2019
Chauvin, L.; Kumar, K.; Wachinger, C.; Vangel, M.; de Guise, J.; Desrosiers, C.; Wells, W.; Toews, M.
Neuroimage signature from salient keypoints is highly specific to individuals and shared by close relatives Journal Article
In: NeuroImage, 2019.
@article{Chauvin2019,
title = {Neuroimage signature from salient keypoints is highly specific to individuals and shared by close relatives},
author = {L. Chauvin and K. Kumar and C. Wachinger and M. Vangel and J. de Guise and C. Desrosiers and W. Wells and M. Toews},
url = {http://www.matthewtoews.com/papers/Chauvin_Neuroimage2020.pdf},
year = {2019},
date = {2019-11-01},
urldate = {2019-11-01},
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Roy, Abhijit Guha; Siddiqui, Shayan; Pölsterl, Sebastian; Navab, Nassir; Wachinger, Christian
'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images Journal Article
In: Medical Image Analysis, 2019.
@article{few_shot_agr_2019,
title = {'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images},
author = {Abhijit Guha Roy and Shayan Siddiqui and Sebastian Pölsterl and Nassir Navab and Christian Wachinger},
url = {https://arxiv.org/abs/1902.01314},
year = {2019},
date = {2019-10-11},
urldate = {2019-10-11},
journal = {Medical Image Analysis},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pölsterl, Sebastian; Gutiérrez-Becker, Benjamı́n; Sarasua, Ignacio; Roy, Abhijit Guha; Wachinger, Christian
An AutoML Approach for the Prediction of Fluid Intelligence from MRI-Derived Features Proceedings Article
In: Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge (ABCD-NP-Challenge), 2019.
@inproceedings{Poelsterl2019-ABCDb,
title = {An AutoML Approach for the Prediction of Fluid Intelligence from MRI-Derived Features},
author = {Sebastian Pölsterl and Benjamı́n Gutiérrez-Becker and Ignacio Sarasua and Abhijit Guha Roy and Christian Wachinger},
url = {http://ai-med.de/wp-content/uploads/2020/02/ABCD-challenge-18-AutoML.pdf},
year = {2019},
date = {2019-10-10},
urldate = {2019-10-10},
booktitle = {Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge (ABCD-NP-Challenge)},
abstract = {We propose an AutoML approach for the prediction of fluid intelligence from T1-weighted magnetic resonance images. We extracted 122 features from MRI scans and employed Sequential Model-based Algorithm Configuration to search for the best prediction pipeline, including the best data pre-processing and regression model. In total, we evaluated over 2600 prediction pipelines. We studied our final model by employing results from game theory in the form of Shapley values. Results indicate that predicting fluid intelligence from volume measurements is a challenging task with many challenges. We found that our final ensemble of 50 prediction pipelines associated larger parahippocampal gyrus volumes with lower fluid intelligence, and higher pons white matter volume with higher fluid intelligence.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Pölsterl, Sebastian; Gutiérrez-Becker, Benjamı́n; Sarasua, Ignacio; Roy, Abhijit Guha; Wachinger, Christian
Prediction of Fluid Intelligence from T1-Weighted Magnetic Resonance Images Proceedings Article
In: Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge (ABCD-NP-Challenge), 2019.
@inproceedings{Poelsterl2019-ABCDa,
title = {Prediction of Fluid Intelligence from T1-Weighted Magnetic Resonance Images},
author = {Sebastian Pölsterl and Benjamı́n Gutiérrez-Becker and Ignacio Sarasua and Abhijit Guha Roy and Christian Wachinger},
url = {http://ai-med.de/wp-content/uploads/2020/02/ABCD-challenge-gboost.pdf},
year = {2019},
date = {2019-10-10},
urldate = {2019-10-10},
booktitle = {Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge (ABCD-NP-Challenge)},
abstract = {We study predicting fluid intelligence of 9–10 year old children from T1-weighted magnetic resonance images. We extract volume and thickness measurements from MRI scans using FreeSurfer and the SRI24 atlas. We empirically compare two predictive models: (i) an ensemble of gradient boosted trees and (ii) a linear ridge regression model. For both, a Bayesian black-box optimizer for finding the best suitable prediction model is used. To systematically analyze feature importance our model, we employ results from game theory in the form of Shapley values. Our model with gradient boosting and FreeSurfer measures ranked third place among 24 submissions to the ABCD Neurocognitive Prediction Challenge. Our results on feature importance could be used to guide future research on the neurobiological mechanisms behind fluid intelligence in children.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Pölsterl, Sebastian; Sarasua, Ignacio; Gutiérrez-Becker, Benjamín; Wachinger, Christian
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data Proceedings Article
In: Data and Machine Learning Advances with Multiple Views Workshop, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2019.
