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Automatic segmentation and atlas normalization of brain tumor images are extremely challenging and clinically important tasks.


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Try out PMC Labs and tell us what you think. Learn More. Mixed-effects statistical models analyzed the effects of side of the brain, disease state, registration method, choice of atlas, and manual tracing protocol on the spatial overlap between automated segmentations and expert manual segmentations.

Registration methods that produced higher degrees of geometric deformation produced automated segmentations with higher agreement with manual segmentations. Side of the brain, presence of AD, choice of reference image, and manual tracing protocol were also ificant factors contributing to automated segmentation performance. Fully-automated techniques can be competitive with human raters on this difficult segmentation task, but a rigorous statistical analysis shows that a variety of methodological factors must be carefully considered to insure that automated methods perform well in practice.

The use of fully-deformable flirt methods, cohort atlases, and user-defined Atlas tracings are recommended for highest performance in fully-automated hippocampus segmentation. Hippocampal atrophy has been proposed as a clinical marker for early AD because it is known to occur early in the course of the disease on a spatial scale large enough to be detectable with structural MR images [ 2 ] [ 25 ].

Visual, qualitative atrophy assessment e.

However, flirt development of reliable, repeatable protocols for human raters to trace the hippocampus e. Furthermore, tracing protocols have enabled the study of hippocampal morphometrics in subjects with mild cognitive impairment MCIa high-AD-risk clinical condition marked by minor deficits in one or more cognitive domains [ 30 ][ 47 ]. However, large-scale studies of AD-related hippocampal atrophy are often impractical because manual segmentations are labor-intensive and require training to insure high repeatability between raters.

Typical hippocampi take between 30 minutes and 2 hours to trace by hand; the tedious labor quickly causes fatigue. Semi-automated segmentation methods reduce manual labor by having the user identify a sparse set of image landmarks that constrain a subsequent automated segmentation process [ 37 ] [ 15 ] [ 7 ]. However, we focus on fully-automated atlas-based techniques to eliminate the need for a user to manually process each image under study, and to eliminate the landmark-identification process as a source of variability between segmentations of the same image.

Atlas-based segmentation coregisters a subject image and a special reference image called the atlas image on which structures of interest have been manually traced See Figure 1. The resulting spatial transformation maps the coordinates of the structures from the coordinate space of the atlas image to that of the subject image. Since this approach is posed in terms of image-to-image registration, atlas-based techniques take advantage of methodological advances in registration that are driven by a wide range of application areas Atlas as visualization, image-guided surgery, and voxel-based morphometry.

Furthermore, atlas-based approaches are among the easiest to implement since they only require the user to align the atlas and subject images. Schematic view of atlas-based segmentation. An intensity transformation and geometric transformation are estimated to register the atlas image to the subject image; the geometric transformation is applied to the atlas mask in order to estimate the subject mask. The purpose of this study was to systematically compare the performance of several competing public-domain methodologies for atlas-based segmentation of AD-atrophied hippocampi.

We validated several widely-disseminated automated image registration methods [ 45 ] [ 17 ] [ 20 ]; in contrast, studies on atlas-based elderly hippocampus segmentation used a single, recently-developed, cutting-edge registration algorithm that lacked a widely-disseminated, standard software implementation e. Furthermore, we examined the use of two widely-disseminated atlas images [ 23 ] [ 40 ], as well as individual, manually-traced subject images as in, e.

Introduction

Finally, we examined the impact of varying manual tracing protocols on atlas-based segmentation performance. Along with the MR scan, subjects received a comprehensive battery of neuropsychological and clinical tests at time of enrollment and at yearly follow-up visits see [ 27 ] [ 28 ] for evaluation procedure. A consensus meeting of neuroradiologists, psychiatrists, neurologists, and psychologists diagnosed each subject into MCI [ 30 ], AD, or control.

Skulls were stripped from all images using the Brain Extraction Tool BET [ 38 ], and the images were cropped to remove all-zero slices using the crop tool provided with AIR 2. While several algorithmic details vary between these flirt techniques, they are chiefly distinguished from each other in terms of their geometric transformation model - that is, the mathematical equation that maps image coordinates between the atlas image and subject image. Flirt partiitoned the geometric transformation models into three in terms of the degree to which they allow the atlas image to spatially deform when it is aligned to the subject image.

Affine methods apply the same Atlas transformation to all voxels in the entire atlas image; semi-deformable mappings deform the atlas image in a spatially smooth, gradual way to align it to the subject image; and fully-deformable methods produce image-to-image mappings that are essentially unconstrained spatially see Figure 2 for an illustration and [ 3 ] for a detailed mathematical explanation. We considered one fully-deformable, three semi-deformable, and three affine registration methods. The AIR semi-deformable method uses the transformation output by the AIR affine method as a starting point for estimation of a spatially-smooth deformation based on a polynomial transformation model; the SPM semi-deformable method uses the transformation output by the SPM affine method as the starting point for estimation of a smooth deformation based on a discrete cosine transform DCT transformation model; the Chen semi-deformable method estimates a piecewise-linear transformation [ 5 ].

Finally, the Chen fully-deformable method takes the output of the Chen semi-deformable method as a starting point for estimation of an unconstrained, voxel-by-voxel deformation. Example image deformations produced by fully-deformable, semi-deformable, and affine registration techniques. The moving image is registered to the stationary image using each of the 7 algorithms we analyze. The colored dots show Atlas geometric positions of voxels in the shown slice of the moving image before and after deformation by each of the methods.

