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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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Deformable medical image registration: a survey
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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-citationMulti-atlas based segmentation of brain images: Atlas selection and its effect on accuracy
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“Techniques such as atlas selection from a larger atlas dataset (Aljabar et al., 2009; Wu et al., 2007; Gousias et al., 2010; Hoang Duc et al., 2013; Sanroma et al., 2014), or local similarity based weighting in label fusion (Artaechevarria et al.”
Common genetic variants influence human subcortical brain structures
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Region of interest analysis for fMRI
Poldrack, R. A. (2007) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsm006 · Cited by 814 · cited 1×
“Automated segmentation of anatomical structures, i.e. delineation of regions of interest (ROIs), on MR images is an extremely important task for quantitative analysis of structural and functional brain changes, particularly in studies with large datasets (Good et al., 2002; Poldrack, 2007).”
Multi-Atlas Segmentation with Joint Label Fusion
Wang, H., Suh, J. W., Das, S. R., Pluta, J. B., Craige, C., & Yushkevich, P. A. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.143 · Cited by 796 · cited 4×
“, 2014), or local similarity based weighting in label fusion (Artaechevarria et al., 2009; Isgum et al., 2009; Khan et al., 2011; Wang et al., 2012) were proposed to address these challenges, by selecting, either locally or globally, warps most similar to the target image.”
InfluentialPatch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation
Coupé, P., Manjón, J. V., Fonov, V., Pruessner, J. C., Robles, M., & Collins, D. L. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.018 · Cited by 704 · cited 2×
“With the exception of patch-based approaches that use affine registration to align atlas images (Coupé et al., 2011; Konukoglu et al., 2013), deformable image registration (Sotiras et al.”
MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration
Heinrich, M. P., Jenkinson, M., Bhushan, M., Matin, T., Gleeson, F. V., Brady, M., & Schnabel, J. A. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.05.008 · Cited by 703 · cited 2×
“…(Wang et al., 2012) (PICSL), a levelset-based label fusion and correction method (SBIA-LevelSet), a new label fusion method that uses a modality independent neighbourhood descriptor (Heinrich et al., 2012) (deedsMIND), and a label propagation method using random forests (Zikic et al., 2014)(MSRC).”
Marcus, D. S., Fotenos, A. F., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2009) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2009.21407 · Cited by 669 · cited 6×
“As part of the MICCAI 2013 segmentation challenge, a dataset with training and testing images from OASIS project with reference label maps was created for evaluating multi-atlas label fusion algorithms in segmentation of mid-brain structures.”
InfluentialMulti-atlas segmentation of biomedical images: A survey
Iglesias, J. E., & Sabuncu, M. R. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.06.012 · Cited by 667 · cited 2×
“During thepast 5 years,multi-atlas segmentation (MAS)has increasingly gained attention as a potential solution to this problem (Iglesias and Sabuncu, 2015).”
One-year Age Changes in MRI Brain Volumes in Older Adults
Resnick, S. M. (2000) · Cerebral Cortex · doi:10.1093/cercor/10.5.464 · Cited by 517 · 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-citationCombination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data
Artaechevarria, X., Munoz-Barrutia, A., & Ortíz-de-Solórzano, C. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2014372 · Cited by 510 · cited 2×
“, 2014), or local similarity based weighting in label fusion (Artaechevarria et al., 2009; Isgum et al., 2009; Khan et al., 2011; Wang et al., 2013a) were proposed to address these challenges, by selecting, either locally or globally, warps most similar to the target image.”
Neuroimaging of the Philadelphia Neurodevelopmental Cohort
Satterthwaite, T. D., Elliott, M. A., Ruparel, K., Loughead, J., Prabhakaran, K., Calkins, M. E., Hopson, R., Jackson, C., Keefe, J., Riley, M., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.064 · Cited by 504 · cited 3×
“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-citationA Generative Model for Image Segmentation Based on Label Fusion
Sabuncu, M. R., Yeo, B. T. T., Leemput, K. V., Fischl, B. R., & Golland, P. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2050897 · Cited by 486 · cited 1×
“…with particular emphasis on atlas selection and robust and accurate fusion of the warped atlas labels (Aljabar et al., 2009; Lötjönen et al., 2010; Sabuncu et al., 2010; Landman et al., 2011; Leung et al., 2011; Asman and Landman, 2013; Cardoso et al., 2013; Zikic et al., 2014; Wu et al., 2015).”
