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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
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InfluentialPrediction of individual brain maturity using fMRI
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“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…”
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Items 101–200 of 310
Race, sex, and mid‐life changes in brain health: Cardia MRI substudy
Moonen, J. E. F., Nasrallah, I. M., Detre, J. A., Dolui, S., Erus, G., Davatzikos, C. A., Meirelles, O., Bryan, R. N., & Launer, L. J. (2022) · Alzheimer s & Dementia · doi:10.1002/alz.12560 · Cited by 23
Self-citationDuggan, M. R., Yang, Z., Cui, Y., Dark, H. E., Wen, J., Erus, G., Hohman, T. J., Chen, J., Lewis, A., Moghekar, A., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14142 · Cited by 20
Self-citationSteward, K. A., Kennedy, R., Erus, G., Nasrallah, I. M., & Wadley, V. G. (2019) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.04.023 · Cited by 21
Self-citationWen, J., Skampardoni, I., Tian, Y., Yang, Z., Cui, Y., Erus, G., Hwang, G., Varol, E., Boquet-Pujadas, A., Chand, G. B., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01412-w · Cited by 21
Self-citationPrediction of rapid amyloid and phosphorylated‐Tau accumulation in cognitively healthy individuals
Koychev, I. G., Vaci, N., Bilgel, M., An, Y., Terrera, G. M., Wong, D. F., Gallacher, J., Mogekhar, A., Albert, M., & Resnick, S. M. (2020) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12019 · Cited by 21
Self-citationZhu, H., Tang, Z., Cheng, H., Wu, Y., & Yong Fan, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-53387-9 · Cited by 20
“In MAIS methods, each atlas image is first registered to the target image and the atlas label is propagated to the target image space accordingly 7 .”
MethodHarmonization with Flow-Based Causal Inference
Wang, R., Chaudhari, P., & Davatzikos, C. A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87199-4_17 · Cited by 20
Self-citationBilgel, M., Bannerjee, A., Shafer, A. T., An, Y., & Resnick, S. M. (2021) · Neuroimage Reports · doi:10.1016/j.ynirp.2021.100068 · Cited by 20
Self-citationChand, G. B., Thakuri, D. S., & Soni, B. (2022) · Journal of Neuroimaging · doi:10.1111/jon.12980 · Cited by 19
Dintica, C. S., Habes, M., Erus, G., Vittinghoff, E., Davatzikos, C. A., Nasrallah, I. M., Launer, L. J., Sidney, S. S., & Yaffe, K. S. (2022) · Alzheimer s & Dementia · doi:10.1002/alz.12725 · Cited by 20
Self-citationAustin, T. R., Jensen, P. N., Nasrallah, I. M., Habes, M., Rashid, T., Ware, J. B., Chen, L. Y., Greenland, P., Hughes, T. M., Post, W. S., et al. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.122.026460 · Cited by 21
Fu, C. H. Y., Erus, G., Yong Fan, A., Antoniades, M., Arnone, D., Arnott, S. R., Chen, T., Choi, K. S., Fatt, C. C., Frey, B. N., et al. (2023) · BMC Psychiatry · doi:10.1186/s12888-022-04509-7 · Cited by 24
Self-citationZheng, Q., Wu, Y., & Yong Fan, A. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00069 · Cited by 20
“Besides improving the image registration component (Hao et al., 2012b; Alven et al., 2016; Doshi et al., 2016; Alchatzidis et al., 2017), many MAIS methods have focused on improving the label fusion component (Rohlfing et al., 2004; Coupé et al., 2011; Han, 2013; Wang et al., 2013; Hao et al.,…”
MethodLife's Essential 8 and midlife trajectories in cognition and brain health: The CARDIA study
Dintica, C. S., Jiang, X., Launer, L. J., Bryan, R. N., & Yaffe, K. S. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14464 · Cited by 19
“31 The images were visually checked for incidental findings, motion artifacts, and other quality issues.”
Deconvolution-based partial volume correction of PET images with parallel level set regularization
Zhu, Y., Bilgel, M., Gao, Y., ROUSSET, O. G., Resnick, S. M., Wong, D. F., & Rahmim, A. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac0d8f · Cited by 17
Self-citationβ-amyloid deposition is associated with gait variability in usual aging
Tian, Q., Bair, W.-N., Resnick, S. M., Bilgel, M., Wong, D. F., & Studenski, S. A. (2018) · Gait & Posture · doi:10.1016/j.gaitpost.2018.02.002 · Cited by 20
“Data acquisition and processing were described elsewhere [11].”