@inproceedings{Poelsterl2019-WideAndDeep,
title = {A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data},
author = {Sebastian Pölsterl and Ignacio Sarasua and Benjamín Gutiérrez-Becker and Christian Wachinger},
url = {http://ai-med.de/wp-content/uploads/2020/02/wide-and-deep-survival-analysis.pdf},
year = {2019},
date = {2019-09-09},
urldate = {2019-09-09},
booktitle = {Data and Machine Learning Advances with Multiple Views Workshop, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)},
abstract = {We introduce a wide and deep neural network for prediction of progression from patients with mild cognitive impairment to Alzheimer's disease. Information from anatomical shape and tabular clinical data (demographics, biomarkers) are fused in a single neural network. The network is invariant to shape transformations and avoids the need to identify point correspondences between shapes. To account for right censored time-to-event data, i.e., when it is only known that a patient did not develop Alzheimer's disease up to a particular time point, we employ a loss commonly used in survival analysis. Our network is trained end-to-end to combine information from a patient's hippocampus shape and clinical biomarkers. Our experiments on data from the Alzheimer's Disease Neuroimaging Initiative demonstrate that our proposed model is able to learn a shape descriptor that augments clinical biomarkers and outperforms a deep neural network on shape alone and a linear model on common clinical biomarkers.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rickmann, Anne-Marie; Roy, Abhijit Guha; Sarasua, Ignacio; Navab, Nassir; Wachinger, Christian
'Project & Excite' Modules for Segmentation of Volumetric Medical Scans Proceedings Article
In: Medical Image Computing and Computer Aided Intervention, Springer, 2019.
@inproceedings{rickmann2019,
title = {'Project & Excite' Modules for Segmentation of Volumetric Medical Scans},
author = {Anne-Marie Rickmann and Abhijit Guha Roy and Ignacio Sarasua and Nassir Navab and Christian Wachinger},
url = {https://arxiv.org/abs/1906.04649},
year = {2019},
date = {2019-07-15},
urldate = {2019-07-15},
booktitle = {Medical Image Computing and Computer Aided Intervention},
publisher = {Springer},
abstract = {Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging. Recently, squeeze and excitation (SE) modules and variations thereof have been introduced to recalibrate feature maps channel- and spatial-wise, which can boost performance while only minimally increasing model complexity. So far, the development of SE has focused on 2D images. In this paper, we propose 'Project & Excite' (PE) modules that base upon the ideas of SE and extend them to operating on 3D volumetric images. 'Project & Excite' does not perform global average pooling, but squeezes feature maps along different slices of a tensor separately to retain more spatial information that is subsequently used in the excitation step. We demonstrate that PE modules can be easily integrated in 3D U-Net, boosting performance by 5% Dice points, while only increasing the model complexity by 2%. We evaluate the PE module on two challenging tasks, whole-brain segmentation of MRI scans and whole-body segmentation of CT scans.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Wachinger, Christian; Becker, Benjamin Gutierrez; Rieckmann, Anna; Pölsterl, Sebastian
Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference Book Section
In: Medical Image Computing and Computer Aided Intervention, 2019.
