For the left STN, multi-modal segmentation with T1 and FGATIR outperformed other approaches (*; p<0.05; Wilcoxon signed rank test). For the correct GPE segmentation with FGATIR, other approaches performed better (*; p<0.05; . Wilcoxon signed-rank test).
Parkinson’s disease
The thalamus, located bilaterally in the diencephalon, is a centralized relay point between the cortex and other parts of the brain [28, 29]. In PD, the cerebellum has been implicated in many primary phenotypes, but has not been studied to the same extent as the basal ganglia [47].
Segmentation Theory
These registered atlases are then merged to create a consensus view of the target image segmentation. If only one atlas is available, the labels transferred to the target image provide an estimate of the labels for the target [71].
Informatics
PHI is not required for research purposes, so it is important to exclude patient information from the research process. Image processing tasks often have a large number of parameters and are memory and processor intensive.
Dissertation Focus
In Chapter IV we present an algorithm for efficient segmentation of the hippocampus and amygdala with almost 200 atlases. We present an algorithm for efficient segmentation of the hippocampus and amygdala with almost 200 atlases.
Narrative
To characterize the structures of interest in PD, several specialized segmentation approaches were developed. In particular, segmentation approaches were developed for the hippocampus and amygdala, cerebellum, and subcortical brain structures.
Introduction
Dependency structure can also be evaluated in the perspective of simultaneous truth and level estimation (STAPLE) by joint modeling of protocol behavior. In STAPLE (B) only atlases with 1:1 correspondence can be used for segmentation, in MS-STAPLE (C) multi-protocol atlases can be used for joint segmentation of all protocols, and in SMS-STAPLE (D) multi-protocol atlases can be used be used to jointly segment one target protocol.
Theory
Briefly, let 𝑊 ∈ ℝ∏𝜌∈𝜓𝐿𝜌x𝑁 where 𝑊𝑙𝑖(𝑘)≡ 𝑓(𝑇𝑖 = 𝑙|𝐷, {𝜃}(𝑘)) is the probability that the real label set observed at voxel 𝑖 during iteration 𝑘 is 𝑙. Instead of {𝜃𝑗} (with ∑𝑗𝐿𝑑𝑗𝛱𝜌𝐿𝜌 degrees of freedom), consider 𝜃̃𝑗, a 𝐿𝑑𝑗× 𝐿𝑡 confusion matrix where 𝑡 is the index of the target label.
Methods and Results
Each of the fusion algorithms was applied to each unique set of simulated rater observations (Figure 2C). For each number of new fine atlases (Q), we randomly selected Q atlases from the 15 training sets to construct a simulated available atlas.
Discussion and Conclusion
In [107], the authors develop an alternative formulation of vote-based merging and STAPLE for multiple protocols by defining a match between the observed labels of individual protocols, an intermediate protocol-specific coarse label, and a set of fine labels that they wish to segment. Multi-atlas segmentation (MAS) [109] is commonly used to quantify T1-weighted MRI through voxel segmentation, which can be used to perform volumetric analysis [110], fMRI correlations [111] and tractography [ 112]. The relationship between intensity and T1-weighted imaging is determined by imaging sequence parameters, eg, for magnetization prepared rapid gradient echo (MPRAGE) - inversion time (TI), repetition time (TR), time of echo (TE), and rotation angle (𝛼).
For most major tissue types, there was a significant reduction in variance using the synthetic segmentation compared to the standard (*, p<0.05). Of the 21 subjects available, ten subjects whose white matter T2 relaxation matched the literature [124] were selected to be used as atlases.
Methods
Second, each scan was segmented using the synthetic multiatlas pipeline outlined earlier, using the ten synthetic atlases derived in §2.2. To assess the sensitivity of standard multi-atlas segmentation to inversion time, target images were created using each of the ten synthetic atlas images with inversion times ranging from . The standard multi-atlas segmentation pipeline [109] was performed on the 21 synthetic and one original image for each of the ten subjects.
We performed standard MAS and synthetic MAS techniques on each ABIDE subject, matching the synthetic sequence information to the site where the scan was performed. Surface distance is measured as the distance between the surface of the standard multi-atlas segmentation and the synthetic multi-atlas segmentation.
Results
This procedure was performed twice, once using the synthetic MAS segmentation volumetric results and once using the standard MAS segmentation volumetric results. Across five major tissue types of interest, cortical gray matter, cortical white matter, subcortical gray matter, cerebrospinal fluid, cerebellar gray matter, and cerebellar white matter, the average effect of sequence on volume was 9% using standard MAS. The standard multi-atlas segmentations performed on the synthetic target images and the acquired images with varying inversion times showed similar trends.
In the autistic versus healthy classification, there was an improvement in classification using the synthetic multi-atlas segmentation compared to the standard multi-atlas segmentation. Of the 17 ABIDE sites considered, 14 showed an increase in AUC using the synthetic multi-atlas segmentation.
