Automatic outlier detection using hidden Markov model for cerebellar lobule segmentation

Lianrui Zuo; Aaron Carass; Shuo Han; Jerry L. Prince

doi:10.1117/12.2295709

12 March 2018 Automatic outlier detection using hidden Markov model for cerebellar lobule segmentation

Lianrui Zuo, Aaron Carass, Shuo Han, Jerry L. Prince

Proceedings Volume 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging; 105780D (2018) https://doi.org/10.1117/12.2295709
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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 Bag-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.

Conference Presentation

Citation Download Citation

Lianrui Zuo, Aaron Carass, Shuo Han, and Jerry L. Prince "Automatic outlier detection using hidden Markov model for cerebellar lobule segmentation", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105780D (12 March 2018); https://doi.org/10.1117/12.2295709

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