@incollection{wachinger2019causal,
title = {Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference},
author = {Christian Wachinger and Benjamin Gutierrez Becker and Anna Rieckmann and Sebastian Pölsterl},
url = {https://arxiv.org/abs/1907.04102},
year = {2019},
date = {2019-07-15},
urldate = {2019-07-15},
booktitle = {Medical Image Computing and Computer Aided Intervention},
abstract = {Neuroimaging datasets keep growing in size to address increasingly complex medical questions. However, even the largest datasets today alone are too small for training complex machine learning models. A potential solution is to increase sample size by pooling scans from several datasets. In this work, we combine 12,207 MRI scans from 15 studies and show that simple pooling is often ill-advised due to introducing various types of biases in the training data. First, we systematically define these biases. Second, we detect bias by experimentally showing that scans can be correctly assigned to their respective dataset with 73.3% accuracy. Finally, we propose to tell causal from confounding factors by quantifying the extent of confounding and causality in a single dataset using causal inference. We achieve this by finding the simplest graphical model in terms of Kolmogorov complexity. As Kolmogorov complexity is not directly computable, we employ the minimum description length to approximate it. We empirically show that our approach is able to estimate plausible causal relationships from real neuroimaging data. },
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Gutiérrez-Becker, Benjamin; Wachinger, Christian
Learning a Conditional Generative Model for Anatomical Shape Analysis Proceedings Article
In: International Conference on Information Processing in Medical Imaging (IPMI), pp. 505–516, Springer 2019.
@inproceedings{gutierrez2019learning,
title = {Learning a Conditional Generative Model for Anatomical Shape Analysis},
author = {Benjamin Gutiérrez-Becker and Christian Wachinger},
url = {http://ai-med.de/wp-content/uploads/2020/02/IPMI_20196.pdf},
year = {2019},
date = {2019-06-06},
urldate = {2019-06-06},
booktitle = {International Conference on Information Processing in Medical Imaging (IPMI)},
pages = {505--516},
organization = {Springer},
keywords = {},
pubstate = {published},
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Paschali, Magdalini; Simson, Walter; Roy, Abhijit Guha; Naeem, Muhammad Ferjad; Göbl, Rüdiger; Wachinger, Christian; Navab, Nassir
Manifold Exploring Data Augmentation with Geometric Transformations for Increased Performance and Robustness Journal Article
In: International Conference on Information Processing in Medical Imaging (IPMI), 2019.
@article{paschali2019data,
title = {Manifold Exploring Data Augmentation with Geometric Transformations for Increased Performance and Robustness},
author = {Magdalini Paschali and Walter Simson and Abhijit Guha Roy and Muhammad Ferjad Naeem and Rüdiger Göbl and Christian Wachinger and Nassir Navab},
url = {https://arxiv.org/abs/1901.04420},
year = {2019},
date = {2019-06-04},
urldate = {2019-06-04},
journal = {International Conference on Information Processing in Medical Imaging (IPMI)},
keywords = {},
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Sailesh Conjeti Abhijit Guha Roy, Nassir Navab
Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control Journal Article
In: NeuroImage, vol. 195, 2019.
@article{bayquicknat2019,
title = {Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control},
author = {Abhijit Guha Roy, Sailesh Conjeti, Nassir Navab, Christian Wachinger},
url = {https://www.sciencedirect.com/science/article/pii/S1053811919302319},
doi = {https://doi.org/10.1016/j.neuroimage.2019.03.042},
year = {2019},
date = {2019-04-01},
urldate = {2019-04-01},
journal = {NeuroImage},
volume = {195},
abstract = {We introduce Bayesian QuickNAT for the automated quality control of whole-brain segmentation on MRI T1 scans. Next to the Bayesian fully convolutional neural network, we also present inherent measures of segmentation uncertainty that allow for quality control per brain structure. For estimating model uncertainty, we follow a Bayesian approach, wherein, Monte Carlo (MC) samples from the posterior distribution are generated by keeping the dropout layers active at test time. Entropy over the MC samples provides a voxel-wise model uncertainty map, whereas expectation over the MC predictions provides the final segmentation. Next to voxel-wise uncertainty, we introduce four metrics to quantify structure-wise uncertainty in segmentation for quality control. We report experiments on four out-of-sample datasets comprising of diverse age range, pathology and imaging artifacts. The proposed structure-wise uncertainty metrics are highly correlated with the Dice score estimated with manual annotation and therefore present an inherent measure of segmentation quality. In particular, the intersection over union over all the MC samples is a suitable proxy for the Dice score. In addition to quality control at scan-level, we propose to incorporate the structure-wise uncertainty as a measure of confidence to do reliable group analysis on large data repositories. We envisage that the introduced uncertainty metrics would help assess the fidelity of automated deep learning based segmentation methods for large-scale population studies, as they enable automated quality control and group analyses in processing large data repositories.},
keywords = {},
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tppubtype = {article}
}
Roy, Abhijit Guha; Siddiqui, Shayan; Pölsterl, Sebastian; Navab, Nassir; Wachinger, Christian
Braintorrent: A peer-to-peer environment for decentralized federated learning Journal Article
In: arXiv preprint arXiv:1905.06731, 2019.