Discussion and Conclusions
Automated, Open Source Segmentation of the Hippocampus and Amygdala with the Open Vanderbilt Archive of the Temporal Lobe. Several open-source, automated techniques have been developed for segmentation of the hippocampus and amygdala using standard, T1-weighted MR images. OVAL is a fully automated segmentation approach that uses 195 atlases to produce an accurate segmentation of the hippocampal head, body and tail and amygdala.
OVAL uses a prior multi-atlas segmentation of the whole brain, a common practice in most neuroimaging techniques, to localize the hippocampus and amygdala. OVAL produces more accurate amygdala and hippocampus segmentations than other common open source tools and.
Methods
For the purposes of this study, the term hippocampus includes the hippocampus proper (CA1-4 and dentate gyrus) and parts of the subiculum, collectively more commonly referred to as the hippocampal formation [154]. Human reproducibility outperforms all other techniques in MSD for left and right hippocampus for rater 1 (p<0.05,. OVAL, OVAL with 30 atlases and human reproducibility is statistically comparable for left and right hippocampus for rater 2 and surpasses all other techniques (p<0.05,
OVAL and FSL FIRST perform statistically similarly for the left amygdala and outperform all other segmentation approaches (p<0.05,. OVAL, OVAL with 30 atlases and FSL FIRST perform statistically similarly for the right amygdala and outperform all other segmentation approaches (p<0.05,
Results
Next, the 10 hippocampus test atlases were segmented with OVAL with 30 atlases and OVAL with 195 atlases to test OVAL's accuracy on the hippocampus head, body and tail. For MSD of the hippocampus test atlases, human reproducibility outperformed other techniques for the left and right hippocampus for reviewer 1, OVAL with 30 atlases and OVAL outperformed all other automated techniques (p<0.05 Wilcoxon signed rank test; figure 1). For DSC of the amygdala test atlases, OVAL outperformed FSL FIRST, FreeSurfer, BrainCOLOR and OVAL with 30 atlases for both left and right amygdala (p<0.05 Wilcoxon signed rank test).
For MSD of the amygdala test atlases, OVAL and FSL FIRST performed better than BrainCOLOR, FreeSurfer, and OVAL with 30 atlases for the left amygdala (p<0.05 Wilcoxon sign-rank test; Figure 1). For the right head and body, OVAL significantly outperformed human reproducibility and OVAL with 30 atlases in DSC (p<0.05 Wilcoxon sign-rank test).
Discussion
In the segmentation of Anura's data, Non-Local SIMPLE produced statistically significant improvements in average Dice compared to all other algorithms. In this work, we investigated new algorithms for fully automated multi-atlas segmentation of the cerebellum. One of the most popular and translatable techniques for automated segmentation is multi-atlas segmentation [89].
Due to the computational complexity of the registration [175] and fusion steps, determining the minimum number of atlases to accurately segment a target image is important for the development of multi-atlas algorithms. This can be seen as a special case of the above framework, where each approach considered involves a certain number of atlases. We first show that using standard Monte Carlo sampling of atlases from a population does not accurately capture the variance of the segmentation with respect to the number of atlases used (Figure 1B).
First, one subject was segmented using majority voting (MV) and variance DSC segmentation with respect to.
Discussion
The Monte Carlo approach identified 136 as the optimal number of atlases for the segmentation, while the proposed segmentation determined that more than 150 atlases were required for the segmentation approach. It is important to understand that reducing the variance is an important consideration along with increasing the accuracy. Finally, we extended the variance estimation framework to work on voxel-wise probabilistic segmentation results.
In this work we looked at the differences in two segmentations from a random draw of atlases from an infinite population. This assesses whether a voxel-based segmentation has converged to a stable result, rather than relying on summary statistics that can obscure underlying results.
Summary
Segmentation with Multiple Label Sets
Segmentation with Multiple Imaging Sequences
Segmentation of Specialized Anatomy
Estimation of Variance in Multi-Atlas Segmentation
We use this approach to first properly estimate the performance of a segmentation result using Dice Similarity Coefficient. Second, we use this approach to calculate the voxel-wise variance and determine the number of atlases required, minimizing the number of voxels that change between two atlas populations.
Summary of Contributions
We developed an algorithm to assess the importance of different imaging modalities in multi-atlas segmentation. Much of imaging research is becoming multimodal as structures and measurements of interest, and it is necessary to have techniques to understand their importance and necessity.
Impact on PD
We developed a segmentation approach to segment the cerebellum when the anatomy is represented in highly variable patterns. Obeso, J.A., Rodríguez-Oroz, M.C., Benitez-Temino, B., Blesa, F.J., Guridi, J., Marin, C., Rodriguez, M.: Functional organization of the basal ganglia: therapeutic implications for Parkinson's disease. Yager, L., Garcia, A., Wunsch, A., Ferguson, S.: Inputs and outputs of the striatum: Role in drug addiction.
Abitz, M., Nielsen, RD, Jones, EG, Laursen, H., Graem, N., Pakkenberg, B.: Excess neurons in the human newborn mediodorsal thalamus compared to that of the adult. Jones, E., Powell, T.: An analysis of the posterior group of thalamic nuclei based on the afferent connections.
