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MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection
Open accessAbstract
Atlas-based automated anatomical labeling is a fundamental tool in medical image segmentation, as it defines regions of interest for subsequent analysis of structural and functional image data. The extensive investigation of multi-atlas warping and fusion techniques over the past 5 or more years has clearly demonstrated the advantages of consensus-based segmentation. However, the common approach is to use multiple atlases with a single registration method and parameter set, which is not necessarily optimal for every individual scan, anatomical region, and problem/data-type. Different registration criteria and parameter sets yield different solutions, each providing complementary information. Herein, we present a consensus labeling framework that generates a broad ensemble of labeled atlases in target image space via the use of several warping algorithms, regularization parameters, and atlases. The label fusion integrates two complementary sources of information: a local similarity ranking to select locally optimal atlases and a boundary modulation term to refine the segmentation consistently with the target image's intensity profile. The ensemble approach consistently outperforms segmentations using individual warping methods alone, achieving high accuracy on several benchmark datasets. The MUSE methodology has been used for processing thousands of scans from various datasets, producing robust and consistent results. MUSE is publicly available both as a downloadable software package, and as an application that can be run on the CBICA Image Processing Portal (https://ipp.cbica.upenn.edu), a web based platform for remote processing of medical images.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
MUSE is a multi-atlas segmentation framework that builds a broad ensemble of warped atlas labels using multiple atlases, registration algorithms, and regularization parameters, then fuses them with local similarity ranking and boundary modulation. It consistently outperformed single registration method/parameter ensembles and achieved the highest average Dice score in the MICCAI 2013 mid-brain segmentation challenge.
Key findings
- The complete ensemble of warps consistently outperformed ensembles from a single registration algorithm on each dataset, with significant differences (p < 0.01, paired t-test); adding local similarity ranking significantly increased performance over simple majority voting (p < 0.01 for all datasets).
- Full MUSE with boundary modulation improved segmentation accuracy for all datasets except NIREP; differences were significant except for OASIS (p = 0.06185). In the MICCAI 2013 challenge, MUSE obtained the highest average Dice score (d = 0.8686) in the mid-brain segmentation category.
- Segmentation accuracy consistently increased with more atlases until reaching a stable value around 7 atlases. On a pooled multi-site dataset of clinically normal subjects (n = 1029, age range 8 to 94), MUSE obtained the highest brain-age prediction accuracy among MUSE, STAPLE, and Joint Label Fusion in terms of Pearson’s r, concordance correlation coefficient, and mean absolute error.
- MUSE has been used for processing thousands of scans from various datasets, producing robust and consistent results, and is publicly available as a downloadable package and via the CBICA Image Processing Portal.
Methods
- MUSE constructs an ensemble of k warped atlas label maps by varying the atlas, deformable registration algorithm, and smoothness/regularization parameter. The paper used DRAMMS with g = {0.1, 0.2} and ANTS with s = {0.25, 0.5}; label fusion integrates a local similarity rank score based on a Gabor-filter attribute descriptor and boundary modulation terms b1 and b2.
- Internal validation used four public benchmark datasets with expert reference labels: BrainWeb, IBSR, NIREP, and OASIS. Leave-one-out cross-validation was applied independently per dataset, and Dice similarity coefficient was used for quantitative evaluation. The MICCAI 2013 challenge evaluation used the OASIS dataset for mid-brain structures.
- Multi-site experiments pooled clinically normal subjects from BLSA, ADNI-1, PNC, BBL-NC, and NiCK (n = 1029). For each subject, ROI segmentation was performed with MUSE, STAPLE, and Joint Label Fusion using 11 atlases selected from 35 OASIS atlases; ROI volumes were then used in a supervised learning framework to predict brain age with leave-one-out cross-validation, reporting Pearson’s r, concordance correlation coefficient, and mean absolute error.
Limitations
- The paper states that one limitation of MUSE is its high computational requirement, a general problem for multi-atlas segmentation methods; this is mitigated by parallelizable registrations, multi-scale registration implementations, and user control over the number of registrations.
- The paper states that for NIREP, the boundary modulation term did not improve quantitative results, which the authors attribute to low accuracy of the reference ROI masks, particularly under-segmented deep brain structures.
- Evident from the text: direct quantitative evaluation of multi-site segmentation accuracy was not possible because common reference labels for multiple datasets were unavailable, so multi-site robustness was assessed indirectly through brain-age prediction.
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13 Own result · 11 Own interpretation · 11 No basis given · 5 Own method · 4 No full text of the sources · 2 Sources not found · 1 Supported · 1 Partly supported · 1 Not checked yet · accuracy not yet measured
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The additional boundary modulation term results in higher segmentation accuracy for all datasets, except NIREP.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 3. Experiments > 3.3. Results on public datasets
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In the paper
“The addition of the similarity based weighting term significantly increases the performance compared to simple majority voting (p < 0.01 for all datasets).”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 3. Experiments > 3.3. Results on public datasets
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In the paper
“MUSE achieved consistently high Dice scores for the segmentation of important deep brain structures, such as hippocampus, thalamus and caudate”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 4. Discussion
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In the paper
“Differences between using the complete ensemble of warps versus any set of warps from a single registration algorithm are significant as well.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 3. Experiments > 3.3. Results on public datasets
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In the paper
“We observe that the segmentation accuracy consistently increases with more atlases until it reaches a stable value around 7 atlases (Figure 5).”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 3. Experiments > 3.3. Results on public datasets
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In the paper
“MUSE obtained the highest accuracy in terms of all three metrics.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 3. Experiments > 3.5. Application of MUSE on a multi-site dataset of healthy control subjects
plainn = 1,029- Asserted: passes — The authors put it forward as true.