MethodSelf-citationAutomated landmarking via multiple templates
Zhang, C., Porto, A., Rolfe, S., Kocatulum, A., & Maga, A. (2022) · bioRxiv · doi:10.1371/journal.pone.0278035 · Cited by 20
Aslanyan, V., Mack, W. J., Ortega, N. E., Nasrallah, I. M., Pajewski, N. M., Williamson, J. D., & Pa, J. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13572 · Cited by 17
Karger, A. B., Nasrallah, I. M., Braffett, B. H., Luchsinger, J. A., Ryan, C. M., Bebu, I., Arends, V. L., Habes, M., Gubitosi‐Klug, R., Chaytor, N. S., et al. (2024) · Diabetes Care · doi:10.2337/dc24-0229 · Cited by 18
FCN Based Label Correction for Multi-Atlas Guided Organ Segmentation
Initiative, A. D. N., Zhu, H., Adeli, E., Shi, F., & Shen, D. G. (2020) · Neuroinformatics · doi:10.1007/s12021-019-09448-5 · Cited by 18
Shafer, A. T., Beason-Held, L., An, Y., Williams, O. A., Huo, Y., Landman, B. A., Caffo, B. S., & Resnick, S. M. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.03.013 · Cited by 16
Self-citationJoynes, C. M., Bilgel, M., An, Y., Moghekar, A., Ashton, N. J., Kac, P. R., Karikari, T. K., Blennow, K., Zetterberg, H., Thambisetty, M., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14405 · Cited by 18
“All regions for the MRI measures were segmented and labeled using the MUlti-atlas region Segmentation utilizing Ensembles (MUSE) software package [26].”
Self-citationShah, C., Srinivasan, D., Erus, G., Schmitt, J. E., Agarwal, A., Cho, M. E., Lerner, A. J., Haley, W. E., Tamura, M. K., Davatzikos, C. A., et al. (2021) · Neuroradiology · doi:10.1007/s00234-020-02614-6 · Cited by 17
Self-citationAssociation of brain microbleeds with risk factors, cognition, and MRI markers in MESA
Jensen, P. N., Rashid, T., Ware, J. B., Cui, Y., Sitlani, C. M., Austin, T. R., Longstreth, W. T., Bertoni, A. G., Mamourian, E., Bryan, R. N., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13346 · Cited by 17
Sex differences in plasma lipid profiles of accelerated brain aging
Tian, Q., Mitchell, B. A., Erus, G., Davatzikos, C. A., Moaddel, R., Resnick, S. M., & Ferrucci, L. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2023.05.013 · Cited by 17
Self-citationEstimating regional cerebral blood flow using resting-state functional MRI via machine learning
Chand, G. B., Habes, M., Dolui, S., Detre, J. A., Wolk, D. A., & Davatzikos, C. A. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.108528 · Cited by 14
“Total 117 anatomical gray matter regions of interest (ROIs) defined by a multi-atlas segmentation method (MUSE) (Doshi et al., 2013; Doshi et al., 2016) were considered.”
MethodSelf-citationGao, Y., Zhu, Y., Bilgel, M., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.07.028 · Cited by 15
Long-term depressive symptoms and midlife brain age
Dintica, C. S., Habes, M., Erus, G., Simone, T., Schreiner, P. J., & Yaffe, K. S. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.09.164 · Cited by 17
Self-citationSahoo, D., Satterthwaite, T. D., & Davatzikos, C. A. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3042873 · Cited by 15
“• PNC-Philadelphia Neuro-developmental Cohort (PNC) [35] dataset contains 969 subjects (ages from 8 to 22) each having 120 time points and 121 nodes described in [44].”