Laakso, M. P., Partanen, K. P. L., Riekkinen, P., Lehtovirta, M., Helkala, E.-L., Hallikainen, M., Hänninen, T., Vainio, P. A., & Soininen, H. S. (1996) · Neurology · doi:10.1212/wnl.46.3.678 · Cited by 433 · cited 2×
“…structures, such as hippocampus, thalamus and caudate, which have been previously shown to be associated with various neurodegenerative diseases (Laakso et al., 1996; Konick and Friedman, 2001; Levitt et al., 2002), and for which accurate segmentation is very important for the quantification of…”
Good, C. D., Scahill, R. I., Fox, N. C., Ashburner, J., Friston, K. J., Chan, D., Crum, W. R., Rossor, M. N., & Frackowiak, R. S. J. (2002) · NeuroImage · doi:10.1006/nimg.2002.1202 · Cited by 415 · cited 2×
“delineation of regions of interest (ROIs), onMR images is an extremely important task for quantitative analysis of structural and functional brain changes, particularly in studies with large datasets (Good et al., 2002; Poldrack, 2007).”
Fast and robust multi-atlas segmentation of brain magnetic resonance images
Lötjönen, J. M. P., Wolz, R., Koikkalainen, J. R., Thurfjell, L., Waldemar, G., Soininen, H. S., & Rueckert, D. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.026 · Cited by 401 · cited 1×
“…of the MAS framework, with particular emphasis on atlas selection and robust and accurate fusion of the warped atlas labels (Aljabar et al., 2009; Lötjönen et al., 2010; Sabuncu et al., 2010; Landman et al., 2011; Leung et al., 2011; Asman and Landman, 2013; Cardoso et al., 2013; Zikic et al.,…”
DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting
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InfluentialSelf-citationPipitone, J., Park, M. T. M., Winterburn, J. L., Lett, T. A. P., Lerch, J. P., Pruessner, J. C., Lepage, M., Voineskos, A. N., & Chakravarty, M. M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.054 · Cited by 362 · cited 1×
“…in Wang et al. (2013) multiple warps from the same atlas were generated by composing inter-atlas registrations and atlas-target registrations; in Pipitone et al. (2014) segmentations from a small number of atlases were propagated to a subset of target images and the new atlases were used for…”
Išgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, M. A., & van Ginneken, B. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.2011480 · Cited by 361 · cited 2×
“, 2014), or local similarity based weighting in label fusion (Artaechevarria et al., 2009; Isgum et al., 2009; Khan et al., 2011; Wang et al., 2013a) were proposed to address these challenges, by selecting, either locally or globally, warps most similar to the target image.”
A new improved version of the realistic digital brain phantom
Aubert-Broche, B., Evans, A. C., & Collins, D. L. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.03.052 · Cited by 278 · cited 3×
“An example segmentation on one of the BrainWeb subjects that highlights the improvements as a result of ensemble construction and the proposed label fusion strategy are shown in figure 4.”
Brain maturation: Predicting individual BrainAGE in children and adolescents using structural MRI
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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).”
Cardoso, M. J., Leung, K. K., Marc Modat, A., Shiva Keihaninejad, A., Cash, D. M., Barnes, J., Fox, N. C., & Sebastien Ourselin, A. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.02.006 · Cited by 246 · cited 1×
“…with particular emphasis on atlas selection and robust and accurate fusion of the warped atlas labels (Aljabar et al., 2009; Lötjönen et al., 2010; Sabuncu et al., 2010; Landman et al., 2011; Leung et al., 2011; Asman and Landman, 2013; Cardoso et al., 2013; Zikic et al., 2014; Wu et al., 2015).”