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In the paper
“Importantly, MUSE achieved an accuracy comparable to a recent learning based approach (Wang et al., 2012), which incorporated massive training for each ROI using image patches and corresponding labels from the training set.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 4. Discussion
What the sources say
- Not matchedcites Wang et al., 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The differences were significant, except for OASIS (p = 0.06185).”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 3. Experiments > 3.3. Results on public datasets
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In the paper
“For one of the datasets, NIREP, the boundary modulation term did not improve the quantitative results.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 4. Discussion
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In the paper
“Specifically in the deep brain structures, the reference ROIs were under-segmented.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 4. Discussion
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- It may state two separate claims as one. 18
- Probably not a substantial claim. 13
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In the paper
“In comparative survey papers (Ou et al., 2014; Klein et al., 2009) and in many other studies, it was reported that more aggressive deformations may result in higher atlas-to-target regional overlaps, which is not ideal for registration but preferable for atlas-based segmentation.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 3. Experiments > 3.2. Choice of registration methods and parameters
What the sources say
- Not matchedcites Ou et al., 2014
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Partly supportedcites Klein et al., 2009 sourceExperimental — accuracy not yet measured
The Discussion (page unavailable) reports a modest correlation between a deformation’s degrees of freedom and registration accuracy, consistent with more flexible methods achieving better measured results. The Caveats section (page unavailable) warns that overlap measures can miss misregistration and folding. The paper does not establish that aggressive deformations are preferable for atlas-based segmentation but not ideal for registration. (LLM, conf=0.78)
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In the paper
“deformable image registration (Sotiras et al., 2013) is a core component of all MAS methods, and the quality of individual registrations has a very high impact on the accuracy of the final segmentation.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 1. Introduction
What the sources say
- Supportedcites Sotiras et al., 2013 source
The citation immediately follows “deformable image registration,” identifying the method rather than attributing the rest of the sentence to Sotiras et al. The survey’s Introduction describes deformable registration and its role in medical image analysis. The MAS-wide and segmentation-accuracy assertions are not part of that identification citation. (LLM, conf=0.88)
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- Supported
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Imaging features derived using MUSE segmentation could accurately predict the brain age”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 4. Discussion
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In our internal validation experiments we demonstrated the contribution of the similarity ranking and the boundary modulation terms to segmentation performance.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 4. Discussion
boosteddirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We observe that, for each dataset, the complete ensemble of warps consistently outperforms ensembles of warps from a single registration algorithm, with significant differences (p < 0.01 with a paired t-test).”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 3. Experiments > 3.3. Results on public datasets
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, the scores of the three methods with the highest ranks were similar and comparable.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 3. Experiments > 3.4. Segmentation of midbrain structures in the OASIS Dataset
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“MUSE obtained the highest average Dice score (d = 0.8686) in the mid-brain segmentation category, and it maintains the first rank as of 11/02/20153.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 1. Introduction
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The MUSE methodology has been used for processing thousands of scans from various datasets, producing robust and consistent results.”
- Also stated in: § 1. Introduction · boosted
“we applied MUSE on thousands of images from several large-scale neuroimaging studies, and showed the robustness of our method and consistency of segmentations for datasets with significant differences in scanner characteristics and sample demographics, by accurately estimating brain age from segmented ROIs.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > Abstract
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It may state two separate claims as one.
- Also stated in: § 1. Introduction · boosted
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Consistently with our cross-validated segmentation accuracy on the training dataset, MUSE performed with high accuracy on the testing dataset and obtained the highest average Dice score.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 3. Experiments > 3.4. Segmentation of midbrain structures in the OASIS Dataset
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The ensemble approach consistently outperforms segmentations using individual warping methods alone, achieving high accuracy on several benchmark datasets.”
- Also stated in: § 1. Introduction · boosted
“we confirmed that the ensemble approach consistently outperforms segmentations obtained using individual warping methods/parameters alone.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > Abstract
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It may state two separate claims as one.
- Also stated in: § 1. Introduction · boosted
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We trained an ensemble of regressors on the ROI volumes using the complete dataset (n = 1029) with leave-one-out cross-validation, and predicted the age of each subject using the trained models.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 3. Experiments > 3.5. Application of MUSE on a multi-site dataset of healthy control subjects
plainn = 1,029- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Anatomies closer to an atlas are well represented by a diffeomorphism.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 1. Introduction
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“As stated in Rohlfing and Maurer (2005), the ensemble approach efficiently solves this problem by covering a range of possible values without having to pick one, and in this way provides a robust segmentation tool.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 4. Discussion
What the sources say
- Not checked — no full text of the sourcecites Rohlfing and Maurer (2005) source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“the general practice is to select a single warping algorithm and to use it with a single set of registration parameters.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A single fold cross-validation was applied for each dataset to segment ROIs in each image. This was preferred in order not to over-tweak the parameters for a specific set of reference atlas dataset and thus to keep our method more generalizable to unseen datasets.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 3. Experiments
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Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All experiments were performed using leave-one-out cross validation, applying it independently for each dataset.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 3. Experiments > 3.3. Results on public datasets
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
In the paper
“the authors observed that in all cases classifier combinations consistently improved classification accuracy, and that improvements in accuracy were possible with various parameterizations of the non-rigid registration technique, even using a single atlas.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 1. Introduction
What the sources say
- Not checked — no full text of the sourcecites Rohlfing and Maurer (2005) source
plaindirectn = 7- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Each method and parameter set can be relatively less or more accurate in certain areas of the brain, thereby rendering an ensemble-based segmentation advantageous.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 4. Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
- Rests on an assumption: Each method and parameter set can be relatively less or more accurate in certain areas of the brain.
In the paper
“the authors investigated the roles of image registration and segmentation model complexity for mouse brain segmentation using 4 different registration algorithms, and concluded that image registration plays a more crucial role in segmentation compared to the complexity of the segmentation model.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 1. Introduction
What the sources say
- Not checked — no full text of the sourcecites Bai et al. (2012) source
boosteddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“anatomical correspondence may not be uniquely determined from intensity-based image attributes, which drive deformable registration algorithms.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 1. Introduction
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The main purpose of the intensity term is to make the final segmentation consistent with the intensity profile of the target image.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 1. Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
- The authors state this as an assumption: ROIs belong to a single tissue type with a smooth intensity variation.
- The authors state this as an assumption: The intensity distribution of each ROI can be modeled as a normal distribution.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We demonstrated that the ensemble of a multitude of warps, particularly using appropriate techniques for fusing them together, has significantly improved the segmentation accuracy, and provided robust segmentations.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 4. Discussion
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Different registration methods provide complementary information about the anatomy.
- Rests on an assumption: Each method and parameter set can be relatively less or more accurate in certain areas of the brain.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A learning based approach is expected to improve the segmentation accuracy within a single dataset, however it may also overfit to a specific set of reference labels and thus may have lower generalizability to new datasets, compared to a pure label fusion based approach without learning.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 4. Discussion
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While the use of multiple registration methods and atlases would linearly increase the computation time, this is not a hard constraint for the practical application of MUSE, because the registrations are very efficiently parallelizable, and notably, multi-scale implementation of registration algorithms can effectively generate warps of various smoothness levels for no additional cost.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 4. Discussion
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our results showed that this approach outperforms methods that are based on single parameter sets and registration algorithms, and can therefore provide a foundation for robust segmentation.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 4. Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Different registration methods provide complementary information about the anatomy.
