Cortical reconstruction using implicit surface evolution: A landmark validation study

Duygu Tosun; Maryam E. Rettmann; Daniel Q. Naiman; Susan M. Resnick; Michael A. Kraut; Jerry L. Prince

doi:10.1007/978-3-540-30135-6_47

Cortical reconstruction using implicit surface evolution: A landmark validation study

Duygu Tosun, Maryam E. Rettmann, Daniel Q. Naiman, Susan M. Resnick, Michael A. Kraut, Jerry L. Prince

Cardiovascular Medicine

Research output: Contribution to journal › Conference article › peer-review

Abstract

A validation study was conducted to assess the accuracy of an algorithm developed for automatic reconstruction of the cerebral cortex from T1-weighted magnetic resonance (MR) brain images. Manually selected landmarks on different sulcal regions throughout the cortex were used to analyze the accuracy of three reconstructed nested surfaces - the inner, central, and pial surfaces. We conclude that the algorithm can find these surfaces with subvoxel accuracy, typically with an accuracy of one third of a voxel, although this varies by brain region and cortical geometry. Parameters were adjusted on the basis of this analysis in order to improve the algorithm's overall performance. ¹

Original language	English (US)
Pages (from-to)	384-392
Number of pages	9
Journal	Lecture Notes in Computer Science
Volume	3216
Issue number	PART 1
DOIs	https://doi.org/10.1007/978-3-540-30135-6_47
State	Published - 2004
Event	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings - Saint-Malo, France Duration: Sep 26 2004 → Sep 29 2004

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-540-30135-6_47

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title = "Cortical reconstruction using implicit surface evolution: A landmark validation study",

abstract = "A validation study was conducted to assess the accuracy of an algorithm developed for automatic reconstruction of the cerebral cortex from T1-weighted magnetic resonance (MR) brain images. Manually selected landmarks on different sulcal regions throughout the cortex were used to analyze the accuracy of three reconstructed nested surfaces - the inner, central, and pial surfaces. We conclude that the algorithm can find these surfaces with subvoxel accuracy, typically with an accuracy of one third of a voxel, although this varies by brain region and cortical geometry. Parameters were adjusted on the basis of this analysis in order to improve the algorithm's overall performance. 1",

author = "Duygu Tosun and Rettmann, {Maryam E.} and Naiman, {Daniel Q.} and Resnick, {Susan M.} and Kraut, {Michael A.} and Prince, {Jerry L.}",

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T1 - Cortical reconstruction using implicit surface evolution

T2 - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings

AU - Tosun, Duygu

AU - Rettmann, Maryam E.

AU - Naiman, Daniel Q.

AU - Resnick, Susan M.

AU - Kraut, Michael A.

AU - Prince, Jerry L.

PY - 2004

Y1 - 2004

N2 - A validation study was conducted to assess the accuracy of an algorithm developed for automatic reconstruction of the cerebral cortex from T1-weighted magnetic resonance (MR) brain images. Manually selected landmarks on different sulcal regions throughout the cortex were used to analyze the accuracy of three reconstructed nested surfaces - the inner, central, and pial surfaces. We conclude that the algorithm can find these surfaces with subvoxel accuracy, typically with an accuracy of one third of a voxel, although this varies by brain region and cortical geometry. Parameters were adjusted on the basis of this analysis in order to improve the algorithm's overall performance. 1

AB - A validation study was conducted to assess the accuracy of an algorithm developed for automatic reconstruction of the cerebral cortex from T1-weighted magnetic resonance (MR) brain images. Manually selected landmarks on different sulcal regions throughout the cortex were used to analyze the accuracy of three reconstructed nested surfaces - the inner, central, and pial surfaces. We conclude that the algorithm can find these surfaces with subvoxel accuracy, typically with an accuracy of one third of a voxel, although this varies by brain region and cortical geometry. Parameters were adjusted on the basis of this analysis in order to improve the algorithm's overall performance. 1

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SN - 0302-9743

VL - 3216

SP - 384

EP - 392

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

IS - PART 1

Y2 - 26 September 2004 through 29 September 2004

ER -

Cortical reconstruction using implicit surface evolution: A landmark validation study

Abstract

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