A comparison of methods for the automated calculation of volumes and atrophy rates in the hippocampus

Barnes, Josephine; Foster, Jo; Boyes, Richard G.; Pepple, Tracey; Moore, Elizabeth; Schott, Jonathan M.; Frost, Chris; Scahill, Rachael I.; Fox, Nick C.

doi:10.1016/j.neuroimage.2008.01.012

Cited by 106 publications

(98 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A number of strategies can be used to propose an accurate initialization, such as matching the best subject (Barnes et al, 2008) followed by a morphological dilation of the mask. In this case, we chose a very fast and simple approach that uses the union of all the expert segmentations in the training database as the initial mask.…”

Section: Fig 1 Preprocessingmentioning

confidence: 99%

“…For both datasets, the impact of the patch size, search volume size, and number of training subjects was studied. Moreover, the proposed patch-based method was compared for both datasets with an appearance-based approach using level-set shape constraints (Hu and Collins, 2007) and a template-based technique inspired by Barnes et al (2008) that uses ANIMAL (Collins et al, 1995) for the nonlinear registration of the best subject.…”

Section: Validation Frameworkmentioning

confidence: 99%

“…For the template-based method inspired by Barnes et al (2008), the best subject was selected using the normalized mutual information, as suggested by Aljabar et al (2009). This subject was then nonlinearly warped to the subject under study with ANIMAL (Collins et al, 1995) within a multiresolution framework until a resolution of 2 mm.…”

Section: Validation Frameworkmentioning

confidence: 99%

“…Compared with other approaches, the appearance-based method (Hu and Collins, 2007) took around 45 s to provide the segmentation of the HC. By contrast, the best template-based approach inspired by Barnes et al (2008) required around 6 min to achieve the nonlinear registration of the cropped images already linearly registered into stereotaxic space. Although comparing these approaches was difficult because our method was coded in C-MEX for MATLAB and not in C like the other two, these results show that the PCA-based approach was quite fast, though at the expense of accuracy.…”

Section: Computational Timementioning

confidence: 99%

“…In contrast to brain tissue classification where the intensity of the MR signal can be used to segment different tissue types, anatomical segmentation usually requires information derived from the manual segmentations done by experts (i.e., expert priors), since anatomical structures can be composed of several tissue types and distinct anatomical structures can have the same MR signal properties. To overcome this difficulty, several automatic methods of segmentation have been proposed, such as deformable models or region growing (Chupin et al, 2007;Ghanei et al, 1998;Shen et al, 2002), appearance-based models (Duchesne et al, 2002;Hu and Collins, 2007), and atlas/template-warping techniques (Aljabar et al, 2009;Barnes et al, 2008;Collins et al, 1995;Fischl et al, 2002;Gousias et al, 2008;Hammers et al, 2007;Heckemann et al, 2006;Rohlfing et al, 2004;Zhou and Rajapakse, 2005).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation

et al. 2011

View full text Add to dashboard Cite

Section: Fig 1 Preprocessingmentioning

confidence: 99%

Section: Validation Frameworkmentioning

confidence: 99%

Section: Validation Frameworkmentioning

confidence: 99%

Section: Computational Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation

et al. 2011

View full text Add to dashboard Cite

Segmentation errors and intertest reliability in automated and manually traced hippocampal volumes

Brinkmann

Guragain

Kenney‐Jung

et al. 2019

Ann Clin Transl Neurol

View full text Add to dashboard Cite

Objective To rigorously compare automated atlas‐based and manual tracing hippocampal segmentation for accuracy, repeatability, and clinical acceptability given a relevant range of imaging abnormalities in clinical epilepsy. Methods Forty‐nine patients with hippocampal asymmetry were identified from our institutional radiology database, including two patients with significant anatomic deformations. Manual hippocampal tracing was performed by experienced technologists on 3T MPRAGE images, measuring hippocampal volume up to the tectal plate, excluding the hippocampal tail. The same images were processed using NeuroQuant and FreeSurfer software. Ten subjects underwent repeated manual hippocampal tracings by two additional technologists blinded to previous results to evaluate consistency. Ten patients with two clinical MRI studies had volume measurements repeated using NeuroQuant and FreeSurfer. Results FreeSurfer raw volumes were significantly lower than NeuroQuant (P < 0.001, right and left), and hippocampal asymmetry estimates were lower for both automatic methods than manual tracing (P < 0.0001). Differences remained significant after scaling volumes to age, gender, and scanner matched normative percentiles. Volume reproducibility was fair (0.4–0.59) for manual tracing, and excellent (>0.75) for both automated methods. Asymmetry index reproducibility was excellent (>0.75) for manual tracing and FreeSurfer segmentation and fair (0.4–0.59) for NeuroQuant segmentation. Both automatic segmentation methods failed on the two cases with anatomic deformations. Segmentation errors were visually identified in 25 NeuroQuant and 27 FreeSurfer segmentations, and nine (18%) NeuroQuant and six (12%) FreeSurfer errors were judged clinically significant. Interpretation Automated hippocampal volumes are more reproducible than hand‐traced hippocampal volumes. However, these methods fail in some cases, and significant segmentation errors can occur.

show abstract

Scan–rescan reliability of subcortical brain volumes derived from automated segmentation

Morey

Selgrade

Wagner

et al. 2010

Human Brain Mapping

193

178

View full text Add to dashboard Cite

Large-scale longitudinal studies of regional brain volume require reliable quantification using automated segmentation and labeling. However, repeated MR scanning of the same subject, even if using the same scanner and acquisition parameters, does not result in identical images due to small changes in image orientation, changes in prescan parameters, and magnetic field instability. These differences may lead to appreciable changes in estimates of volume for different structures. This study examined scan–rescan reliability of automated segmentation algorithms for measuring several subcortical regions, using both within-day and across-day comparison sessions in a group of 23 normal participants. We found that the reliability of volume measures including percent volume difference, percent volume overlap (Dice’s coefficient), and intraclass correlation coefficient (ICC), varied substantially across brain regions. Low reliability was observed in some structures such as the amygdala (ICC = 0.6), with higher reliability (ICC = 0.9) for other structures such as the thalamus and caudate. Patterns of reliability across regions were similar for automated segmentation with FSL/FIRST and FreeSurfer (longitudinal stream). Reliability was associated with the volume of the structure, the ratio of volume to surface area for the structure, the magnitude of the interscan interval, and the method of segmentation. Sample size estimates for detecting changes in brain volume for a range of likely effect sizes also differed by region. Thus, longitudinal research requires a careful analysis of sample size and choice of segmentation method combined with a consideration of the brain structure(s) of interest and the magnitude of the anticipated effects.

show abstract

A comparison of methods for the automated calculation of volumes and atrophy rates in the hippocampus

Cited by 106 publications

References 54 publications

Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation

Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation

Segmentation errors and intertest reliability in automated and manually traced hippocampal volumes

Scan–rescan reliability of subcortical brain volumes derived from automated segmentation

Contact Info

Product

Resources

About