- Rests on an assumption: Each method and parameter set can be relatively less or more accurate in certain areas of the brain.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“One of the limitations of MUSE is the high computational requirement, which is a general problem for MAS methods.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 4. Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The authors state this as an assumption.
ROIs belong to a single tissue type with a smooth intensity variation.
“with the assumption that the ROIs belong to a single tissue type with a smooth intensity variation.”
The authors state this as an assumption.
The intensity distribution of each ROI can be modeled as a normal distribution.
“The intensity distribution of each ROI is modeled as a normal distribution”
The paper relies on this without support.
Different registration methods provide complementary information about the anatomy.
“Different registration methods, which generally use notably different image features, energy formulations and optimization algorithms, provide complementary information about the anatomy.”
The paper relies on this without support.
Each method and parameter set can be relatively less or more accurate in certain areas of the brain.
“Each method and parameter set can be relatively less or more accurate in certain areas of the brain”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Data from these projects have been extensively used in the development of neuroimaging tools, such as brain atlases 3,4 , tissue segmentation algorithms 5 , and de-identification tools 6 , used to create models of healthy aging 7,8 and AD timecourse 7-11 , used to answer hypothesis driven questions 12,13 , and used for educational and training purposes.”
From OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease (2019) · cites this paper as [5]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 3. Experiments
“A single fold cross-validation was applied for each dataset to segment ROIs in each image.”
§ MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection > 3. Experiments > 3.3. Results on public datasets
“All experiments were performed using leave-one-out cross validation, applying it independently for each dataset.”
Citations
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InfluentialNonrigid registration using free-form deformations: application to breast mr images
Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.796284 · Cited by 5,265
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InfluentialPrediction of individual brain maturity using fMRI
Dosenbach, N. U. F., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., Nelson, S. M., Wig, G. S., Vogel, A. C., Lessov-Schlaggar, C. N., et al. (2010) · Science · doi:10.1126/science.1194144 · Cited by 2,183 · cited 2×
“In recent years the concept of using machine learning to determine brain age has gained popularity as a means for defining normative trajectories of brain development and aging (Dosenbach et al., 2010; Franke et al., 2012; Erus et al., 2014).”
Warfield, S. K., Zou, K. H., & Wells, W. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.828354 · Cited by 2,072 · cited 2×
“For each subject of the pooled multi-site dataset ROI segmentation was performed by independently applying three different methods, MUSE, STAPLE (Warfield et al., 2004) and Joint Label Fusion (Wang et al.”
Deformable medical image registration: a survey
Sotiras, A., Davatzikos, C., & Paragios, N. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2265603 · Cited by 1,587 · cited 2×
“, 2013), deformable image registration (Sotiras et al., 2013) is a core component of allMASmethods, and the quality of individual registrations has a very high impact on the accuracy of the final segmentation.”
Self-citationLongitudinal Magnetic Resonance Imaging Studies of Older Adults: A Shrinking Brain
Resnick, S. M., Pham, D. L., Kraut, M. A., Zonderman, A. B., & Davatzikos, C. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-08-03295.2003 · Cited by 1,425 · cited 2×
“In order to evaluate the multi-site segmentation performance of our method we created a large multi-study dataset of clinically normal subjects by pooling data from studies including BLSA (Resnick et al., 2000, 2003), ADNI-1 (Jack et al., 2008), PNC (Satterthwaite et al., 2014), BBL-NC 7 , and NiCK (Hartung et al., 20…”
Self-citation
Cited by
Sorted by citations
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049
Self-citationLaMontagne, P. J., Benzinger, T. L., Morris, J. C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E., Hassenstab, J., Moulder, K., Vlassenko, A. G., et al. (2019) · medRxiv · doi:10.1101/2019.12.13.19014902 · Cited by 779
“…from these projects have been extensively used in the development of neuroimaging tools, such as brain atlases 3,4 , tissue segmentation algorithms 5 , and de-identification tools 6 , used to create models of healthy aging 7,8 and AD timecourse 7–11 , used to answer hypothesis driven questions…”
Pomponio, R., Erus, G., Habes, M., Doshi, J. J., Srinivasan, D., Mamourian, E., Bashyam, V., Nasrallah, I. M., Satterthwaite, T. D., Yong Fan, A., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116450 · Cited by 561
Self-citationAssociation of Intensive vs Standard Blood Pressure Control With Cerebral White Matter Lesions
Nasrallah, I. M., Pajewski, N. M., Auchus, A. P., Chelune, G. J., Cheung, A. K., Cleveland, M. L., Coker, L. H., Crowe, M. G., Cushman, W. C., Cutler, J. A., et al. (2019) · JAMA · doi:10.1001/jama.2019.10551 · Cited by 433
Self-citationOrr, M., Gonzales, M., Garbarino, V., Kautz, T., Palavicini, J., Lopez-Cruzan, M., Dehkordi, S. K., Mathews, J., Zare, H., Xu, P., et al. (2023) · doi:10.21203/rs.3.rs-2809973/v1 · Cited by 319
Senolytic therapy in mild Alzheimer's disease: a phase 1 feasibility trial
Gonzales, M. M., Garbarino, V. R., Kautz, T. F., Palavicini, J. P., Lopez-Cruzan, M., Dehkordi, S. K., Mathews, J. J., Zare, H., Xu, P., Zhang, B., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02543-w · Cited by 334
Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning
Chand, G. B., Dwyer, D., Erus, G., Sotiras, A., Varol, E., Srinivasan, D., Doshi, J. J., Pomponio, R., Pigoni, A., Dazzan, P., et al. (2020) · Brain · doi:10.1093/brain/awaa025 · Cited by 279
“Individuals’ T 1 images were checked for quality (Supplementary material) and a state-of-the-art multi-atlas segmentation (Doshi et al. , 2016) was used to obtain anatomical regions of interest consisting of grey matter, white matter and CSF. Regional volumetric maps (Davatzikos et al. , 2001),…”
MethodSelf-citationMachine learning in neuroimaging: Progress and challenges
Davatzikos, C. A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.003 · Cited by 221
“(ROI volumes were obtained via an optimized multi-atlas, multi-warping consensus method[1], which”
MethodSelf-citationVarol, E., Sotiras, A., & Davatzikos, C. A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.041 · Cited by 217
Self-citationDong, A., Toledo, J. B., Honnorat, N., Doshi, J. J., Varol, E., Sotiras, A., Wolk, D. A., Trojanowski, J. Q., Davatzikos, C. A., & Initiative, A. D. N. (2016) · Brain · doi:10.1093/brain/aww319 · Cited by 183
Self-citationHabes, M., Pomponio, R., Shou, H., Doshi, J., Mamourian, E., Erus, G., Nasrallah, I., Launer, L. J., Rashid, T., Bilgel, M., et al. (2021) · Alzheimer's & Dementia · doi:10.1002/alz.12178 · Cited by 173
Self-citationBinder, Z. A., Thorne, A. H., Bakas, S., Wileyto, E. P., Bilello, M., Akbari, H., Rathore, S., Ha, S. M., Zhang, L., Ferguson, C., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.06.006 · Cited by 166
Self-citationHabes, M., Janowitz, D., Erus, G., Toledo, J. B., Resnick, S. M., Doshi, J. J., der Auwera, S. V., Wittfeld, K., Hegenscheid, K., Hosten, N., et al. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.39 · Cited by 144
Self-citationWen, J., Fu, C. H. Y., Tosun, D., Veturi, Y., Yang, Z., Abdulkadir, A., Mamourian, E., Srinivasan, D., Skampardoni, I., Singh, A., et al. (2022) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.0020 · Cited by 142
Self-citationRozycki, M., Satterthwaite, T. D., Koutsouleris, N., Erus, G., Doshi, J. J., Wolf, D. H., Yong Fan, A., Gur, R. E., Gur, R. C., Meisenzahl, E. M., et al. (2017) · Schizophrenia Bulletin · doi:10.1093/schbul/sbx137 · Cited by 138
“Schizophrenia is an often devastating illness that results in substantial morbidity and mortality worldwide.(1) Structural brain abnormalities were noted by early anatomists,2,3 and results from in vivo magnetic resonance imaging (MRI) have supported the notion that schizophrenia is a brain disorder.”
