden Markov Model (HMM) to produce a global segmentation assessment. For each intermediate step, a state-of-the-art image classification model ag-of-Words (BoW) is applied to quantize features of segmentation results, which then serves as observations of the trained HMM. Experiments show that the proposed method achieves both a high accuracy on predicting Dice of upcoming segmentation steps, and a high sensitivity to outlier detection.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2018
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsBarjor Gimi, Andrzej Krol
PublisherSPIE
Volume10578
ISBN (Electronic)9781510616455
DOIs
Publication statusPublished - Jan 1 2018
EventMedical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging - Houston, United States
Duration: Feb 11 2018Feb 13 2018

Other

OtherMedical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
CountryUnited States
CityHouston
Period2/11/182/13/18

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Keywords

  • cerebellum
  • Outlier detection
  • quality assurance
  • segmentation

ASJC Scopus subject areas

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

Cite this

Zuo, L., Carass, A., Han, S., & Prince, J. L. (2018). Automatic outlier detection using hidden Markov model for cerebellar lobule segmentation. In B. Gimi, & A. Krol (Eds.), Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging (Vol. 10578). [105780D] SPIE. https://doi.org/10.1117/12.2295709