BackgroundSelf-citationBlom, K., Koek, H. L., Zwartbol, M. H. T., Ghaznawi, R., Kuijf, H. J., Witkamp, T. D., Hendrikse, J., Biessels, G. J., Geerlings, M. I., & on behalf of the UCC-SMART Study Group (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200159 · Cited by 15
Automated segmentation of pediatric brain tumors based on multi-parametric MRI and deep learning
Madhogarhia, R., Kazerooni, A. F., Arif, S., Ware, J. B., Familiar, A., Vidal, L., Bagheri, S., Anderson, H., Haldar, D., Yagoda, S., et al. (2022) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2611551 · Cited by 15
Self-citationSociodemographic disparities in corticolimbic structures
Shaked, D., Millman, Z. B., Moody, D. L. B., Rosenberger, W. F., Shao, H., Katzel, L. I., Davatzikos, C. A., Gullapalli, R. P., Seliger, S. L., Erus, G., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0216338 · Cited by 17
“This sophisticated image segmentation method integrates a broad ensemble of labeled templates through the use of several warping algorithms, regularization parameters, and atlases [50].”
MethodSelf-citationMulti-atlas active contour segmentation method using template optimization algorithm
Wang, M., Li, P., & Liu, F. (2019) · BMC Medical Imaging · doi:10.1186/s12880-019-0340-6 · Cited by 14
“Jimit et al. [15] proposed a multi-atlas region segmentation utilizing ensembles (MUSE), which improves segmentation accuracy by linear combinations of different atlas selections, deformation algorithms and deformation parameters.”
MethodBao, J., Wen, J., Wen, Z., Yang, S., Cui, Y., Yang, Z., Erus, G., Saykin, A. J., Long, Q., Davatzikos, C. A., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120346 · Cited by 15
“A multi-atlas parcellation method (MUSE) (Doshi et al. 2016) was then used to extract 145 regions of interest (ROI) from”
MethodInfluentialSelf-citationAntoniades, M., Srinivasan, D., Wen, J., Erus, G., Abdulkadir, A., Mamourian, E., Melhem, R., Hwang, G., Cui, Y., Govindarajan, S. T., et al. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105399 · Cited by 13
“Volumes for 145 ROIs derived using MUSE 10 were used as the fi nal features for the structural dataset.”
MethodSelf-citationWhy experimental variation in neuroimaging should be embraced
Kiar, G., Mumford, J. A., Xu, T., Vogelstein, J. T., Tristan Glatard, B., & Milham, M. P. (2024) · Nature Communications · doi:10.1038/s41467-024-53743-y · Cited by 18
Brain–heart–eye axis revealed by multi-organ imaging genetics and proteomics
Zalesky, A., Tian, Y., Ferrucci, L., Bai, W., Rafii, M. S., Aisen, P. S. S., Zeng, J., Boquet-Pujadas, A., Anagnostakis, F., Duggan, M. R., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01506-5 · Cited by 11
Self-citationYang, A. I., Parker, D., Vijayakumari, A. A., et al. (2022) · Neurosurgery · doi:10.1227/neu.0000000000001840 · Cited by 14
Medical Image Analysis: Human and Machine
Bryan, R. N., Davatzikos, C. A., Herskovits, E. H., Mohan, S., Rudie, J. D., & Rauschecker, A. M. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.09.011 · Cited by 14
Self-citationSensory impairment and beta‐amyloid deposition in the Baltimore longitudinal study of aging
Yesantharao, L. V., Cai, Y., Schrack, J. A., Gross, A. L., Wang, H., Bilgel, M., Dougherty, R. J., Simonsick, E. M., Ferrucci, L., Resnick, S. M., et al. (2023) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12407 · Cited by 12
Self-citationBhattarai, P., Taha, A., Soni, B., Thakuri, D. S., Ritter, E., & Chand, G. B. (2023) · Brain Informatics · doi:10.1186/s40708-023-00213-8 · Cited by 10
“All participants got T1-weighted brain MRI using 3T Biograph MR or 1.5T Vision, Siemens 3T TrioTim. of registration algorithms and parameters and locally optimal atlas selection (MUSE) was used for individual T1-MRI segmentation [28, 29, 40–42].”
MethodLocalised delineation uncertainty for iterative atlas selection in automatic cardiac segmentation
Finnegan, R., Lorenzen, E. L., Dowling, J., Holloway, L. C., Thwaites, D., & Brink, C. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab652a · Cited by 13
Energetic Cost of Walking and Brain Atrophy in Mid-to-Late Life
Dougherty, R. J., Liu, F., An, Y., Wanigatunga, A. A., Tian, Q., Davatzikos, C. A., Simonsick, E. M., Ferrucci, L., Resnick, S. M., & Schrack, J. A. (2021) · The Journals of Gerontology Series A · doi:10.1093/gerona/glab309 · Cited by 13
Self-citationTrajectories in depressive symptoms and midlife brain health
Dintica, C. S., Habes, M., Schreiner, P. J., Launer, L. J., & Yaffe, K. S. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02883-2 · Cited by 15
“We de fi ned poor cognitive function using a cutoff ≥ 1 SD below the CARDIA cohort mean for the cognitive composite score, as previously used in CARDIA [19] and other population-based studies [20].”