@article{roy2019braintorrent,
title = {Braintorrent: A peer-to-peer environment for decentralized federated learning},
author = {Abhijit Guha Roy and Shayan Siddiqui and Sebastian Pölsterl and Nassir Navab and Christian Wachinger},
url = {https://arxiv.org/abs/1905.06731},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {arXiv preprint arXiv:1905.06731},
keywords = {},
pubstate = {published},
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}
2018
Roy, Abhijit Guha; Conjeti, Sailesh; Navab, Nassir; Wachinger, Christian
QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy Journal Article
In: NeuroImage, 2018.
@article{quicknat_neuroimage_agr,
title = {QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy},
author = {Abhijit Guha Roy and Sailesh Conjeti and Nassir Navab and Christian Wachinger},
url = {https://arxiv.org/abs/1801.04161},
year = {2018},
date = {2018-11-23},
urldate = {2018-11-23},
journal = {NeuroImage},
keywords = {},
pubstate = {published},
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Reuter, Martin; Wachinger, Christian; Lombaert, Hervé; Paniagua, Beatriz; Lüthi, Marcel; Egger, Bernhard
Shape in Medical Imaging: International Workshop, ShapeMI 2018 Book
Granada, Spain, Springer, 2018.
@book{reuter2018shape,
title = {Shape in Medical Imaging: International Workshop, ShapeMI 2018},
author = {Martin Reuter and Christian Wachinger and Hervé Lombaert and Beatriz Paniagua and Marcel Lüthi and Bernhard Egger},
year = {2018},
date = {2018-11-05},
urldate = {2018-11-05},
volume = {11167},
publisher = {Granada, Spain, Springer},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
Wachinger, C.; Toews, M.; Langs, G.; Wells, W.; Golland, P.
Keypoint Transfer for Fast Whole-Body Segmentation Journal Article
In: IEEE Transactions on Medical Imaging, 2018.
@article{wachinger_2018_tmi,
title = {Keypoint Transfer for Fast Whole-Body Segmentation},
author = {C. Wachinger and M. Toews and G. Langs and W. Wells and P. Golland},
url = {http://ai-med.de/wp-content/uploads/2020/02/1806.087231.pdf},
year = {2018},
date = {2018-09-13},
urldate = {2018-09-13},
journal = {IEEE Transactions on Medical Imaging},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gutiérrez-Becker, Benjamin; Gatidis, Sergios; Gutmann, Daniel; Peters, Annette; Schlett, Christopher; Bamberg, Fabian; Wachinger, Christian
Deep Shape Analysis on Abdominal Organs for Diabetes Prediction Proceedings Article
In: International Workshop on Shape in Medical Imaging, pp. 223–231, Springer 2018.
@inproceedings{gutierrez2018deep,
title = {Deep Shape Analysis on Abdominal Organs for Diabetes Prediction},
author = {Benjamin Gutiérrez-Becker and Sergios Gatidis and Daniel Gutmann and Annette Peters and Christopher Schlett and Fabian Bamberg and Christian Wachinger},
url = {https://arxiv.org/abs/1808.01946},
year = {2018},
date = {2018-09-05},
urldate = {2018-09-05},
booktitle = {International Workshop on Shape in Medical Imaging},
pages = {223--231},
organization = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Roy, Abhijit Guha; Navab, Nassir; Wachinger, Christian
Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks Journal Article
In: IEEE Transactions on Medical Imaging, 2018.
@article{se_tmi_agr2018,
title = {Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks},
author = {Abhijit Guha Roy and Nassir Navab and Christian Wachinger},
url = {https://arxiv.org/abs/1808.08127},
year = {2018},
date = {2018-08-17},
urldate = {2018-08-17},
journal = {IEEE Transactions on Medical Imaging},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gutierrez-Becker, Benjamin; Klein, Tassilo; Wachinger, Christian
Deep Multi-Structural Shape Analysis: Application to Neuroanatomy Conference Forthcoming
MICCAI 2018, Forthcoming.
