Research
Print page Print page
Switch language
Rigshospitalet - a part of Copenhagen University Hospital
Published

Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images

Research output: Contribution to journalJournal articleResearchpeer-review

  1. The structure of the serotonin system: A PET imaging study

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. Validity and reliability of extrastriatal [11C]raclopride binding quantification in the living human brain

    Research output: Contribution to journalJournal articleResearchpeer-review

  3. Optimization of preprocessing strategies in Positron Emission Tomography (PET) neuroimaging: A [11C]DASB PET study

    Research output: Contribution to journalJournal articleResearchpeer-review

  4. Functional neuroimaging of recovery from motor conversion disorder: A case report

    Research output: Contribution to journalJournal articleResearchpeer-review

  5. Men with high serotonin 1B receptor binding respond to provocations with heightened amygdala reactivity

    Research output: Contribution to journalJournal articleResearchpeer-review

  1. The structure of the serotonin system: A PET imaging study

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. Optimization of preprocessing strategies in Positron Emission Tomography (PET) neuroimaging: A [11C]DASB PET study

    Research output: Contribution to journalJournal articleResearchpeer-review

  3. Cerebral serotonin transporter measurements with [11C]DASB: A review on acquisition and preprocessing across 21 PET centres

    Research output: Contribution to journalJournal articleResearchpeer-review

  4. Multi-view Consensus CNN for 3D Facial Landmark Placement

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  5. Preprocessing, Prediction, and Significance: Framework and Application to Brain Imaging

    Research output: Contribution to conferencePaperCommunication

  • Aaron Carass
  • Jennifer L Cuzzocreo
  • Shuo Han
  • Carlos R Hernandez-Castillo
  • Paul E Rasser
  • Melanie Ganz
  • Vincent Beliveau
  • Jose Dolz
  • Ismail Ben Ayed
  • Christian Desrosiers
  • Benjamin Thyreau
  • José E Romero
  • Pierrick Coupé
  • José V Manjón
  • Vladimir S Fonov
  • D Louis Collins
  • Sarah H Ying
  • Chiadi U Onyike
  • Deana Crocetti
  • Bennett A Landman
  • Stewart H Mostofsky
  • Paul M Thompson
  • Jerry L Prince
View graph of relations

The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method.

Original languageEnglish
JournalNeuroImage
Volume183
Pages (from-to)150-172
Number of pages23
ISSN1053-8119
DOIs
Publication statusPublished - Dec 2018

ID: 55237200