MethodIdentification of Clinically Relevant Brain Endothelial Cell Biomarkers in Plasma
Cordon, J., Duggan, M. R., Gómez, G., Pucha, K. A., Peng, Z., Dark, H. E., Davatzikos, C. A., Erus, G., Lewis, A., Moghekar, A., et al. (2023) · Stroke · doi:10.1161/strokeaha.123.043908 · Cited by 11
Self-citationComputational and mathematical methods in brain atlasing
Nowinski, W. L. (2017) · The Neuroradiology Journal · doi:10.1177/1971400917740362 · Cited by 12
Zheng, Q., & Yong Fan, A. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363544 · Cited by 12
“In addition to improving image registration [2-6], most MAIS methods are developed to improve the label fusion [7-14].”
Long-term television viewing patterns and gray matter brain volume in midlife
Dougherty, R. J., Hoang, T. D. M., Launer, L. J., Jacobs, D. R., Sidney, S., & Yaffe, K. S. (2021) · Brain Imaging and Behavior · doi:10.1007/s11682-021-00534-4 · Cited by 10
Evaluating a novel 24-hour rest/activity rhythm marker of preclinical β-amyloid deposition
Spira, A. P., Liu, F., Zipunnikov, V., Bilgel, M., Rabinowitz, J. A., An, Y., Di, J., Bai, J., Wanigatunga, S., Wu, M. N., et al. (2024) · SLEEP · doi:10.1093/sleep/zsae037 · Cited by 11
Self-citationDuggan, M. R., Yang, S., Gómez, G., Cui, Y., Capuano, A. W., Chen, J., Yang, Z., Wen, J., Erus, G., Drouin, S. M., et al. (2025) · Science Advances · doi:10.1126/sciadv.adt7176 · Cited by 12
“We applied a validated, Multi-Atlas Region Segmentation Utilizing Ensembles anatomic labeling method specifically designed to achieve a consistent parcellation of brain anatomy in longitudinal MRI studies using T1-weighted sequences ( 61 ).”
Self-citationVestibular Function and Beta-Amyloid Deposition in the Baltimore Longitudinal Study of Aging
Kamil, R. J., Bilgel, M., Wong, D. F., Resnick, S. M., & Agrawal, Y. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00408 · Cited by 11
Self-citationSmith, A. L., Weissbart, S. J., Hartigan, S. M., et al. (2019) · Neurourology and Urodynamics · doi:10.1002/nau.24206 · Cited by 11
Self-citationDisease Burden Affects Aging Brain Function
Beason‐Held, L. L., Fournier, D. I., Shafer, A. T., Fabbri, E., An, Y., Huang, C.-W., Bilgel, M., Wong, D. F., Ferrucci, L., & Resnick, S. M. (2021) · The Journals of Gerontology Series A · doi:10.1093/gerona/glab218 · Cited by 11
Self-citationHigher literacy is associated with better white matter integrity and cognition in middle age
Resende, E. D. P. F., Xia, F., Sidney, S., Launer, L. J., Schreiner, P. J., Erus, G., Bryan, R. N., & Yaffe, K. S. (2022) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12363 · Cited by 11
Self-citationPark, G., Kwak, K., Seo, S. W., & Lee, J. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00629 · Cited by 12
Yang, Z., Wen, J., & Davatzikos, C. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15255 · Cited by 11
“For all T1 MRIs, brain tissue segmentation was performed using a multi-atlas segmentation technique [16] and 145 ROIs were derived as features for Smile-GANs.”