@conference{Gutierrez-Becker2018,
title = {Deep Multi-Structural Shape Analysis: Application to Neuroanatomy},
author = {Benjamin Gutierrez-Becker and Tassilo Klein and Christian Wachinger },
url = {https://arxiv.org/abs/1806.01069},
year = {2018},
date = {2018-05-28},
booktitle = {MICCAI 2018},
abstract = {Shape representations are a powerful tool in medical image analysis for its ability to quantify morphological differences. Current approaches are mostly based on hand-engineered representations which are potentially not optimal for specific tasks. In this work we propose the use of a neural network operating directly on point cloud representations to perform shape analysis. Our method is able to perform a joint shape analysis of different structures and since it operates directly on raw point cloud representations does not require feature engineering, image registration, mesh calculations or point correspondences. We showcase the advantages of our method by performing prediction based on the analysis of shape of different brain subcortical structures to perform Alzheimer's disease and Mild Cognitive Impairment classification as well as age regression. },
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
Roy, Abhijit Guha; Navab, Nassir; Wachinger, Christian
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks Conference Forthcoming
Accepted at MICCAI 2018, Forthcoming.
@conference{spatial_SE_roy_2018,
title = {Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks},
author = {Abhijit Guha Roy and Nassir Navab and Christian Wachinger},
url = {https://arxiv.org/pdf/1803.02579.pdf},
year = {2018},
date = {2018-05-28},
urldate = {2018-05-28},
booktitle = {Accepted at MICCAI 2018},
abstract = {Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in image segmentation for a plethora of applications. Architectural
innovations within F-CNNs have mainly focused on improving spatial encoding or network connectivity to aid gradient flow. In this paper,
we explore an alternate direction of recalibrating the feature maps adaptively, to boost meaningful features, while suppressing weak ones.
We draw inspiration from the recently proposed squeeze & excitation (SE) module for channel recalibration of feature maps for image classification. Towards this end, we introduce three variants of SE modules for image segmentation, (i) squeezing spatially and exciting channel-wise (cSE), (ii) squeezing channel-wise and exciting spatially (sSE) and (iii) concurrent spatial and channel squeeze & excitation (scSE). We effectively incorporate these SE modules within three different state-of-theart F-CNNs (DenseNet, SD-Net, U-Net) and observe consistent improvement of performance across all architectures, while minimally effecting model complexity. Evaluations are performed on two challenging applications: whole brain segmentation on MRI scans (Multi-Atlas Labelling Challenge Dataset) and organ segmentation on whole body contrast enhanced CT scans (Visceral Dataset).},
keywords = {},
pubstate = {forthcoming},
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}
innovations within F-CNNs have mainly focused on improving spatial encoding or network connectivity to aid gradient flow. In this paper,
we explore an alternate direction of recalibrating the feature maps adaptively, to boost meaningful features, while suppressing weak ones.
We draw inspiration from the recently proposed squeeze & excitation (SE) module for channel recalibration of feature maps for image classification. Towards this end, we introduce three variants of SE modules for image segmentation, (i) squeezing spatially and exciting channel-wise (cSE), (ii) squeezing channel-wise and exciting spatially (sSE) and (iii) concurrent spatial and channel squeeze & excitation (scSE). We effectively incorporate these SE modules within three different state-of-theart F-CNNs (DenseNet, SD-Net, U-Net) and observe consistent improvement of performance across all architectures, while minimally effecting model complexity. Evaluations are performed on two challenging applications: whole brain segmentation on MRI scans (Multi-Atlas Labelling Challenge Dataset) and organ segmentation on whole body contrast enhanced CT scans (Visceral Dataset).
Roy, Abhijit Guha; Conjeti, Sailesh; Navab, Nassir; Wachinger, Christian
Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling Conference Forthcoming
Accepted at MICCAI 2018, Forthcoming.
