Rebuttal to Hasan and Pedraza in comments and controversies: "Improving the reliability of manual and automated methods for hippocampal and amygdala volume measurements"

Here we address the critiques offered by Hasan and Pedraza to our recently published manuscript comparing the performance of two automated segmentation programs, FSL/FIRST and FreeSurfer (Morey R, Petty C, Xu Y, Pannu Hayes J, Wagner H, Lewis D, LaBar K, Styner M, McCarthy G. (2009): A comparison of automated segmentation and manual tracing for quantifying of hippocampal and amygdala volumes. Neuroimage 45:855-866). We provide an assessment and discussion of their specific critiques. Hasan and Pedraza bring up some important points concerning our omission of sample demographic features and inclusion of left and right hemisphere volumes as independent measures in correlational analyses. We present additional data on demographic attributes of our sample and correlations analyzed separately on left and right hemispheres of the amygdala and hippocampus. While their commentary aids the reader to more critically asses our study, it falls short of substantiating that our omissions ought to lead readers to significantly revise their interpretations. Further research will help to disentangle the advantages and limitations of the various freely-available automated segmentation software packages

The investigation of hippocampal and hippocampal subfield volumetry, morphology and metabolites using 3T MRI

A detailed account of the hippocampal anatomy has been provided. This thesis will explore and exploit the use of 3T MRI and the latest developments in image processing techniques to measure hippocampal and hippocampal subfield volumes, hippocampal metabolites and morphology. In chapter two a protocol for segmenting the hippocampus was created. The protocol was assessed in two groups of subjects with differing socioeconomic status (SES). This was a novel, community based sample in which hippocampal volumes have yet to be assessed in the literature. Manual and automated hippocampal segmentation measurements were compared on the two distinct SES groups. The mean volumes and also the variance in these measurements were comparable between two methods. The Dice overlapping metric comparing the two methods was 0.81. In chapter three voxel based morphometry (VBM) was used to compare local volume differences in grey matter volume between the two SES groups. Two approaches to VBM were compared. DARTEL-VBM results were found to be superior to the earlier ’optimised’ VBM method. Following a small volume correction, DARTEL-VBM results were suggesitive of focal GM volumes reductions in both the right and left hippocampi of the lower SES group. In chapter four an MR spectroscopy protocol was implemented to assess hippocampal metabolites in the two differing SES groups. Interpretable spectra were obtained in 73% of the 42 subjects. The poorer socioeconomic group were considered to have been exposed to chronic stress and therefore via inflammatory processes it was anticipated that the NAA/Cr metabolite ratio would be reduced in this group when compared to the more affluent group. Both NAA/Cr and Cho/Cr hippocampal metabolite ratios were not significantly different between the two groups. The aim of chapter 5 was to implement the protocol and methodology developed in chapter 2 to determine a normal range for hippocampal volumes at 3T MRI. 3D T1-weighted IR-FSPGR images were acquired in 39 healthy, normal volunteers in the age range from 19 to 64. Following the automated procedure hippocampal volumes were manually inspected and edited. The mean and standard deviation of the left and right hippocampal volumes were determined to be: 3421mm3 ± 399mm3 and 3487mm3 ± 431mm3 respectively. After correcting for total ICV the volumes were: 0.22% ± 0.03% and 0.23% ± 0.03% for the left and right hippocampi respectively. Thus, a normative database of hippocampal volumes was established. The normative data here will in future act as a baseline on which other methods of determining hippocampal volumes may be compared. The utility of using the normative dataset to compare other groups of subjects will be limited as a result of the lack of a comprehensive assessment of IQ or education level of the normal volunteers which may affect the volume of the hippocampus. In chapter six Incomplete hippocampal inversion (IHI) was assessed. Few studies have assessed the normal incidence of IHI and of those studies the analysis of IHI extended only to a radiological assessment. Here we present a comprehensive and quantitative assessment of IHI. IHI was found on 31 of the 84 normal subjects assessed (37%). ICV corrected IHI left-sided hippocampal volumes were compared against ICV corrected normal left-sided hippocampal volumes (25 vs. 52 hippocampi). The IHI hippocampal volumes were determined to be smaller than the normal hippocampal volumes (p<< 0.05). However, on further inspection it was observed that the ICV of the IHI was significantly smaller than the ICV of the normal group, confounding the previous result. In chapter seven a pilot study was performed on patients with Rheumatoid Arthritis (RA). The aim was to exploit the improved image quality offered by the 3T MRI to create a protocol for assessing the CA4/ dentate volume and to compare the volume of this subfield of the hippocampus before and after treatment. Two methodologies were implemented. In the first method a protocol was produced to manually segment the CA4/dentate region of the hippocampus from coronal T2-weighted FSE images. Given that few studies have assessed hippocampal subfields, an assessment of study power and sample size was conducted to inform future work. In the second method, the data the DARTEL-VBM image processing pipeline was applied. Statistical nonparametric mapping was applied in the final statistical interpretation of the VBM data. Following an FDR correction, a single GM voxel in the hippocampus was deemed to be statistically significant, this was suggestive of small GM volume increase following antiinflammatory treatment. Finally, in chapter eight, the manual segmentation protocol for the CA4/dentate hippocampal subfield developed in chapter seven was extended to include a complete set of hippocampal subfields. This is one of the first attempts to segment the entire hippocampus into its subfields using 3T MRI and as such, it was important to assess the quality of the measurement procedure. Furthermore, given the subfield volumes and the variability in these measurements, power and sample size calculations were also estimated to inform further work. Seventeen healthy volunteers were scanned using 3T MRI. A detailed manual segmentation protocol was created to guide two independent operators to measure the hippocampal subfield volumes. Repeat measures were made by a single operator for intra-operator variability and inter-operator variability was also assessed. The results of the intra-operator comparison proved reasonably successful where values compared well but were typically slightly poorer than similar attempts in the literature. This was likely to be the result of the additional complication of trying to segment subfields in the head and tail of the hippocampus where previous studies have focused only on the body of the hippocampus. Inter-rater agreement measures for subfield volumes were generally poorer than would be acceptable if full exchangeability of the data between the raters was necessary. This would indicate that further refinements to the manual segmentation protocol are necessary. Future work should seek to improve the methodology to reduce the variability and improve the reproducibility in these measures