MethodSelf-citationWen, J., Yang, Z., Nasrallah, I. M., Cui, Y., Erus, G., Srinivasan, D., Abdulkadir, A., Mamourian, E., Skampardoni, I., Hwang, G., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.16.508329 · Cited by 12
Self-citationMesse, S. R., Erus, G., Bilello, M., et al. (2021) · Stroke · doi:10.1161/strokeaha.121.034451 · Cited by 10
Self-citationGugger, J. J., Walter, A. E., Parker, D., Sinha, N., Morrison, J., Ware, J. B., Schneider, A. L. C., Petrov, D., Sandsmark, D. K., Verma, R., et al. (2022) · Journal of Neurotrauma · doi:10.1089/neu.2022.0259 · Cited by 10
“17 Diffusion MRI data was preprocessed in three steps.”
Soldan, A., Wang, J., Pettigrew, C. A., Davatzikos, C. A., Erus, G., Hohman, T. J., Dumitrescu, L. C., Bilgel, M., Resnick, S. M., Rivera‐Rivera, L. A., et al. (2024) · Brain Communications · doi:10.1093/braincomms/fcae276 · Cited by 10
“84 This method was specifically designed for longitudinal studies to handle differences in scanners and imaging protocols over time and across sites and employs harmonized acquisition-specific atlases.”
MethodSelf-citationTian, Q., Greig, E. E., Davatzikos, C. A., Landman, B. A., Resnick, S. M., & Ferrucci, L. (2024) · Nature Communications · doi:10.1038/s41467-024-55009-z · Cited by 10
Self-citationLinking maternal disrupted interaction and infant limbic volumes: The role of infant cortisol output
Khoury, J. E., Ahtam, B., Ou, Y., Jenkins, E., Klengel, T., Enlow, M. B., Grant, P. E., & Lyons‐Ruth, K. (2023) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2023.106379 · Cited by 11
Self-citationVedmurthy, P., Pinto, A., Lin, D. D. M., Comi, A. M., & Ou, Y. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-053103 · Cited by 10
Self-citationBeydoun, M. A., Hooten, N. N., Weiss, J., Maldonado, A. I., Beydoun, H. A., Katzel, L. I., Davatzikos, C. A., Gullapalli, R. P., Seliger, S. L., Erus, G., et al. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.10.004 · Cited by 10
Self-citationdu Plessis, S., Chand, G. B., Erus, G., Phahladira, L., Luckhoff, H. K., Smit, R., Asmal, L., Wolf, D. H., Davatzikos, C. A., & Emsley, R. A. (2023) · Schizophrenia Bulletin · doi:10.1093/schbul/sbad040 · Cited by 13
Self-citationSerpa, M. H., Doshi, J. J., Joaquim, H. P. G., Vieira, É. L. M., Erus, G., Chaim-Avancini, T. M., Cavallet, M., Guglielmi, L. G., Sallet, P. C., Talib, L. L., et al. (2023) · Schizophrenia Research · doi:10.1016/j.schres.2023.05.005 · Cited by 10
Self-citationAnagnostakis, F., Kokkorakis, M., Walker, K. A., Skampardoni, I., Wen, J., Erus, G., Tosun, D., Tassopoulou, V., Cui, Y., Govindarajan, S. T., et al. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.105763 · Cited by 9
Self-citationChuang, Y., Varma, V. R., An, Y., Tanaka, T., Davatzikos, C. A., Resnick, S. M., & Thambisetty, M. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-191335 · Cited by 8
Self-citationCLAIRE—Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications
Himthani, N., Brunn, M., Kim, J., Schulte, M., Mang, A., & Biros, G. (2022) · Journal of Imaging · doi:10.3390/jimaging8090251 · Cited by 9
Autism-associated brain differences can be observed in utero using MRI
Ortuğ, A., Guo, Y., Feldman, H. A., Ou, Y., Warren, J. L. A., Dieuveuil, H., Baumer, N., Faja, S., & Takahashi, E. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae117 · Cited by 10