@conference{qc_brainseg_roy_208,
title = {Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling},
author = {Abhijit Guha Roy and Sailesh Conjeti and Nassir Navab and Christian Wachinger},
url = {https://arxiv.org/pdf/1804.07046.pdf},
year = {2018},
date = {2018-05-28},
urldate = {2018-05-28},
booktitle = {Accepted at MICCAI 2018},
abstract = {We introduce inherent measures for effective quality control of brain segmentation based on a Bayesian fully convolutional neural network, using model uncertainty. Monte Carlo samples from the posterior distribution are efficiently generated using dropout at test time. Based on these samples, we introduce next to a voxel-wise uncertainty map also three metrics for structure-wise uncertainty. We then incorporate these structure-wise uncertainty in group analyses as a measure of confidence in the observation. Our results show that the metrics are highly correlated to segmentation accuracy and therefore present an inherent measure of segmentation quality. Furthermore, group analysis with uncertainty results in effect sizes closer to that of manual annotations. The introduced uncertainty metrics can not only be very useful in translation to clinical practice but also provide automated quality control and group analyses in processing large data repositories.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
Wachinger, Christian; Nho, Kwangsik; Saykin, Andrew J; Reuter, Martin; Rieckmann, Anna
A Longitudinal Imaging Genetics Study of Neuroanatomical Asymmetry in Alzheimer’s Disease Journal Article
In: Biological Psychiatry, 2018.
@article{wachinger2018longitudinal,
title = {A Longitudinal Imaging Genetics Study of Neuroanatomical Asymmetry in Alzheimer’s Disease},
author = {Wachinger, Christian and Nho, Kwangsik and Saykin, Andrew J and Reuter, Martin and Rieckmann, Anna},
url = {http://ai-med.de/wp-content/uploads/2020/02/1-s2.0-S0006322318314720-main1.pdf},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Biological Psychiatry},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gutierrez-Becker, Benjamin; Klein, Tassilo; Wachinger, Christian
Gaussian process uncertainty in age estimation as a measure of brain abnormality Journal Article
In: NeuroImage, 2018.
@article{becker2018gaussian,
title = {Gaussian process uncertainty in age estimation as a measure of brain abnormality},
author = {Gutierrez-Becker, Benjamin and Klein, Tassilo and Wachinger, Christian },
url = {https://arxiv.org/pdf/1804.01296.pdf},
year = {2018},
date = {2018-01-01},
journal = {NeuroImage},
publisher = {Elsevier},
abstract = {Multivariate regression models for age estimation are a powerful tool for assessing abnormal brain morphology associated to neuropathology. Age prediction models are built on cohorts of healthy subjects and are built to reflect normal aging patterns. The application of these multivariate models to diseased subjects usually results in high prediction errors, under the hypothesis that neuropathology presents a similar degenerative pattern as that of accelerated aging. In this work, we propose an alternative to the idea that pathology follows a similar trajectory than normal aging. Instead, we propose the use of metrics which measure deviations from the mean aging trajectory. We propose to measure these deviations using two different metrics: uncertainty in a Gaussian process regression model and a newly proposed age weighted uncertainty measure. Consequently, our approach assumes that pathologic brain patterns are different to those of normal aging. We present results for subjects with autism, mild cognitive impairment and Alzheimer's disease to highlight the versatility of the approach to different diseases and age ranges. We evaluate volume, thickness, and VBM features for quantifying brain morphology. Our evaluations are performed on a large number of images obtained from a variety of publicly available neuroimaging databases. Across all features, our uncertainty based measurements yield a better separation between diseased subjects and healthy individuals than the prediction error. Finally, we illustrate differences in the disease pattern to normal aging, supporting the application of uncertainty as a measure of neuropathology.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Roy, Abhijit Guha; Conjeti, Sailesh; Navab, Nassir; Wachinger, Christian
Fast MRI Whole Brain Segmentation with Fully Convolutional Neural Networks Book Section
In: Bildverarbeitung für die Medizin 2018, pp. 42–42, Springer, 2018.
@incollection{roy2018fast,
title = {Fast MRI Whole Brain Segmentation with Fully Convolutional Neural Networks},
author = {Roy, Abhijit Guha and Conjeti, Sailesh and Navab, Nassir and Wachinger, Christian},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Bildverarbeitung für die Medizin 2018},
pages = {42--42},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
2017
Gutierrez-Becker, Benjamin; Peter, Loic; Klein, Tassilo; Wachinger, Christian
A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data Conference
Proceedings of MICCAI 2017, Elsevier, 2017.