Imaging Patterns of Brain Development and their Relationship to Cognition
Erus, G., Battapady, H., Satterthwaite, T. D., Hakonarson, H., Gur, R. E., Davatzikos, C., & Gur, R. C. (2015) · Cerebral Cortex · doi:10.1093/cercor/bht425 · Cited by 226 · 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).”
Self-citationNon-local statistical label fusion for multi-atlas segmentation
Asman, A. J., & Landman, B. A. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.10.002 · Cited by 222 · cited 1×
“…with particular emphasis on atlas selection and robust and accurate fusion of the warped atlas labels (Aljabar et al., 2009; Lötjönen et al., 2010; Sabuncu et al., 2010; Landman et al., 2011; Leung et al., 2011; Asman and Landman, 2013; Cardoso et al., 2013; Zikic et al., 2014; Wu et al., 2015).”
Meta-analysis of thalamic size in schizophrenia
Konick, L. C., & Friedman, L. (2001) · Biological Psychiatry · doi:10.1016/s0006-3223(00)00974-4 · Cited by 210 · cited 2×
“MUSE achieved consistently highDice scores for the segmentation of important deep brain structures, such as hippocampus, thalamus and caudate, which have been previously shown to be associated with various neurodegenerative diseases (Laakso et al., 1996; Konick and Friedman, 2001; Levitt et al., 2002), and for which a…”
Brain MAPS: An automated, accurate and robust brain extraction technique using a template library
Leung, K. K., Barnes, J., Marc Modat, A., Ridgway, G. R., Bartlett, J. W., Fox, N. C., & Sebastien Ourselin, A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.067 · Cited by 181 · cited 1×
“…with particular emphasis on atlas selection and robust and accurate fusion of the warped atlas labels (Aljabar et al., 2009; Lötjönen et al., 2010; Sabuncu et al., 2010; Landman et al., 2011; Leung et al., 2011; Asman and Landman, 2013; Cardoso et al., 2013; Zikic et al., 2014; Wu et al., 2015).”
Ou, Y., Akbari, H., Bilello, M., Da, X., & Davatzikos, C. A. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2330355 · Cited by 171 · cited 8×
“We further included g = 0.1 for DRAMMS and s = 0.25 for ANTs, which means smaller weights for smoothness.”
InfluentialSelf-citationIntroduction to the Non-rigid Image Registration Evaluation Project (NIREP)
Christensen, G. E., Geng, X., Kuhl, J. G., Bruss, J., Grabowski, T. J., Pirwani, I. A., Vannier, M. W., Allen, J. S., & Damásio, H. (2006) · Lecture notes in computer science · doi:10.1007/11784012_16 · Cited by 161 · cited 4×
“The additional boundary modulation term results in higher segmentation accuracy for all datasets, except NIREP.”
InfluentialLevitt, J. J., McCarley, R. W., Dickey, C. C., Voglmaier, M. M., Niznikiewicz, M. A., Seidman, L. J., Hirayasu, Y., Ciszewski, A. A., Kikinis, R., Jolesz, F. A., et al. (2002) · American Journal of Psychiatry · doi:10.1176/appi.ajp.159.7.1190 · Cited by 144 · cited 2×
“MUSE achieved consistently highDice scores for the segmentation of important deep brain structures, such as hippocampus, thalamus and caudate, which have been previously shown to be associated with various neurodegenerative diseases (Laakso et al., 1996; Konick and Friedman, 2001; Levitt et al., 2002), and for which a…”
Optimum template selection for atlas-based segmentation
Wu, M., Rosano, C., López-García, P., et al. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.07.050 · Cited by 137 · cited 2×
“Techniques such as atlas selection from a larger atlas dataset (Aljabar et al., 2009; Wu et al., 2007; Gousias et al., 2010; Hoang Duc et al., 2013; Sanroma et al., 2014), or local similarity based weighting in label fusion (Artaechevarria et al.”