Self-citationZhuang, Z., Lin, J.-X., Wan, Z., Weng, J.-R., Yuan, Z., Xie, Y.-M., Liu, Z., Xie, P., Mao, S., Wang, Z., et al. (2024) · BMC Medicine · doi:10.1186/s12916-024-03573-y · Cited by 10
Hajipour, M., Hu, W.-H., Esmaeili, N., Sands, S. A., Wellman, A., Kwon, Y., Gonzalo Labarca, T., Nasrallah, I. M., Bryan, R. N., Strollo, P. J., et al. (2024) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.11422 · Cited by 8
The Dementia SomaSignal Test (dSST): A plasma proteomic predictor of 20‐year dementia risk
Duggan, M. R., Paterson, C., Lü, Y., Biegel, H., Dark, H. E., Cordon, J., Bilgel, M., Kaneko, N., Shibayama, M., Kato, S., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.14549 · Cited by 10
Self-citationCharisis, S., Rashid, T., Dintica, C. S., Gonzales, M. M., Liu, H., Ware, J. B., Austin, T. R., Jensen, P. N., Fohner, A. E., Tanley, J. E., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70326 · Cited by 8
Self-citationLyons‐Ruth, K., Ahtam, B., Li, F. H., Dickerman, S., Khoury, J. E., Sisitsky, M. D., Ou, Y., Enlow, M. B., Teicher, M. H., & Grant, P. E. (2023) · Human Brain Mapping · doi:10.1002/hbm.26401 · Cited by 9
“…gray and white matter as well as subcortical gray matter regions, including amygdala and hippocampus, using 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-citationGenerative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples
Chintapalli, S. S., Wang, R., Yang, Z., Tassopoulou, V., Yu, F., Bashyam, V., Erus, G., Chaudhari, P., Shou, H., & Davatzikos, C. A. (2024) · Scientific Data · doi:10.1038/s41597-024-04157-4 · Cited by 13
Self-citationProbabilistic estimation for across‐batch compatibility enhancement for amyloid PET
Bilgel, M. (2023) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12436 · Cited by 7
Armstrong, N. M., Dumitrescu, L. C., Huang, C.-W., An, Y., Tanaka, T., Hernandez, D. G., Doshi, J. J., Erus, G., Davatzikos, C. A., Ferrucci, L., et al. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.05.007 · Cited by 7
Self-citationThe Role of Race in Relations of Social Support to Hippocampal Volumes Among Older Adults
Bygrave, D. C., Gerassimakis, C., Mwendwa, D. T., Erus, G., Davatzikos, C. A., & Wright, R. S. (2021) · Research on Aging · doi:10.1177/01640275211017268 · Cited by 7
“Hemispheric frontal, temporal, occipital, parietal, and deep brain volumes were calculated within each ROI, as well as in l. 3 208 Research on Aging 44(2) larger anatomical regions obtained by grouping single ROIs within a hierarchical representation.”
MethodInfluentialSelf-citationPadova, D., Ratnanather, J. T., Xue, Q., Resnick, S. M., & Agrawal, Y. (2021) · Aperture Neuro · doi:10.52294/6727e860-95c1-445c-a47a-177d9e699d46 · Cited by 7
Self-citationGong, Z., Lu, Z., Zhao, D., Wang, S., Liu, Y., Song, Y., Xuan, K., Tan, W., & Li, C. (2017) · Digital Medicine · doi:10.4103/digm.digm_23_17 · Cited by 7
“Recently, multi-atlas approaches have received more attentions due to its ability of segmenting complex tissues.[1-7] In the past few years, multi-atlas segmentation (MAS) develops rapidly.”
BackgroundLiu, Z., Initiative, A. D. N., Maiti, T., & Bender, A. R. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-201398 · Cited by 9
Cooper, C. P., Shafer, A. T., Armstrong, N. M., An, Y., Erus, G., Davatzikos, C. A., Ferrucci, L., Rapp, P. R., & Resnick, S. M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120048 · Cited by 7
“anatomical labeling approach (Doshi et al., 2016) with harmonized acquisition-specific atlases (Erus et al.”
