Accounting for the Confound of Meninges in Segmenting Entorhinal and Perirhinal Cortices in T1-Weighted MRI

  • Long XieEmail author
  • Laura E. M. Wisse
  • Sandhitsu R. Das
  • Hongzhi Wang
  • David A. Wolk
  • Jose V. Manjón
  • Paul A. Yushkevich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)


Quantification of medial temporal lobe (MTL) cortices, including entorhinal cortex (ERC) and perirhinal cortex (PRC), from in vivo MRI is desirable for studying the human memory system as well as in early diagnosis and monitoring of Alzheimer’s disease. However, ERC and PRC are commonly over-segmented in T1-weighted (T1w) MRI because of the adjacent meninges that have similar intensity to gray matter in T1 contrast. This introduces errors in the quantification and could potentially confound imaging studies of ERC/PRC. In this paper, we propose to segment MTL cortices along with the adjacent meninges in T1w MRI using an established multi-atlas segmentation framework together with super-resolution technique. Experimental results comparing the proposed pipeline with existing pipelines support the notion that a large portion of meninges is segmented as gray matter by existing algorithms but not by our algorithm. Cross-validation experiments demonstrate promising segmentation accuracy. Further, agreement between the volume and thickness measures from the proposed pipeline and those from the manual segmentations increase dramatically as a result of accounting for the confound of meninges. Evaluated in the context of group discrimination between patients with amnestic mild cognitive impairment and normal controls, the proposed pipeline generates more biologically plausible results and improves the statistical power in discriminating groups in absolute terms comparing to other techniques using T1w MRI. Although the performance of the proposed pipeline is inferior to that using T2-weighted MRI, which is optimized to image MTL sub-structures, the proposed pipeline could still provide important utilities in analyzing many existing large datasets that only have T1w MRI available.


Gray Matter Medial Temporal Lobe Automatic Segmentation Manual Segmentation Segmentation Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by NIH (grant numbers R01-AG040271, P30-AG010124, R01-AG037376, R01-EB017255).


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Long Xie
    • 1
    Email author
  • Laura E. M. Wisse
    • 1
  • Sandhitsu R. Das
    • 1
    • 3
  • Hongzhi Wang
    • 5
  • David A. Wolk
    • 2
    • 3
  • Jose V. Manjón
    • 4
  • Paul A. Yushkevich
    • 1
  1. 1.Penn Image Computing and Science Laboratory (PICSL), Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Penn Memory CenterUniversity of PennsylvaniaPhiladelphiaUSA
  3. 3.Department of NeurologyUniversity of PennsylvaniaPhiladelphiaUSA
  4. 4.Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA)Universidad Politécnica de ValenciaValenciaSpain
  5. 5.IBM Almaden Research CenterSan JoseUSA

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