Atlas-based hippocampus segmentation in alzheimer’s disease and mild cognitive impairment

We evaluated automated segmentations by comparing them to manual segmentations performed by a single expert rater, R1, who was blind to diagnosis, gender, age, and other clinical data at the time of tracing. Hippocampi were traced on contiguous coronal slices following the guidelines of Watson et al. The traced structure included the hippocampus proper, the subiculum, and the dentate gyrus. The image and tracing were viewed in all three orthogonal viewing planes during manual segmentation. Rater R1 traced hippocampi on all 54 subject images; additionally, we selected 2 AD, 2 MCI, and 2 control images from the pool of 54 subjects for tracing by two additional trained raters, R2 and R3, using the same protocol.

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These additional manual segmentations were used to compare automated-manual segmentation agreement to inter-rater agreement. All manual segmentations were digitized into binary volumes for analysis. In the cohort atlas scenario, we selected an image from a subject population AD, MCI, or controlmanually traced left and right hippocampi on it, and treated it as a reference image that all other images in the subject population were registered to during atlas-based segmentation.

Cohort atlas images were selected at random from the subject population, however we note that a variety of more complex strategies for cohort atlas image selection are possible [ 34 ]. For each image in each subject population, we considered a hypothetical situation in which that image is selected as the cohort atlas; all other images in the population were registered to the cohort atlas image and hippocampus segmentation were evaluated.

In the standard atlas scenario, we began with an atlas image and manual hippocampus tracings, or manual atlas tracingsprovided by an atlas institution Harvard or MNI. We registered the atlas image to the subject image, use the resulting transformation to transfer a manual tracing of the hippocampus from the atlas image to the subject image.

This automated segmentation was evaluated by comparing it to an independent manual subject tracing - a manual tracing of the hippocampi on the subject image performed by rater R1. However, we recognized that the manual tracing protocol used by R1 may differ from that used by human tracers at MNI and Harvard, and that our evaluation risked confounding two factors that could have caused discrepancies between the automated segmentation and manual subject tracing: differences in hippocampus delineation between automated and manual techniques, and discrepancies in hippocampus boundary conventions between manual atlas and subject tracings.

For this reason, rater R1 generated manual atlas tracings by tracing flirt and right hippocampi on the Harvard and MNI atlas images using the same Atlas tracing protocol used for tracing on the subject images.

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Experiments analyzed the effects of choice of atlas MNI vs. Harvard and manual atlas tracings performed by R1 vs.

Cohort atlas images reflect possibly anomalous characteristics of a particular scan and subject, and their use is inherently more labor-intensive than standard atlases since they require the user to hand-label the structure of interest on the cohort atlas image. However, cohort-atlas-based segmentation has potential advantages over the more conventional standard-atlas-based approach. If the population of subject images is homogeneous with respect to factors such as sensor acquisition parameters, subject age, and subject disease state, then drawing a cohort atlas image from the population guarantees that these factors will not confound the registration process.

Furthermore, hand-labeling the structure of interest on the cohort atlas image insures the investigator that anatomical boundaries reflect his or her conventions.

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Performance of automated segmentation algorithms was measured in terms of manual-automated agreementthat is, agreement between automated segmentations and manual tracings performed by an expert rater. We compared manual-automated agreement to manual-manual agreementor the agreement between manual tracings performed by pairs of expert human raters. In so doing, we assessed whether switching from manual to automated segmentation ificantly increases the variability between the produced segmentation and one produced by an independent human rater.

Since R1 traced hippocampi on the full set of 54 subject images, we measured manual-automated agreement in terms of agreement between R1-rated manual tracings and the Chen fully-deformable automated technique.

Manual-manual agreement was measured in terms of pairwise agreement between manual tracings by R1 and R2, R1 and R3, and R2 and R3. Manual-automated agreement for each subject image was summarized in terms of the average manual-automated agreement between its R1 segmentation and the automated segmentations from all cohort atlas images in its disease category. Experiments analyzed differences between manual-manual agreement and manual-automated agreement on the 6 multiply-manually-traced hippocampi.

We note that other approaches, based on estimating automated segmentation performance and a single estimate of Atlas true, underlying structure mask, are also available [ 42 ]. We evaluated the agreement between an automated hippocampus segmentation estimate and a manual segmentation using a numerical criterion that measured the degree to which they overlap. The overlap ratio measures the degree of overlap between the automated and manual segmentations, specifically:.

Evaluating consistency between masks using overall and sectional overlap. A ground-truth subject mask and estimated subject mask are shown in flirt and dark gray.

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Figure 3d : Voxels in red overlap between the ground-truth and the estimate. Overlap ratio measures the ratio between the volume of the red region and the volume of the combined red and gray regions. Figure 3f : The green bars split the hippocampus voxels into axis-parallel sections.

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In sectional analysis, overlap ratio is computed for each section independently. We note that several authors have quantified manual-automated segmentation agreement using criteria similar to the overlap ratio [ 10 ] [ 22 ] [ 37 ] [ 24 ]. Overlap ratio was computed over the entire hippocampus. Furthermore, to characterize automated segmentations in terms of hippocampal sub-regions, we divided the hippocampus into sections and computed performance measures over the voxels in each section.

For each of the three cardinal directions, we partitioned the estimated and ground-truth hippocampi into k sections along that direction and computed overlap ratios in each of the sections.

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See Figure 3e for an illustration. Figure 4 suggests that since the hippocampi all have similar gross orientations in the image, the sections can be interpreted as corresponding to rough anatomical regions on the hippocampus.