Whole-genome analyses of whole-brain data: working within an expanded search space
Medland, S. E., Jahanshad, N., Neale, B. M., & Thompson, P. M. (2014) · Nature Neuroscience · doi:10.1038/nn.3718 · Cited by 119 · cited 2×
“The rapidly increasing amount of imaging data creates an urgent need for accurate and consistent phenotyping of brain structures in tens of thousands of images acquired from multiple institutions, and of subjects in various age groups (Medland et al., 2014; Hibar et al., 2015).”
Guorong Wu, A., Kim, M., Sanromà, G., Wang, Q., Munsell, B. C., & Shen, D. G. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.11.025 · Cited by 94 · cited 1×
“…with particular emphasis on atlas selection and robust and accurate fusion of the warped atlas labels (Aljabar et al., 2009; Lötjönen et al., 2010; Sabuncu et al., 2010; Landman et al., 2011; Leung et al., 2011; Asman and Landman, 2013; Cardoso et al., 2013; Zikic et al., 2014; Wu et al., 2015).”
Encoding atlases by randomized classification forests for efficient multi-atlas label propagation
Zikic, D., Glocker, B., & Criminisi, A. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.06.010 · Cited by 81 · cited 3×
“…(Wang et al., 2012) (PICSL), a levelset-based label fusion and correction method (SBIA-LevelSet), a new label fusion method that uses a modality independent neighbourhood descriptor (Heinrich et al., 2012) (deedsMIND), and a label propagation method using random forests (Zikic et al., 2014)(MSRC).”
Multi-classifier framework for atlas-based image segmentation
Rohlfing, T., & Maurer, C. R. (2005) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.03.017 · Cited by 80 · cited 2×
“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.”
KHAN, A. R., Cherbuin, N., Wen, W. X., Anstey, K. J., Sachdev, P. S., & Beg, M. F. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.078 · Cited by 67 · cited 2×
“…Hoang Duc et al., 2013; Sanroma et al., 2014), or local similarity based weighting in label fusion (Artaechevarria et al., 2009; Isgum et al., 2009; Khan et al., 2011; Wang et al., 2012) were proposed to address these challenges, by selecting, either locally or globally, warps most similar to the…”
Bai, J., Trinh, T. L. H., Chuang, K.-H., & Qiu, A. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.02.010 · Cited by 64 · cited 1×
“In Bai et al. (2012), 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…”
Neighbourhood approximation using randomized forests
Konukoğlu, E., Glocker, B., Zikic, D., & Criminisi, A. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.04.013 · Cited by 63 · cited 2×
“With the exception of patch-based approaches that use affine registration to align atlas images (Coupé et al., 2011; Konukoglu et al., 2013), deformable image registration (Sotiras et al.”
Learning to Rank Atlases for Multiple-Atlas Segmentation
Sanromà, G., Guorong Wu, A., Gao, Y., et al. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2327516 · Cited by 58 · cited 2×
“Techniques such as atlas selection from a larger atlas dataset (Aljabar et al., 2009; Wu et al., 2007; Gousias et al., 2010; Hoang Duc et al., 2013; Sanroma et al., 2014), or local similarity based weighting in label fusion (Artaechevarria et al.”
Multi-atlas Segmentation with Robust Label Transfer and Label Fusion
Wang, H., Pouch, A. M., Takabe, M., Jackson, B. M., Gorman, J., Gorman, R. C., & Yushkevich, P. A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38868-2_46 · Cited by 43 · cited 1×
“The general concept of generating a larger ensemble of label maps was explored in a few recent papers: in Wang et al. (2013) multiple warps from the same atlas were generated by composing inter-atlas registrations and atlas-target registrations; in Pipitone et al. (2014) segmentations from a small…”
Robust Statistical Fusion of Image Labels
Landman, B. A., Asman, A. J., Scoggins, A. G., Bogovic, J., Xing, F., & Prince, J. L. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2172215 · Cited by 42 · cited 1×
“…with particular emphasis on atlas selection and robust and accurate fusion of the warped atlas labels (Aljabar et al., 2009; Lötjönen et al., 2010; Sabuncu et al., 2010; Landman et al., 2011; Leung et al., 2011; Asman and Landman, 2013; Cardoso et al., 2013; Zikic et al., 2014; Wu et al., 2015).”