BackgroundSelf-citationKuo, P.-L., Schrack, J. A., Shardell, M. D., Levine, M. E., Moore, A. Z., An, Y., Elango, P., Karikkineth, A. C., Tanaka, T., de Cabo, R., et al. (2020) · Journal of Internal Medicine · doi:10.1111/joim.13024 · Cited by 140
Self-citationStructural Brain Abnormalities in Youth With Psychosis Spectrum Symptoms
Satterthwaite, T. D., Wolf, D. H., Calkins, M. E., Vandekar, S. N., Erus, G., Ruparel, K., Roalf, D. R., Linn, K. A., Elliott, M. A., Moore, T. M., et al. (2016) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.3463 · Cited by 132
Self-citationAssociation of Midlife Hearing Impairment With Late-Life Temporal Lobe Volume Loss
Armstrong, N. M., An, Y., Doshi, J. J., Erus, G., Ferrucci, L., Davatzikos, C. A., Deal, J. A., Lin, F. R., & Resnick, S. M. (2019) · JAMA Otolaryngology–Head & Neck Surgery · doi:10.1001/jamaoto.2019.1610 · Cited by 135
Self-citationShinohara, R. T., Oh, J., Nair, G., Calabresi, P. A., Davatzikos, C. A., Doshi, J. J., Henry, R. G., Kim, G., Linn, K. A., Papinutto, N., et al. (2017) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5254 · Cited by 116
Self-citationArmstrong, N. M., An, Y., Beason‐Held, L. L., Doshi, J. J., Erus, G., Ferrucci, L., Davatzikos, C. A., & Resnick, S. M. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.05.020 · Cited by 125
Self-citationYang, Z., Nasrallah, I. M., Shou, H., Wen, J., Doshi, J. J., Habes, M., Erus, G., Abdulkadir, A., Resnick, S. M., Albert, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26703-z · Cited by 112
“For the ADNI study, 145 anatomical regions of interest (ROIs) were identi fi ed in gray matter (GM, 119 ROIs), white matter (WM, 20 ROIs) and ventricles (6 ROIs) using a multi ‐ atlas label fusion method 35 .”
MethodSelf-citationAssociations between cognitive and brain volume changes in cognitively normal older adults
Armstrong, N. M., An, Y., Shin, J. J., Williams, O. A., Doshi, J. J., Erus, G., Davatzikos, C. A., Ferrucci, L., Beason‐Held, L. L., & Resnick, S. M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117289 · Cited by 106
Self-citationEffects of amyloid pathology and neurodegeneration on cognitive change in cognitively normal adults
Bilgel, M., An, Y., Helphrey, J. H., Elkins, W., Gómez, G., Wong, D. F., Davatzikos, C. A., Ferrucci, L., & Resnick, S. M. (2018) · Brain · doi:10.1093/brain/awy150 · Cited by 95
Self-citationBrain aging patterns in a large and diverse cohort of 49,482 individuals
Yang, Z., Wen, J., Erus, G., Govindarajan, S. T., Melhem, R., Mamourian, E., Cui, Y., Srinivasan, D., Abdulkadir, A., Parmpi, P., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03144-x · Cited by 102
“To optimize this parametrized latent distribution to approximate the ground-truth correlations, we introduced modifications to the original GAN loss function, The updated loss function enables us to sample z from a multivariate uniform distribution, p U ( z )=U[0,1] M , but penalizes the losses…”
Self-citationHe, S., Pereira, D., Perez, J. D., Gollub, R. L., Murphy, S. N., Prabhu, S. P., Pienaar, R., Robertson, R. L., Grant, P. E., & Ou, Y. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102091 · Cited by 85
Self-citationAssociation of Intensive vs Standard Blood Pressure Control With Cerebral Blood Flow
Dolui, S., Detre, J. A., Gaussoin, S. A., Herrick, J. S., Wang, D. J. J., Tamura, M. K., Cho, M. E., Haley, W. E., Launer, L. J., Punzi, H. A., et al. (2022) · JAMA Neurology · doi:10.1001/jamaneurol.2022.0074 · Cited by 76
Moving beyond processing- and analysis-related variation in resting-state functional brain imaging
Li, X., Esper, N. B., Ai, L., Giavasis, S., Jin, H., Feczko, E., Xu, T., Clucas, J. C., Franco, A. R., Heinsfeld, A. S., et al. (2024) · Nature Human Behaviour · doi:10.1101/2021.12.01.470790 · Cited by 73
Self-citationNasrallah, I. M., Gaussoin, S. A., Pomponio, R., Dolui, S., Erus, G., Wright, C. B., Launer, L. J., Detre, J. A., Wolk, D. A., Davatzikos, C. A., et al. (2021) · JAMA Neurology · doi:10.1001/jamaneurol.2021.0178 · Cited by 72
Self-citationThe genetic architecture of multimodal human brain age
Wen, J., Zhao, B., Yang, Z., Erus, G., Skampardoni, I., Mamourian, E., Cui, Y., Hwang, G., Bao, J., Boquet-Pujadas, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46796-6 · Cited by 65
Self-citationMulti-scale semi-supervised clustering of brain images: Deriving disease subtypes
Wen, J., Varol, E., Sotiras, A., Yang, Z., Chand, G. B., Erus, G., Shou, H., Abdulkadir, A., Hwang, G., Dwyer, D., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102304 · Cited by 68
Self-citationAustin, T. R., Nasrallah, I. M., Erus, G., et al. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.121.023159 · Cited by 65
“20 Anatomical regions of interest were identified using a multiatlas label fusion method 21 and were used to segment GM and WM tissues, with the sum of GM and WM defining TBV. Total intracranial volume was defined as the sum of all GM, WM, and cerebrospinal fluid.”