@conference{gutierrez2017bandits,
title = {A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data},
author = {Benjamin Gutierrez-Becker and Loic Peter and Tassilo Klein and Christian Wachinger},
url = {http://ai-med.de/wp-content/uploads/2017/06/MICCAI17-300.pdf},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of MICCAI 2017},
publisher = {Elsevier},
abstract = {With the availability of big medical image data, the selection of an adequate training set is becoming more important to address the heterogeneity of different datasets. Simply including all the data does not only incur high processing costs but can even harm the prediction. We formulate the smart and efficient selection of a training dataset from big medical image data as a multi-armed bandit problem, solved by Thompson sampling. Our method assumes that image features are not available at the time of the selection of the samples, and therefore relies only on meta information associated with the images. Our strategy simultaneously exploits data sources with high chances of yielding useful samples and explores new data regions. For our evaluation, we focus on the application of estimating the age from a brain MRI. Our results on 7,250 subjects from 10 datasets show that our approach leads to higher accuracy while only requiring a fraction of the training data.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Gutierrez-Becker, Benjamin; Mateus, Diana; Peter, Loic; Navab, Nassir
Guiding Multimodal Registration with Learned Optimization Updates Journal Article
In: Medical Image Analysis, 2017.
@article{gutierrez2017guidingb,
title = {Guiding Multimodal Registration with Learned Optimization Updates},
author = {Gutierrez-Becker, Benjamin and Mateus, Diana and Peter, Loic and Navab, Nassir},
url = {http://ai-med.de/wp-content/uploads/2017/06/MultimodalMedIA-1.pdf},
year = {2017},
date = {2017-01-01},
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Riva, Marco; Hennersperger, Christoph; Milletari, Fausto; Katouzian, Amin; Pessina, Federico; Gutierrez-Becker, Benjamin; Castellano, Antonella; Navab, Nassir; Bello, Lorenzo
3D intra-operative ultrasound and MR image guidance: pursuing an ultrasound-based management of brainshift to enhance neuronavigation Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, pp. 1–15, 2017.
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Reuter, Martin; Wachinger, Christian
Abtract: Shape Analysis in Human Brain MRI Book Section
In: Bildverarbeitung für die Medizin 2017, pp. 358–358, Springer Vieweg, Berlin, Heidelberg, 2017.
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Wachinger, Christian; Reuter, Martin; Klein, Tassilo
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy Journal Article
In: NeuroImage, 2017.
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Conjeti, Sailesh; Guha Roy, Abhijit; Katouzian, Amin; Navab, Nassir
Deep Residual Hashing Journal Article
In: Accepted at MICCAI 2017, 2017.
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Guha Roy, Abhijit; Conjeti, Sailesh; Karri, Sri Phani Krishna; Sheet, Debdoot; Katouzian, Amin; Wachinger, Christian; Navab, Nassir
ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network Journal Article
In: Accepted at Biomedical Optics Express, 2017.
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Guha Roy, Abhijit; Conjeti, Sailesh; Sheet, Debdoot; Katouzian, Amin; Navab, Nassir; Wachinger, Christian
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data Journal Article
In: Accepted at MICCAI 2017, 2017.
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Wachinger, Christian; Rieckmann, Anna; Reuter, Martin
Latent Processes Governing Neuroanatomical Change in Aging and Dementia Proceedings Article
In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer 2017.
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Wachinger, Christian; Volkmer, Susanne; Bublath, Katharina; Bruder, Jennifer; Bartling, Jürgen; Schulte-Körne, Gerd
Does the late positive component reflect successful reading acquisition? A longitudinal ERP study Journal Article
In: NeuroImage: Clinical, 2017.
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2016
Dubost, Florian; Peter, Loic; Rupprecht, Christian; Gutierrez-Becker, Benjamin; Navab, Nassir
Hands-Free Segmentation of Medical Volumes via Binary Inputs Proceedings
Springer International Publishing 2016.
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date = {2016-01-01},
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Gutierrez-Becker, Benjamin; Mateus, Diana; Peter, Loic; Navab, Nassir
Learning Optimization Updates for Multimodal Registration Proceedings
Springer 2016.