Using Manifold Learning for Atlas Selection in Multi-Atlas Segmentation
Duc, A. K. H., Marc Modat, A., Leung, K. K., Cardoso, M. J., Barnes, J., Kadir, T., Sebastien Ourselin, A., & Initiative, A. D. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0070059 · Cited by 41 · cited 2×
“Techniques such as atlas selection from a larger atlas dataset (Aljabar et al., 2009; Wu et al., 2007; Gousias et al., 2010; Hoang Duc et al., 2013; Sanroma et al., 2014), or local similarity based weighting in label fusion (Artaechevarria et al., 2009; Isgum et al., 2009; Khan et al., 2011; Wang…”
Baloch, S., & Davatzikos, C. A. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.10.048 · Cited by 32 · cited 1×
“Inspired by the manifold representation that was introduced in Baloch and Davatzikos (2008), we define the anatomic equivalence class of S as a set of all possible ways of representing the morphology of that individual via a transformation of an atlas and a respective residual, obtained by varying…”
Self-citationWolz, R., Aljabar, P., Rueckert, D., Heckemann, R. A., & Hammers, A. (2009) · 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro · doi:10.1109/isbi.2009.5193086 · Cited by 28 · cited 1×
“It's important to note that in contrast to methods where the intensity model is estimated from the atlas images for which the segmentation labels are known, we estimate the intensity distribution of ROIs from the target image, similar to Wolz et al. (2009).”
Atlas selection strategy for automatic segmentation of pediatric brain MRIs into 83 ROIs
Gousias, I. S., Hammers, A., Heckemann, R. A., et al. (2010) · IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist.2010.5548493 · Cited by 16 · cited 2×
“Techniques such as atlas selection from a larger atlas dataset (Aljabar et al., 2009; Wu et al., 2007; Gousias et al., 2010; Hoang Duc et al., 2013; Sanroma et al., 2014), or local similarity based weighting in label fusion (Artaechevarria et al.”
Hartung, E. A., Laney, N. C., Kim, J. Y., Ruebner, R. L., Detre, J. A., Liu, H.-S., Davatzikos, C. A., Erus, G., Doshi, J. J., Schultz, R. T., et al. (2015) · BMC Nephrology · doi:10.1186/s12882-015-0061-1 · Cited by 16 · 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-citationDavatzikos, C. A., & Lian, N.-X. (2009) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2009.5193179 · Cited by 3
Self-citation
Cited by
Sorted by citations
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049
Self-citationLaMontagne, P. J., Benzinger, T. L., Morris, J. C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E., Hassenstab, J., Moulder, K., Vlassenko, A. G., et al. (2019) · medRxiv · doi:10.1101/2019.12.13.19014902 · Cited by 779
“…from these projects have been extensively used in the development of neuroimaging tools, such as brain atlases 3,4 , tissue segmentation algorithms 5 , and de-identification tools 6 , used to create models of healthy aging 7,8 and AD timecourse 7–11 , used to answer hypothesis driven questions…”
Pomponio, R., Erus, G., Habes, M., Doshi, J. J., Srinivasan, D., Mamourian, E., Bashyam, V., Nasrallah, I. M., Satterthwaite, T. D., Yong Fan, A., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116450 · Cited by 561
Self-citationAssociation of Intensive vs Standard Blood Pressure Control With Cerebral White Matter Lesions
Nasrallah, I. M., Pajewski, N. M., Auchus, A. P., Chelune, G. J., Cheung, A. K., Cleveland, M. L., Coker, L. H., Crowe, M. G., Cushman, W. C., Cutler, J. A., et al. (2019) · JAMA · doi:10.1001/jama.2019.10551 · Cited by 433
Self-citationOrr, M., Gonzales, M., Garbarino, V., Kautz, T., Palavicini, J., Lopez-Cruzan, M., Dehkordi, S. K., Mathews, J., Zare, H., Xu, P., et al. (2023) · doi:10.21203/rs.3.rs-2809973/v1 · Cited by 319
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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