About me.
I am a Postdoctoral Research. I have previously done my PhD studies at the Chair of Computer Aided Procedures under the supervision of Nassir Navab. My main research topics are focused on the application of Machine Learning techniques for the analysis and processing of medical images.
Research Interests.
- Machine Learning for Medical Image Processing
- Reinforcement Learning applied to Dataset Optimization
- Statistical Shape Modelling
- Age Estimation from MR images
Awards.
- Student Travel Award MICCAI 2016
- Young Investigator Award MICCAI 2016
- Student Travel Award MICCAI 2017
Publications
Benjamin ; Sarasua Ignacio ; Wachinger Gutierrez-Becker, Christian
Discriminative and generative models for anatomical shape analysis on point clouds with deep neural networks Journal Article
In: Medical Image Analysis, vol. 67, pp. 101852, 2020.
@article{gutierrez2020discriminative,
title = {Discriminative and generative models for anatomical shape analysis on point clouds with deep neural networks},
author = {Gutierrez-Becker, Benjamin ; Sarasua Ignacio ; Wachinger, Christian },
url = {https://arxiv.org/pdf/2010.00820},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Medical Image Analysis},
volume = {67},
pages = {101852},
publisher = {Elsevier},
abstract = {We introduce deep neural networks for the analysis of anatomical shapes that learn a low-dimensional shape representation from the given task, instead of relying on hand-engineered representations. Our framework is modular and consists of several computing blocks that perform fundamental shape processing tasks. The networks operate on unordered point clouds and provide invariance to similarity transformations, avoiding the need to identify point correspondences between shapes. Based on the framework, we assemble a discriminative model for disease classification and age regression, as well as a generative model for the accruate reconstruction of shapes.
In particular, we propose a conditional generative model, where the condition vector provides a mechanism to control the generative process. For instance, it enables to assess shape variations specific to a particular diagnosis, when passing it as side information.
Next to working on single shapes, we introduce an extension for the joint analysis of multiple anatomical structures, where the simultaneous modeling of multiple structures can lead to a more compact encoding and a better understanding of disorders.
We demonstrate the advantages of our framework in comprehensive experiments on real and synthetic data. The key insights are that (i) learning a shape representation specific to the given task yields higher performance than alternative shape descriptors, (ii) multi-structure analysis is both more efficient and more accurate than single-structure analysis, and (iii) point clouds generated by our model capture morphological differences associated to Alzheimer’s disease, to the point that they can be used to train a discriminative model for disease classification. Our framework naturally scales to the analysis of large datasets, giving it the potential to learn characteristic variations in large populations.},
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We introduce deep neural networks for the analysis of anatomical shapes that learn a low-dimensional shape representation from the given task, instead of relying on hand-engineered representations. Our framework is modular and consists of several computing blocks that perform fundamental shape processing tasks. The networks operate on unordered point clouds and provide invariance to similarity transformations, avoiding the need to identify point correspondences between shapes. Based on the framework, we assemble a discriminative model for disease classification and age regression, as well as a generative model for the accruate reconstruction of shapes.
In particular, we propose a conditional generative model, where the condition vector provides a mechanism to control the generative process. For instance, it enables to assess shape variations specific to a particular diagnosis, when passing it as side information.
Next to working on single shapes, we introduce an extension for the joint analysis of multiple anatomical structures, where the simultaneous modeling of multiple structures can lead to a more compact encoding and a better understanding of disorders.
We demonstrate the advantages of our framework in comprehensive experiments on real and synthetic data. The key insights are that (i) learning a shape representation specific to the given task yields higher performance than alternative shape descriptors, (ii) multi-structure analysis is both more efficient and more accurate than single-structure analysis, and (iii) point clouds generated by our model capture morphological differences associated to Alzheimer’s disease, to the point that they can be used to train a discriminative model for disease classification. Our framework naturally scales to the analysis of large datasets, giving it the potential to learn characteristic variations in large populations. 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.},
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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. 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.},
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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. 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.},
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tppubtype = {inproceedings}
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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. 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. },
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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. 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},
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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}
}
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}
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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. 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},
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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. 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.},
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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. 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},
journal = {Medical Image Analysis},
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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.
