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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
Cites
Hirono, N., Mori, E., Ikejiri, Y., Imamura, T., Shimomura, T., Hashimoto, M., Yamashita, H., & Ikeda, M. (1997) · PubMed · Cited by 21,012
A concordance correlation coefficient to evaluate reproducibility.
Lin, L. I.-K. (1989) · Biometrics · doi:10.2307/2532051 · Cited by 8,785 · cited 2×
“We calculated Pearson's correlation coefficient (r), concordance correlation coefficient (ccc) (Lin, 1989) and mean absolute error (MAE) as quantitative metrics of prediction accuracy.”
Avants, B., Epstein, C. L., Grossman, M., & Gee, J. C. (2007) · Medical Image Analysis · doi:10.1016/j.media.2007.06.004 · Cited by 5,924 · cited 4×
“For both methods, the main parameter that regulates the smoothness of the deformation field was sampled at two operational points, specifically g = {0.1, 0.2} for DRAMMS and s = {0.25, 0.5} for ANTS.”
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
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347
Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration
Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G. E., Collins, D. L., Gee, J., Hellier, P., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.037 · Cited by 2,227 · cited 6×
“, 2011, 2014) and ANTS (Avants et al., 2008; Klein et al., 2009), for transferring atlas labels to target space.”
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
Items 301–310 of 310
Yang, B., Earnest, T., Bilgel, M., Albert, M., Johnson, S. C., Davatzikos, C. A., Erus, G., Masters, C. L., Resnick, S. M., Miller, M. I., et al. (2026) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2026.04.005
Self-citationA comparative evaluation of multiple enlarged perivascular space segmentation tools
LeFevre, J. D., Robb, W. H., Liu, D., Jackson, T. B., Pechman, K. R., Shashikumar, N., Vyas, Y., Landman, B. A., Davis, L. T., Hohman, T. J., et al. (2026) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2026.110749
Comprehensive social determinants of health and brain health in midlife
Dintica, C. S., Cheunkarndee, J., Bryan, R. N., Launer, L. J., & Yaffe, K. S. (2026) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2026.100652
“Each T1-weighted scan was then automatically segmented into a set of anatomical ROIs using a multi-atlas label fusion method [26].”
Implementing AI in Neuroradiology
Shanker, R. R. B. J., Shetty, A., & Ginat, D. T. (2026) · Springer eBooks · doi:10.1007/978-3-032-24568-7_2
Charisis, S., Pajewski, N. M., Price, L. R., Ho, N.-H., Rashid, T., Wang, D., Zeng, Y., Kutty, N. S., Bryan, R. N., Kern, K. C., et al. (2026) · EClinicalMedicine · doi:10.1016/j.eclinm.2026.104143
“24 The total intracranial volume (ICV) was calculated as the sum of GM, WM, and cerebrospinal fluid volumes.”
Self-citationLe, N. H., Ishrat, S., Yaghooti, B., Murad, T., Sapkota, R., Thakuri, D. S., Wong, D. F., Cruchaga, C., Initiative, A. D. N., & Chand, G. B. (2026) · medRxiv · doi:10.64898/2026.09.02.26361668
“…of interest (ROIs) using the multi-atlas region segmentation using ensembles of registration algorithms and parameters with locally optimal atlas selection (MUSE), 27 a consensus labeling framework that employs a spatially adaptive label fusion strategy across multiple registered brain atlases.”
Robb, W. H., Santner, A. M., Zhang, P., Kim, D. S., Jackson, T. B., Shashikumar, N., Pechman, K. R., Vyas, Y., Liu, D., Davis, L. T., et al. (2026) · Stroke · doi:10.1161/strokeaha.125.054879
Early Life Vascular Risk and Midlife Markers of Brain Aging
Yaghoobi, S., Salboukh, F., Lloyd‐Jones, D. M., Allen, N. B., Jacobs, D. R., Launer, L. J., Yaffe, K. S., Mesulam, M. M., Gorelick, P. B., Bryan, R. N., et al. (2026) · Stroke · doi:10.1161/strokeaha.126.055765
GG-AE: Genetically-Guided Autoencoder for Disentangling Structural Networks of Brain Atrophy
Pian, M. D., Wen, J., & Davatzikos, C. A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38239-9_23
Self-citationIntegration of neuroimaging signatures of aging and Alzheimer's with fluid biomarkers
Gonzales, M. M., Ho, N. H., Wang, D. H., Rashid, T., Brandigampala, S. R., Dong, M., Martinez, D., Charisis, S., Hohman, T. J., Seshadri, S. S., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71889
Self-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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-18, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-18 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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