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Simonovsky, Martin; Gutierrez-Becker, Benjamin; Mateus, Diana; Navab, Nassir; Komodakis, Nikos
A Deep Metric for Multimodal Registration Proceedings
Springer 2016.
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Wachinger, Christian; Reuter, Martin
Domain adaptation for Alzheimer's disease diagnostics Journal Article
In: NeuroImage, 2016.
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Wachinger, C; Brennan, M; Sharp, G; Golland, P
Efficient Descriptor-Based Segmentation of Parotid Glands with Non-Local Means. Journal Article
In: IEEE transactions on bio-medical engineering, 2016.
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Wachinger, Christian; Salat, David H; Weiner, Michael; Reuter, Martin; Initiative, Alzheimer’s Disease Neuroimaging; others,
Whole-brain analysis reveals increased neuroanatomical asymmetries in dementia for hippocampus and amygdala Journal Article
In: Brain, vol. 139, no. 12, pp. 3253–3266, 2016.
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Reuter, Martin; Wachinger, Christian; Lombaert, Herv'e
Spectral and Shape Analysis in Medical Imaging: First International Workshop, SeSAMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Revised Selected Papers Miscellaneous
2016.
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Conjeti, Sailesh; Katouzian, Amin; Guha Roy, Abhijit; Peter, Lo"ic; Sheet, Debdoot; Carlier, St'ephane; Laine, Andrew; Navab, Nassir
Supervised domain adaptation of decision forests: Transfer of models trained in vitro for in vivo intravascular ultrasound tissue characterization Journal Article
In: Medical image analysis, vol. 32, pp. 1–17, 2016.
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Conjeti, S; Guha Roy, A; Sheet, D; Carlier, S; Syeda-Mahmood, T; Navab, N; Katouzian, A
Domain Adapted Model for In Vivo Intravascular Ultrasound Tissue Characterization Journal Article
In: Computing and Visualization for Intravascular Imaging and Computer-Assisted Stenting, pp. 157, 2016.
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2015
Wachinger, Christian; Golland, Polina; Magnain, Caroline; Fischl, Bruce; Reuter, Martin
Multi-modal robust inverse-consistent linear registration Journal Article
In: Human brain mapping, vol. 36, no. 4, pp. 1365–1380, 2015.
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Wachinger, Christian; Golland, Polina; Kremen, William; Fischl, Bruce; Reuter, Martin; Initiative, Alzheimer's Disease Neuroimaging; others,
BrainPrint: A discriminative characterization of brain morphology Journal Article
In: NeuroImage, vol. 109, pp. 232–248, 2015.
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Bron, Esther E; Smits, Marion; van der Flier, Wiesje M; Vrenken, Hugo; Barkhof, Frederik; Scheltens, Philip; Papma, Janne M; Steketee, Rebecca ME; Orellana, Carolina M'endez; Meijboom, Rozanna; others,
Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge Journal Article
In: NeuroImage, vol. 111, pp. 562–579, 2015.
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Wachinger, Christian; Golland, Polina
Diverse Landmark Sampling from Determinantal Point Processes for Scalable Manifold Learning Journal Article
In: arXiv preprint arXiv:1503.03506, 2015.
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Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells III, William M
A Feature-based Approach to Big Data Analysis of Medical Images Proceedings Article
In: International Conference on Information Processing in Medical Imaging, pp. 339–350, Springer International Publishing 2015.
@inproceedings{toews2015feature,
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Wachinger, Christian; Golland, Polina
Sampling from determinantal point processes for scalable manifold learning Journal Article
In: Information Processing in Medical Imaging. Lecture Notes in Computer Science, Springer, pp. 687–698, 2015.
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Wachinger, C; Toews, M; Langs, G; Wells, W; Golland, P
Keypoint Transfer Segmentation Proceedings Article
In: Information Processing in Medical Imaging, Springer 2015.
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Wachinger, Christian; Fritscher, Karl; Sharp, Greg; Golland, Polina
Countour-Driven Atlas-Based Segmenation Journal Article
In: Transactions on medical imaging, 2015.
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2014
Gutierrez-Becker, Benjamin; Mateus, Diana; Shiban, Ehab; Meyer, Bernhard; Lehmberg, Jens; Navab, Nassir
A sparse approach to build shape models with routine clinical data Proceedings
IEEE 2014.
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