Self-citationWang, D., Honnorat, N., Fox, P. T., Ritter, K., Eickhoff, S. B., Seshadri, S. S., & Habes, M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119929 · Cited by 70
“These registrations are used to warp the atlas brain masks into the space of the T1 scan to process, where they are combined by majority voting to produce an accurate brain mask ( Doshi et al., 2016 ).”
InfluentialTau pathology in cognitively normal older adults
Ziontz, J., Bilgel, M., Shafer, A. T., Moghekar, A., Elkins, W., Helphrey, J. H., Gómez, G., June, D., McDonald, M. A., Dannals, R. F., et al. (2019) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2019.07.007 · Cited by 67
“Anatomical labels and global and regional brain volumes were obtained using Multi-atlas region Segmentation using Ensembles of registration algorithms and parameters (MUSE) [24].”
MethodSelf-citationEmbracing the disharmony in medical imaging: A Simple and effective framework for domain adaptation
Wang, R., Chaudhari, P., & Davatzikos, C. A. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102309 · Cited by 69
Self-citationNeuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods
Honnorat, N., Dong, A., Meisenzahl‐Lechner, E., Koutsouleris, N., & Davatzikos, C. A. (2017) · Schizophrenia Research · doi:10.1016/j.schres.2017.12.008 · Cited by 61
Self-citationModifiable vascular risk factors, white matter disease and cognition in early Parkinson's disease
Chahine, L. M., Santos, C. D., Fullard, M. E., Scordia, C., Weintraub, D. A., Erus, G., Rosenthal, L. S., Davatzikos, C. A., & McMillan, C. T. (2018) · European Journal of Neurology · doi:10.1111/ene.13797 · Cited by 60
“A registration-based multiatlas segmentation method [23] was applied to parcellate the brain into anatomical regions of interest.”
MethodSelf-citationErus, G., Doshi, J. J., An, Y., Verganelakis, D. A., Resnick, S. M., & Davatzikos, C. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.026 · Cited by 57
“The proposed method is founded on a relatively recent multi-atlas segmentation method that utilizes a rich ensemble of warped atlases (Doshi et al., 2016).”
InfluentialSelf-citationPetkus, A. J., Resnick, S. M., Wang, X., Beavers, D. P., Espeland, M. A., Gatz, M., Gruenewald, T. L., Millstein, J., Chui, H. C., Kaufman, J. D., et al. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.153642 · Cited by 56
Self-citationShao, M., Han, S., Carass, A., Li, X., Blitz, A. M., Shin, J., Prince, J. L., & Ellingsen, L. M. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101871 · Cited by 56
Plasma proteomic signatures predict dementia and cognitive impairment
Tanaka, T., Lavery, R. F., Varma, V. R., Fantoni, G., Colpo, M., Thambisetty, M., Candia, J., Resnick, S. M., Bennett, D. A., Biancotto, A., et al. (2020) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12018 · Cited by 55
Self-citationYang, Z., Wen, J., Abdulkadir, A., Cui, Y., Erus, G., Mamourian, E., Melhem, R., Srinivasan, D., Govindarajan, S. T., Chen, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44271-2 · Cited by 55
“Subsequently, 144 anatomical ROIs wereidenti fi ed in graymatter (GM, 119 ROIs), white matter (WM, 19 ROIs), and ventricles (6 ROIs) using a multi ‐ atlas label fusion method (MUSE 3.0.5: https://github.com/ CBICA/MUSE/releases/tag/3.0.5) 75 .”
InfluentialSelf-citationBakas, S., Shukla, G., Akbari, H., Erus, G., Sotiras, A., Rathore, S., Sako, C., Ha, S. M., Rozycki, M., Shinohara, R. T., et al. (2020) · Journal of medical imaging · doi:10.1117/1.jmi.7.3.031505 · Cited by 52
Self-citationArmstrong, N. M., An, Y., Beason‐Held, L. L., Doshi, J. J., Erus, G., Ferrucci, L., Davatzikos, C. A., & Resnick, S. M. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.10.024 · Cited by 52
“A uthor M anuscript A uthor M anuscript A uthor M anuscript A uthor M anuscript labeling approach (Doshi et al., 2016) with harmonized acquisition-specific atlases (Erus et al., 2018).”
MethodSelf-citationOlfaction, Cognitive Impairment, and PET Biomarkers in Community-Dwelling Older Adults
Tian, Q., Bilgel, M., Moghekar, A., et al. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-210636 · Cited by 50
Self-citationTruelove‐Hill, M., Erus, G., Bashyam, V., Varol, E., Sako, C., Gur, R. C., Gur, R. E., Koutsouleris, N., Zhuo, C., Yong Fan, A., et al. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2092-19.2019 · Cited by 50
“Anatomical regions-of-interest (ROIs) were identified using a multiatlas label fusion method (Doshi et al., 2016).”