@article{riva20173d,
title = {3D intra-operative ultrasound and MR image guidance: pursuing an ultrasound-based management of brainshift to enhance neuronavigation},
author = {Riva, Marco and Hennersperger, Christoph and Milletari, Fausto and Katouzian, Amin and Pessina, Federico and Gutierrez-Becker, Benjamin and Castellano, Antonella and Navab, Nassir and Bello, Lorenzo},
year = {2017},
date = {2017-01-01},
journal = {International Journal of Computer Assisted Radiology and Surgery},
pages = {1--15},
publisher = {Springer International Publishing},
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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.
@proceedings{dubost2016hands,
title = {Hands-Free Segmentation of Medical Volumes via Binary Inputs},
author = {Dubost, Florian and Peter, Loic and Rupprecht, Christian and Gutierrez-Becker, Benjamin and Navab, Nassir},
url = {http://campar.in.tum.de/pub/dubost2016labels/dubost2016labels.pdf},
year = {2016},
date = {2016-01-01},
booktitle = {International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis},
pages = {259--268},
organization = {Springer International Publishing},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
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Gutierrez-Becker, Benjamin; Mateus, Diana; Peter, Loic; Navab, Nassir
Learning Optimization Updates for Multimodal Registration Proceedings
Springer 2016.
@proceedings{gutierrez2016learning,
title = {Learning Optimization Updates for Multimodal Registration},
author = {Gutierrez-Becker, Benjamin and Mateus, Diana and Peter, Loic and Navab, Nassir},
url = {http://ai-med.de/wp-content/uploads/2017/06/MultimodalMICCAI15_CameraReady-1.pdf},
year = {2016},
date = {2016-01-01},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages = {19--27},
organization = {Springer},
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Simonovsky, Martin; Gutierrez-Becker, Benjamin; Mateus, Diana; Navab, Nassir; Komodakis, Nikos
A Deep Metric for Multimodal Registration Proceedings
Springer 2016.
@proceedings{simonovsky2016deep,
title = {A Deep Metric for Multimodal Registration},
author = {Martin Simonovsky and Benjamin Gutierrez-Becker and Diana Mateus and Nassir Navab and Nikos Komodakis},
url = {https://arxiv.org/pdf/1609.05396.pdf},
year = {2016},
date = {2016-01-01},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
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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.
@proceedings{gutierrez2014sparse,
title = {A sparse approach to build shape models with routine clinical data},
author = {Gutierrez-Becker, Benjamin and Mateus, Diana and Shiban, Ehab and Meyer, Bernhard and Lehmberg, Jens and Navab, Nassir},
year = {2014},
date = {2014-01-01},
booktitle = {Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on},
pages = {258--261},
organization = {IEEE},
keywords = {},
pubstate = {published},
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Gutierrez-Becker, Benjamin; Cosio, Fernando Arambula; Huerta, Mario E Guzman; Benavides-Serralde, Jesus Andres; Camargo-Marin, Lisbeth; Medina Banuelos, Veronica journal=Medical & biological engineering & computing
Automatic segmentation of the fetal cerebellum on ultrasound volumes, using a 3D statistical shape model Journal Article
In: vol. 51, no. 9, pp. 1021–1030, 2013.
@article{gutierrez2013automatic,
title = {Automatic segmentation of the fetal cerebellum on ultrasound volumes, using a 3D statistical shape model},
author = {Gutierrez-Becker, Benjamin and Cosio, Fernando Arambula and Huerta, Mario E Guzman and Benavides-Serralde, Jesus Andres and Camargo-Marin, Lisbeth and Medina Banuelos, Veronica} journal={Medical & biological engineering & computing},
year = {2013},
date = {2013-01-01},
volume = {51},
number = {9},
pages = {1021--1030},
publisher = {Springer Berlin Heidelberg},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Gutierrez-Becker, Benjamin; Arambula, Fernando; Guzman, Mario; Benavides, Jesus
Automatic segmentation of the cerebellum of fetuses on 3D ultrasound images, using a 3D Point Distribution Model Proceedings
IEEE 2010.
@proceedings{becker2010automatic,
title = {Automatic segmentation of the cerebellum of fetuses on 3D ultrasound images, using a 3D Point Distribution Model},
author = {Benjamin Gutierrez-Becker and Fernando Arambula and Mario Guzman and Jesus Benavides},
year = {2010},
date = {2010-01-01},
booktitle = {Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE},
pages = {4731--4734},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
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