Self-citationShared and Distinct Associations of Manual Dexterity and Gross Motor Function With Brain Atrophy
Dougherty, R. J., Wang, H., Gross, A. L., Schrack, J. A., Agrawal, Y., Davatzikos, C. A., Cai, Y., Simonsick, E. M., Ferrucci, L., Resnick, S. M., et al. (2023) · The Journals of Gerontology Series A · doi:10.1093/gerona/glad245 · Cited by 5
Self-citationVazquez, J. P., Pettigrew, C. A., Zhu, Y., Anderson, C. L., Erus, G., Davatzikos, C. A., Miller, M. I., Moghekar, A., Oh, S., Na, C. H., et al. (2025) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.70216 · Cited by 6
Self-citationXiao, W., Woodham, R. D., Cui, Y., Wen, J., Antoniades, M., Srinivasan, D., Yong Fan, A., Erus, G., García, J., Arnott, S. R., et al. (2025) · Communications Medicine · doi:10.1038/s43856-025-01219-5 · Cited by 7
Self-citationPierpaoli, C. M., Nayak, A., Hafiz, R., Irfanoglu, M. O., Chen, G., Taylor, P. A., Hallett, M., Hoa, M., Pham, D. L., Chou, Y., et al. (2024) · JAMA · doi:10.1001/jama.2024.2424 · Cited by 7
Image Retrieval for Alzheimer’s Disease Based on Brain Atrophy Pattern
Trojacanec, K., Kalajdziski, S., Kitanovski, I., et al. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-67597-8_16 · Cited by 6
Registration Error and Intensity Similarity Based Label Fusion for Segmentation
Lin, X., Li, X., & Guo, D. (2019) · IRBM · doi:10.1016/j.irbm.2019.02.001 · Cited by 6
Wen, Z., & Biros, G. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103757 · Cited by 5
HILAMA: High-dimensional multi-omics mediation analysis with latent confounding
Wang, X., Liu, J., Hu, S. S., Liu, Z., Lu, H. J., Liu, L., & Initiative, T. A. D. N. (2025) · BMC Medical Research Methodology · doi:10.1186/s12874-025-02686-z · Cited by 5
“Additionally, the brain imaging data is obtained through anatomical magnetic resonance imaging (MRI), and volumetric measurements are extracted from 145 brain regions-of-interest (ROI) [63].”
Drouin, S. M., Kuo, P., Blew, C., Duggan, M. R., Chen, D., Smith, H. M., Chen, J., Giorgio, K., Gómez, G., Moore, A. Z., et al. (2025) · Aging Cell · doi:10.1111/acel.70281 · Cited by 5
“Volumes of anatomical regions of interest were segmented using Multi-atlas Region Segmentation Utilizing Ensembles (Doshi et al. 2016).”
Self-citationAssociation of MRI-Visible Perivascular Spaces With Longitudinal Cognitive Decline Over a Decade
Kohno, K., Sun, Y., LeFevre, J. D., Robb, W. H., Jackson, T. B., Liu, D., Vyas, Y., Sweely, B., Pechman, K. R., Shashikumar, N., et al. (2026) · Neurology · doi:10.1212/wnl.0000000000214803 · Cited by 9
Davatzikos, C. A. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.06.026 · Cited by 5
“MUSE utilizes an ensemble of atlases warped to an individual MRI with multiple transformations, each emanating from a different algorithm/optimization criterion, and multiple parameters, which influence the amount of regularization and other properties of these transformations.”
MethodSelf-citationPlasma neurofilament light and brain volumetric outcomes among middle-aged urban adults
Beydoun, M. A., Hooten, N. N., Beydoun, H. A., Weiss, J., Maldonado, A. I., Katzel, L. I., Davatzikos, C. A., Gullapalli, R. P., Seliger, S. L., Erus, G., et al. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2023.04.013 · Cited by 5
Self-citationRathore, S., Niazi, T. M., Iftikhar, M. A., Singh, A., Rathore, B., Bilello, M., & Chaddad, A. (2020) · Applied Sciences · doi:10.3390/app10061903 · Cited by 5
Weinstein, S. M., Davatzikos, C. A., Doshi, J. J., Linn, K. A., Shinohara, R. T., & Initiative, A. D. N. (2022) · Biostatistics · doi:10.1093/biostatistics/kxac031 · Cited by 6
Self-citationDeep convolutional neural network for hippocampus segmentation with boundary region refinement
He, G., Zhang, G., Zhou, L., et al. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02836-9 · Cited by 5
“, atlas selection [10, 11], image registration [12, 13], and label fusion [14–16].”
BackgroundInfarct‐related structural disconnection and delirium in surgical aortic valve replacement patients
Browndyke, J. N., Tomalin, L. E., Erus, G., Overbey, J., Kuceyeski, A. F., Moskowitz, A. J., Bagiella, E., Iribarne, A., Acker, M. G., Mack, M. J., et al. (2023) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.51949 · Cited by 6
Self-citationBayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging
Brown, D. A., McMahan, C. S., Shinohara, R. T., Linn, K. A., & Initiative, A. D. N. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.2014854 · Cited by 4
“Doshi et al. (2016) proposed MAS via ensembles of different registration algorithms, as opposed to applying the same registration algorithm to every atlas.”
Method
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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