MethodSelf-citationChand, G. B., Singhal, P., Dwyer, D., Wen, J., Erus, G., Doshi, J. J., Srinivasan, D., Mamourian, E., Varol, E., Sotiras, A., et al. (2022) · American Journal of Psychiatry · doi:10.1176/appi.ajp.21070686 · Cited by 51
Self-citationSrinivasan, D., Erus, G., Doshi, J., Wolk, D. A., Shou, H., Habes, M., & Davatzikos, C. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117248 · Cited by 45
Self-citationCognitive and neuroimaging profiles of older adults with dual decline in memory and gait speed
Tian, Q., Studenski, S. A., Montero‐Odasso, M., Davatzikos, C. A., Resnick, S. M., & Ferrucci, L. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.10.002 · Cited by 45
Self-citationRashid, T., Abdulkadir, A., Nasrallah, I. M., Ware, J. B., Liu, H., Spincemaille, P., Romero, J. R., Bryan, R. N., Heckbert, S. R., & Habes, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-93427-x · Cited by 46
Habes, M., Toledo, J. B., Resnick, S. M., Doshi, J., Van der Auwera, S., Erus, G., Janowitz, D., Hegenscheid, K., Homuth, G., Völzke, H., et al. (2016) · American Journal of Neuroradiology · doi:10.3174/ajnr.a4805 · Cited by 44
Self-citationDuggan, M. R., Peng, Z., Sipilä, P. N., Lindbohm, J. V., Chen, J., Lü, Y., Davatzikos, C. A., Erus, G., Hohman, T. J., Andrews, S. J., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00682-4 · Cited by 47
“We applied a validated, Multi-atlas Region Segmentation Utilizing Ensembles (MUSE) anatomical labeling method specifically designed to achieve a consistent parcellation of brain anatomy in longitudinal MRI studies using T1-weighted sequences 57 .”
Self-citationBilgel, M., Beason‐Held, L. L., An, Y., Zhou, Y., Wong, D. F., & Resnick, S. M. (2019) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x19830537 · Cited by 42
Self-citationWadley, V. G., Bull, T. P., Zhang, Y., Barba, C., Bryan, R. N., Crowe, M. G., Desiderio, L. M., DEUTSCH, G., Erus, G., Geldmacher, D., et al. (2020) · The Journals of Gerontology Series A · doi:10.1093/gerona/glaa312 · Cited by 43
Self-citationCausal links among amyloid, tau, and neurodegeneration
Bilgel, M., Wong, D. F., Moghekar, A., Ferrucci, L., Resnick, S. M., & Initiative, T. A. D. N. (2022) · Brain Communications · doi:10.1093/braincomms/fcac193 · Cited by 38
“We computed anatomical labels and regional brain volumes using Multi-atlas region Segmentation using Ensembles of registration algorithms and parameters, 28 and intracranial volume (ICV) was determined from intracranial masks computed using a deep learning approach.”
Self-citationTian, Q., Bilgel, M., Walker, K. A., Moghekar, A., Fishbein, K. W., Spencer, R. G. S., Resnick, S. M., & Ferrucci, L. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13388 · Cited by 43
Self-citationTian, Q., An, Y., Kitner‐Triolo, M. H., et al. (2022) · Neurology · doi:10.1212/wnl.0000000000201646 · Cited by 38
“This ensemble approach has consistently outperformed segmentations using individual warping methods alone and has achieved high accuracy in several benchmark datasets.(28) The MUSE methodology has been used for processing thousands of scans from various datasets, producing robust and consistent results.”
MethodSelf-citationFu, C. H. Y., Antoniades, M., Erus, G., García, J., Yong Fan, A., Arnone, D., Arnott, S. R., Chen, T., Choi, K. S., Fatt, C. C., et al. (2024) · Nature Mental Health · doi:10.1038/s44220-023-00187-w · Cited by 38
Self-citationBrain Magnetic Resonance Imaging Findings in Children and Young Adults With CKD
Hartung, E. A., Erus, G., Jawad, A. F., Laney, N. C., Doshi, J. J., Hooper, S. R., Radcliffe, J., Davatzikos, C. A., & Furth, S. L. (2018) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2017.11.024 · Cited by 39
Self-citationVlontzou, M. E., Αθανασίου, Μ., Dalakleidi, K. V., Skampardoni, I., Davatzikos, C. A., & Nikita, K. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-92577-6 · Cited by 43
Self-citationWeiss, R. J., Bates, S. V., Song, Y., Zhang, Y., Herzberg, E. M., Chen, Y., Gong, M., Chien, I., Zhang, L., Murphy, S. N., et al. (2019) · Journal of Translational Medicine · doi:10.1186/s12967-019-2119-5 · Cited by 37
Self-citationShafer, A. T., Williams, O. A., Perez, E., An, Y., Landman, B. A., Ferrucci, L., & Resnick, S. M. (2022) · Brain Communications · doi:10.1093/braincomms/fcac051 · Cited by 36
Self-citationOu, Y., Zöllei, L., Retzepi, K., Castro, V. M., Bates, S. V., Pieper, S., Andriole, K. P., Murphy, S. N., Gollub, R. L., & Grant, P. E. (2017) · Human Brain Mapping · doi:10.1002/hbm.23573 · Cited by 36
“Future efforts will also include automatic segmentation of ADC maps into regions of interest [Doshi et al., 2016; Iglesias and Sabuncu, 2015], instead of using the manual annotations used in this article.”
MethodSelf-citationDwyer, D., Chand, G. B., Pigoni, A., Khuntia, A. T., Wen, J., Antoniades, M., Hwang, G., Erus, G., Doshi, J. J., Srinivasan, D., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02069-0 · Cited by 33
Self-citationGenomic loci influence patterns of structural covariance in the human brain
Wen, J., Nasrallah, I. M., Abdulkadir, A., Satterthwaite, T. D., Yang, Z., Erus, G., Robert‐Fitzgerald, T., Singh, A., Sotiras, A., Boquet-Pujadas, A., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2300842120 · Cited by 33
Self-citationMetric Learning for Multi-atlas based Segmentation of Hippocampus
Initiative, A. D. N., Zhu, H., Cheng, H., Yang, X., & Yong Fan, A. (2016) · Neuroinformatics · doi:10.1007/s12021-016-9312-y · Cited by 36
Deep learning based detection of enlarged perivascular spaces on brain MRI
Rashid, T., Liu, H., Ware, J. B., Li, K., Romero, J. R., Fadaee, E., Nasrallah, I. M., Hilal, S., Bryan, R. N., Hughes, T. M., et al. (2023) · Neuroimage Reports · doi:10.1016/j.ynirp.2023.100162 · Cited by 35
Self-citationShaked, D., Katzel, L. I., Seliger, S. L., Gullapalli, R. P., Davatzikos, C. A., Erus, G., Evans, M. K., Zonderman, A. B., & Waldstein, S. R. (2018) · Neuropsychology · doi:10.1037/neu0000484 · Cited by 32
“Preprocessed images were segmented into a set of anatomical regions of interest (ROIs) using a new multiatlas label fusion method, which has achieved state-of-the-art accuracy in segmentation of deep brain structures in an independent comparative evaluation (Doshi et al., 2016).”
