Spectral-based automatic labeling and refining of human cortical sulcal curves using expert-provided examples

Ilwoo Lyu, Joon Kyung Seong, Sung Yong Shin, Kiho Im, Jee Hoon Roh, Min Jeong Kim, Geon Ha Kim, Jong Hun Kim, Alan C. Evans, Duk L. Na, Jong Min Lee

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


We present a spectral-based method for automatically labeling and refining major sulcal curves of a human cerebral cortex. Given a set of input (unlabeled) sulcal curves automatically extracted from a cortical surface and a collection of expert-provided examples (labeled sulcal curves), our objective is to identify the input major sulcal curves and assign their neuroanatomical labels, and then refines these curves based on the expert-provided example data, without employing any atlas-based registration scheme as preprocessing. In order to construct the example data, neuroanatomists manually labeled a set of 24 major sulcal curves (12 each for the left and right hemispheres) for each individual subject according to a precise protocol. We collected 30 sets of such curves from 30 subjects. Given the raw input sulcal curve set of a subject, we choose the most similar example curve to each input curve in the set to label and refine the latter according to the former. We adapt a spectral matching algorithm to choose the example curve by exploiting the sulcal curve features and their relationship. The high dimensionality of sulcal curve data in spectral matching is addressed by using their multi-resolution representations, which greatly reduces time and space complexities. Our method provides consistent labeling and refining results even under high variability of cortical sulci across the subjects. Through experiments we show that the results are comparable in accuracy to those done manually. Most output curves exhibited accuracy values higher than 80%, and the mean accuracy values of the curves in the left and the right hemispheres were 84.69% and 84.58%, respectively.

Original languageEnglish
Pages (from-to)142-157
Number of pages16
Issue number1
Publication statusPublished - 2010 Aug
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by Brain Korea Project, the School of Information Technology, KAIST in 2010 , by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MEST) ( 2009-0077290 ), and by the Korea Science and Engineering Foundation (KOSEF) NRL program grant funded by the Korea government (MEST) ( R0A-2007-000-20068-0 ). All opinions, findings, conclusions or recommendations expressed in this document are those of the author and do not necessarily reflect the views of the sponsoring agencies.


  • Labeling
  • Refining
  • Spectral matching
  • Sulcal curve
  • Sulcal variability

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


Dive into the research topics of 'Spectral-based automatic labeling and refining of human cortical sulcal curves using expert-provided examples'. Together they form a unique fingerprint.

Cite this