Arrow Research search

Author name cluster

Maryam E. Rettmann

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers
1 author row

Possible papers

8

YNIMG Journal 2006 Journal Article

Cortical reconstruction using implicit surface evolution: Accuracy and precision analysis

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

Two different studies were conducted to assess the accuracy and precision of an algorithm developed for automatic reconstruction of the cerebral cortex from T1-weighted magnetic resonance (MR) brain images. Repeated scans of three different brains were used to quantify the precision of the algorithm, and manually selected landmarks on different sulcal regions throughout the cortex were used to analyze the accuracy of the three reconstructed surfaces: inner, central, and pial. We conclude that the algorithm can find these surfaces in a robust fashion and with subvoxel accuracy, typically with an accuracy of one third of a voxel, although this varies with brain region and cortical geometry. Parameters were adjusted on the basis of this analysis in order to improve the algorithm's overall performance.

Details DOI

YNIMG Journal 2005 Journal Article

Program for Assisted Labeling of Sulcal Regions (PALS): description and reliability

  • Maryam E. Rettmann
  • Duygu Tosun
  • Xiaodong Tao
  • Susan M. Resnick
  • Jerry L. Prince

With the improvements in techniques for generating surface models from magnetic resonance (MR) images, it has recently become feasible to study the morphological characteristics of the human brain cortex in vivo. Studies of the entire surface are important for measuring global features, but analysis of specific cortical regions of interest provides a more detailed understanding of structure. We have previously developed a method for automatically segmenting regions of interest from the cortical surface using a watershed transform. Each segmented region corresponds to a cortical sulcus and is thus termed a “sulcal region. ” In this work, we describe two important augmentations of this methodology. First, we describe a user interface that allows for the efficient labeling of the segmented sulcal regions called the Program for Assisted Labeling of Sulcal Regions (PALS). An additional augmentation allows for even finer divisions on the cortex with a methodology that employs the fast marching technique to track a curve on the cortical surface that is then used to separate segmented regions. After regions of interest have been identified, we compute both the cortical surface area and gray matter volume. Reliability experiments are performed to assess both the long-term stability and short-term repeatability of the proposed techniques. These experiments indicate the proposed methodology gives both highly stable and repeatable results.

Details DOI

YNIMG Journal 2004 Journal Article

Cortical surface segmentation and mapping

  • Duygu Tosun
  • Maryam E. Rettmann
  • Xiao Han
  • Xiaodong Tao
  • Chenyang Xu
  • Susan M. Resnick
  • Dzung L. Pham
  • Jerry L. Prince

Segmentation and mapping of the human cerebral cortex from magnetic resonance (MR) images plays an important role in neuroscience and medicine. This paper describes a comprehensive approach for cortical reconstruction, flattening, and sulcal segmentation. Robustness to imaging artifacts and anatomical consistency are key achievements in an overall approach that is nearly fully automatic and computationally fast. Results demonstrating the application of this approach to a study of cortical thickness changes in aging are presented.

Details DOI

YNIMG Journal 2004 Journal Article

CRUISE: Cortical reconstruction using implicit surface evolution

  • Xiao Han
  • Dzung L. Pham
  • Duygu Tosun
  • Maryam E. Rettmann
  • Chenyang Xu
  • Jerry L. Prince

Segmentation and representation of the human cerebral cortex from magnetic resonance (MR) images play an important role in neuroscience and medicine. A successful segmentation method must be robust to various imaging artifacts and produce anatomically meaningful and consistent cortical representations. A method for the automatic reconstruction of the inner, central, and outer surfaces of the cerebral cortex from T1-weighted MR brain images is presented. The method combines a fuzzy tissue classification method, an efficient topology correction algorithm, and a topology-preserving geometric deformable surface model (TGDM). The algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self-intersections. Validation results on real MR data are presented to demonstrate the performance of the method.

Details DOI

YNIMG Journal 2002 Journal Article

Automated Sulcal Segmentation Using Watersheds on the Cortical Surface

  • Maryam E. Rettmann
  • Xiao Han
  • Chenyang Xu
  • Jerry L. Prince

The human cortical surface is a highly complex, folded structure. Sulci, the spaces between the folds, define location on the cortex and provide a parcellation into anatomically distinct areas. A topic that has recently received increased attention is the segmentation of these sulci from magnetic resonance images, with most work focusing on extracting either the sulcal spaces between the folds or curve representations of sulci. Unlike these methods, we propose a technique that extracts actual regions of the cortical surface that surround sulci, which we call “sulcal regions. ” The method is based on a watershed algorithm applied to a geodesic depth measure on the cortical surface. A well-known problem with the watershed algorithm is a tendency toward oversegmentation, meaning that a single region is segmented as several pieces. To address this problem, we propose a postprocessing algorithm that merges appropriate segments from the watershed algorithm. The sulcal regions are then manually labeled by simply selecting the appropriate regions with a mouse click and a preliminary study of sulcal depth is reported. Finally, a scheme is presented for computing a complete parcellation of the cortical surface.

Details DOI
v2026.09.13