MethodSelf-citationA prospective study of focal brain atrophy, mobility and fitness
Tian, Q., Resnick, S. M., Davatzikos, C. A., Erus, G., Simonsick, E. M., Studenski, S. A., & Ferrucci, L. (2019) · Journal of Internal Medicine · doi:10.1111/joim.12894 · Cited by 33
Self-citationConnectomic consistency: a systematic stability analysis of structural and functional connectivity
Osmanlıoğlu, Y., Alappatt, J. A., Parker, D., & Verma, R. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab947b · Cited by 33
Charissé, D., Erus, G., Pomponio, R., Gorges, M., Schmidt, N., Schneider, C. B., Liepelt‐Scarfone, I., Riedel, O., Reetz, K., Schulz, J. B., et al. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.08.020 · Cited by 31
Self-citationJacobson, A. M., Braffett, B. H., Erus, G., Ryan, C. M., Biessels, G. J., Luchsinger, J. A., Bebu, I., Gubitosi‐Klug, R., Desiderio, L. M., Lorenzi, G. M., et al. (2022) · Diabetes Care · doi:10.2337/dc21-2438 · Cited by 33
Self-citationHabes, M., Jacobson, A. M., Braffett, B. H., Rashid, T., Ryan, C. M., Shou, H., Cui, Y., Davatzikos, C. A., Luchsinger, J. A., Biessels, G. J., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.16182 · Cited by 34
Self-citationField of View Normalization in Multi-Site Brain MRI
Ou, Y., Zöllei, L., Da, X., Retzepi, K., Murphy, S. N., Gerstner, E. R., Rosen, B. R., Grant, P. E., Kalpathy–Cramer, J., & Gollub, R. L. (2018) · Neuroinformatics · doi:10.1007/s12021-018-9359-z · Cited by 29
Self-citationArmstrong, N. M., Huang, C.-W., Williams, O. A., Bilgel, M., An, Y., Doshi, J. J., Erus, G., Davatzikos, C. A., Wong, D. F., Ferrucci, L., et al. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101769 · Cited by 31
“This method combines the MUSE anatomical labeling approach (Doshi et al., 2016) with harmonized acquisition-specific atlases (Erus et al.”
MethodSelf-citationFAM222A encodes a protein which accumulates in plaques in Alzheimer’s disease
Yan, T., Liang, J., Gao, J., Wang, L., Fujioka, H., Initiative, T. A. D. N., Weiner, M. W., Schuff, N. W., Rosen, H. J., Miller, B. L., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-13962-0 · Cited by 32
Cigarette smoking and cerebral blood flow in a cohort of middle-aged adults
Elbejjani, M., Auer, R., Dolui, S., Jacobs, D. R., Haight, T. J., Goff, D. C., Detre, J. A., Davatzikos, C. A., Bryan, R. N., & Launer, L. J. (2018) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x18754973 · Cited by 31
Self-citationBruckert, L., Shpanskaya, K. S., McKenna, E. S., Borchers, L. R., Yablonski, M., Blecher, T., Ben‐Shachar, M., Travis, K. E., Feldman, H. M., & Yeom, K. W. (2019) · The Cerebellum · doi:10.1007/s12311-018-1003-9 · Cited by 27
The joint effect of aging and HIV infection on microstructure of white matter bundles
Kuhn, T. P., Jin, Y., Huang, C., Kim, Y., Nir, T. M., Gullett, J. M., Jones, J. D., Sayegh, P., Chung, C. D., Dang, B. H., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24708 · Cited by 29
“…18 WM tracts of interest (TOIs) based on the Johns Hopkins University White Matter Atlas (Mori et al., 2008). autoMATE involves a harmonized, multi-atlas anatomical labelling approach that has been shown to outperform alternative single-atlas approaches (Doshi et al., 2016; Erus et al., 2018).”
MethodJune, D., Williams, O. A., Huang, C.-W., An, Y., Landman, B. A., Davatzikos, C. A., Bilgel, M., Resnick, S. M., & Beason‐Held, L. L. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117182 · Cited by 35
“This method combines the USE anatomical labeling approach ( Doshi et al., 2016 ) with harmoized acquisition-specific atlases which leverage the availability of brain cans from the same individuals with both 1.5T and 3T images to har- onize the labeling process ( Erus et al., 2018 ).”
MethodSelf-citationWanigatunga, A. A., Wang, H., An, Y., Simonsick, E. M., Tian, Q., Davatzikos, C. A., Urbanek, J. K., Zipunnikov, V., Spira, A. P., Ferrucci, L., et al. (2020) · The Journals of Gerontology Series A · doi:10.1093/gerona/glaa294 · Cited by 30
Self-citationChaim-Avancini, T. M., Doshi, J. J., Zanetti, M. V., Erus, G., Silva, M. A., Duran, F., Cavallet, M., Serpa, M. H., Caetano, S. C., Louzã, M. R., et al. (2017) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.12824 · Cited by 31
“T1-weighted images were preprocessed by correcting for signal inhomogeneities and removing extracerebral tissue, and automatically segmented into 259 expert-defined anatomical ROIs using a stateof-the-art multi-atlas label fusion method (44).”