Quantitation in MRI : application to ageing and epilepsy

Multi-atlas propagation and label fusion techniques have recently been developed for segmenting the human brain into multiple anatomical regions. In this thesis, I investigate possible adaptations of these current state-of-the-art methods. The aim is to study ageing on the one hand, and on the other hand temporal lobe epilepsy as an example for a neurological disease. Overall effects are a confounding factor in such anatomical analyses. Intracranial volume (ICV) is often preferred to normalize for global effects as it allows to normalize for estimated maximum brain size and is hence independent of global brain volume loss, as seen in ageing and disease. I describe systematic differences in ICV measures obtained at 1.5T versus 3T, and present an automated method of measuring intracranial volume, Reverse MNI Brain Masking (RBM), based on tissue probability maps in MNI standard space. I show that this is comparable to manual measurements and robust against field strength differences. Correct and robust segmentation of target brains which show gross abnormalities, such as ventriculomegaly, is important for the study of ageing and disease. We achieved this with incorporating tissue classification information into the image registration process. The best results in elderly subjects, patients with TLE and healthy controls were achieved using a new approach using multi-atlas propagation with enhanced registration (MAPER). I then applied MAPER to the problem of automatically distinguishing patients with TLE with (TLE-HA) and without (TLE-N) hippocampal atrophy on MRI from controls, and determine the side of seizure onset. MAPER-derived structural volumes were used for a classification step consisting of selecting a set of discriminatory structures and applying support vector machine on the structural volumes as well as morphological similarity information such as volume difference obtained with spectral analysis. Acccuracies were 91-100 %, indicating that the method might be clinically useful. Finally, I used the methods developed in the previous chapters to investigate brain regional volume changes across the human lifespan in over 500 healthy subjects between 20 to 90 years of age, using data from three different scanners (2x 1.5T, 1x 3T), using the IXI database. We were able to confirm several known changes, indicating the veracity of the method. In addition, we describe the first multi-region, whole-brain database of normal ageing

Comparison of manual and semi-automated delineation of regions of interest for radioligand PET imaging analysis

BACKGROUND As imaging centers produce higher resolution research scans, the number of man-hours required to process regional data has become a major concern. Comparison of automated vs. manual methodology has not been reported for functional imaging. We explored validation of using automation to delineate regions of interest on positron emission tomography (PET) scans. The purpose of this study was to ascertain improvements in image processing time and reproducibility of a semi-automated brain region extraction (SABRE) method over manual delineation of regions of interest (ROIs). METHODS We compared 2 sets of partial volume corrected serotonin 1a receptor binding potentials (BPs) resulting from manual vs. semi-automated methods. BPs were obtained from subjects meeting consensus criteria for frontotemporal degeneration and from age- and gender-matched healthy controls. Two trained raters provided each set of data to conduct comparisons of inter-rater mean image processing time, rank order of BPs for 9 PET scans, intra- and inter-rater intraclass correlation coefficients (ICC), repeatability coefficients (RC), percentages of the average parameter value (RM%), and effect sizes of either method. RESULTS SABRE saved approximately 3 hours of processing time per PET subject over manual delineation (p 0.8) for both methods. RC and RM% were lower for the manual method across all ROIs, indicating less intra-rater variance across PET subjects' BPs. CONCLUSION SABRE demonstrated significant time savings and no significant difference in reproducibility over manual methods, justifying the use of SABRE in serotonin 1a receptor radioligand PET imaging analysis. This implies that semi-automated ROI delineation is a valid methodology for future PET imaging analysis

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by &lt;0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as well

Comprehensive Brain MRI Segmentation in High Risk Preterm Newborns

Most extremely preterm newborns exhibit cerebral atrophy/growth disturbances and white matter signal abnormalities on MRI at term-equivalent age. MRI brain volumes could serve as biomarkers for evaluating the effects of neonatal intensive care and predicting neurodevelopmental outcomes. This requires detailed, accurate, and reliable brain MRI segmentation methods. We describe our efforts to develop such methods in high risk newborns using a combination of manual and automated segmentation tools. After intensive efforts to accurately define structural boundaries, two trained raters independently performed manual segmentation of nine subcortical structures using axial T2-weighted MRI scans from 20 randomly selected extremely preterm infants. All scans were re-segmented by both raters to assess reliability. High intra-rater reliability was achieved, as assessed by repeatability and intra-class correlation coefficients (ICC range: 0.97 to 0.99) for all manually segmented regions. Inter-rater reliability was slightly lower (ICC range: 0.93 to 0.99). A semi-automated segmentation approach was developed that combined the parametric strengths of the Hidden Markov Random Field Expectation Maximization algorithm with non-parametric Parzen window classifier resulting in accurate white matter, gray matter, and CSF segmentation. Final manual correction of misclassification errors improved accuracy (similarity index range: 0.87 to 0.89) and facilitated objective quantification of white matter signal abnormalities. The semi-automated and manual methods were seamlessly integrated to generate full brain segmentation within two hours. This comprehensive approach can facilitate the evaluation of large cohorts to rigorously evaluate the utility of regional brain volumes as biomarkers of neonatal care and surrogate endpoints for neurodevelopmental outcomes