MethodSelf-citationMorton, S. U., Vyas, R., Gagoski, B. A., Vu, C., Litt, J. S., Larsen, R. J., Kuchan, M. J., Lasekan, J. B., Sutton, B. P., Grant, P. E., et al. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz222 · Cited by 29
Self-citationLinn, K. A., Gaonkar, B., Satterthwaite, T. D., Doshi, J. J., Davatzikos, C. A., & Shinohara, R. T. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.044 · Cited by 30
Self-citationSkull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning
Thakur, S. P., Doshi, J. J., Pati, S., Ha, S. M., Sako, C., Talbar, S. N., Kulkarni, U., Davatzikos, C. A., Erus, G., & Bakas, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46640-4_6 · Cited by 28
Self-citationMultimodal data integration via mediation analysis with high‐dimensional exposures and mediators
Zhao, Y., Li, L., & Initiative, A. D. N. (2022) · Human Brain Mapping · doi:10.1002/hbm.25800 · Cited by 25
Skampardoni, I., Nasrallah, I. M., Abdulkadir, A., Wen, J., Melhem, R., Mamourian, E., Erus, G., Doshi, J., Singh, A., Yang, Z., et al. (2024) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2023.5599 · Cited by 28
Self-citationTosun, D., Yardibi, O., Benzinger, T. L. S., Kukull, W. A., Masters, C. L., Perrin, R. J., Weiner, M. W., Simen, A. A., Schwarz, A. J., & Initiative, A. D. N. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13447 · Cited by 27
Hippocampal Segmentation in Brain MRI Images Using Machine Learning Methods: A Survey
Pan, Y., Liu, J., Tian, X., Lan, W., & Guo, R. (2021) · Chinese Journal of Electronics · doi:10.1049/cje.2021.06.002 · Cited by 28
Hippocampal activation and connectivity in the aging brain
Beason‐Held, L. L., Shafer, A. T., Goh, J. O. S., Landman, B. A., Davatzikos, C. A., Viscomi, B., Ash, J. A., Kitner‐Triolo, M. H., Ferrucci, L., & Resnick, S. M. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00279-6 · Cited by 28
Self-citationDisentangling Alzheimer’s disease neurodegeneration from typical brain ageing using machine learning
Hwang, G., Abdulkadir, A., Erus, G., Habes, M., Pomponio, R., Shou, H., Doshi, J. J., Mamourian, E., Rashid, T., Bilgel, M., et al. (2022) · Brain Communications · doi:10.1093/braincomms/fcac117 · Cited by 31
Self-citationLyons‐Ruth, K., Li, F. H., Khoury, J. E., Ahtam, B., Sisitsky, M. D., Ou, Y., Enlow, M. B., & Grant, P. E. (2023) · Research on Child and Adolescent Psychopathology · doi:10.1007/s10802-023-01041-4 · Cited by 31
“…and white matter as well as subcortical gray matter regions, including amygdala and hippocampus, using a multi-atlas-to-subject registration and fusion (Doshi et al., 2016), that have been validated (Ou et al., 2011, 2014) and adapted to infant brain MRIs (Morton et al., 2020; Ou et al., 2017).”
MethodSelf-citationKhoury, J. E., Ahtam, B., Sisitsky, M. D., Ou, Y., Gagoski, B. A., Enlow, M. B., Teicher, M. H., Grant, P. E., & Lyons‐Ruth, K. (2021) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2021.09.005 · Cited by 26
Self-citationDuggan, M. R., Peng, Z., An, Y., Kitner Triolo, M. H., Shafer, A. T., Davatzikos, C., Erus, G., Karikkineth, A., Lewis, A., Moghekar, A., et al. (2022) · Neurology · doi:10.1212/wnl.0000000000201036 · Cited by 24
“By combining different atlases, warping algorithms, standardizing parameters, and using a consensus labeling approach to fuse these labels into a final segmentation, the multiatlas region segmentation utilizing ensembles (MUSE) anatomic labeling method generates an ensemble of labeled atlases in target image space; a…”
ResultSelf-citationVijayakumari, A. A., Parker, D., Osmanlıoğlu, Y., Alappatt, J. A., Whyte, J., Diaz-Arrastia, R. R., Kim, J., & Verma, R. (2021) · Journal of Neurotrauma · doi:10.1089/neu.2021.0057 · Cited by 24
Wang, D., Honnorat, N., Toledo, J. B., Li, K., Charisis, S., Rashid, T., Nirmala, A. B., Brandigampala, S. R., Mojtabai, M., Seshadri, S. S., et al. (2024) · Brain · doi:10.1093/brain/awae388 · Cited by 26
“59 The skull-stripped scans were then registered to the 1 mm 3 resolution 2009c version of the ICBM152 MNI atlas 60 of size 193 × 229 × 193 using the non-rigid SyN registration method 61 provided with the ANTs library (v. 2.3.4).”
InfluentialPeng, Z., Duggan, M. R., Dark, H. E., Daya, G. N., An, Y., Davatzikos, C. A., Erus, G., Lewis, A., Moghekar, A., & Walker, K. A. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.08.004 · Cited by 24
Self-citationHwang, G., Wen, J., Sotardi, S. T., Brodkin, E. S., Chand, G. B., Dwyer, D., Erus, G., Doshi, J. J., Singhal, P., Srinivasan, D., et al. (2023) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2023.0409 · Cited by 26
Self-citationWen, J., Yang, Z., Nasrallah, I. M., Cui, Y., Erus, G., Srinivasan, D., Abdulkadir, A., Mamourian, E., Skampardoni, I., Hwang, G., et al. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03121-5 · Cited by 31
“In total, 145 anatomical regions of interest (ROIs) were generated in gray matter (GM, 119 ROIs), white matter (WM, 20 ROIs), and ventricles (6 ROIs) using a multi ‐ atlas label fusion method [36] (Supplementary eMethod 3).”
MethodSelf-citationAlzheimer's disease diagnostic accuracy by fluid and neuroimaging ATN framework
Xiong, X., He, H., Ye, Q., Qian, S., Zhou, S., Feng, F., Fang, E. F., & Xie, C. (2023) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.14357 · Cited by 24
“37 The software can be ob - tained from the website https://www.cbica.upenn.edu/sbia/softw are/index.html.”
MethodSmoking mediates the relationship between SES and brain volume: The CARDIA study
Dougherty, R. J., Moonen, J. E. F., Yaffe, K. S., Sidney, S., Davatzikos, C. A., Habes, M., & Launer, L. J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0239548 · Cited by 23
“Each T1-weighted scan is automatically segmented into a set of anatomical gray matter regions of interest (ROIs) using a mutli-atlas label fusion method, MUSE [33].”
MethodSelf-citation
Metadata
- Authors
- Doshi, J., Erus, G., Ou, Y., Resnick, S. M., Gur, R. C., Gur, R. E., Satterthwaite, T. D., Furth, S., & Davatzikos, C.
- Affiliations
- University of Pennsylvania (US)
- Harvard University (US)
- National Institute on Aging (US)
- Children's Hospital of Philadelphia (US)
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-02
- Volume
- 127
- Pages
- 186-195
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4806537
- OpenAlex
- W2196038229
- Semantic Scholar
- 2cee0b733a1a63fe50ade982d837f892a00ea1c0
- Cited by
- 309
- References
- 56
- Citations by year
- 2026: 26 · 2025: 45 · 2024: 35 · 2023: 41 · 2022: 39 · 2021: 34 · 2020: 27 · 2019: 25 · 2018: 15 · 2017: 12 · 2016: 10
- FWCI
- 10.62
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- Funders
- Intramural Research Program, National Institute on Aging, NIH
- National Institutes of Health (R01-AG014971)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
- Keywords
- multi-atlas segmentation, anatomical labeling, atlas selection, label fusion, brain segmentation, medical image segmentation, web-based image processing
- MeSH terms
- Algorithms, Anatomy, Artistic, Atlases as Topic, Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Pattern Recognition, Automated
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