Jochen Peters - Academia.edu (original) (raw)

Papers by Jochen Peters

Research paper thumbnail of Left Atrial Segmentation Challenge 2013: MRI training

<p>This fileset is associated with the Left Atrial Segmentation Challenge 2013 (LASC'13... more <p>This fileset is associated with the Left Atrial Segmentation Challenge 2013 (LASC'13). LASC'13 was part of the STACOM'13 workshop, held in conjunction with MICCAI'13. Seven international research groups, comprising 11 algorithms, participated in the challenge.</p> <p>For a detailed report, please refer to:</p> <p>Tobon-Gomez C, Geers AJ, Peters, J, Weese J, Pinto K, Karim R, Ammar M, Daoudi A, Margeta J, Sandoval Z, Stender B, Zheng Y, Zuluaga, MA, Betancur J, Ayache N, Chikh MA, Dillenseger J-L, Kelm BM, Mahmoudi S, Ourselin S, Schlaefer A, Schaeffter T, Razavi R, Rhode KS. Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets. IEEE Transactions on Medical Imaging, 34(7):1460–1473, 2015.</p> <p>The challenge is also featured on <a href="http://www.cardiacatlas.org/challenges/left-atrium-segmentation-challenge/&quot; target="_blank">http://www.cardiacatlas.org/challenges/left-atrium-segmentation-challenge/</a></p><p></p><p>The data and code of the challenge have been made publicly available to serve as a benchmark for left atrial segmentation algorithms. Code is hosted on <a href="https://github.com/catactg/lasc&quot; target="_blank">https://github.com/catactg/lasc</a></p&gt; <p>Feel free to contact us with any questions.</p> <p> </p> <p>This fileset consists of 10 MRI datasets for training segmentation algorithms. Included are the image and GT segmentation.</p> <p>gt_binary.mhd + gt_binary.raw: Binary image representation of GT</p> <p>image.mhd + image.raw: Image for training</p

Research paper thumbnail of User support method by an automatic speech recognition system

Research paper thumbnail of Comparison of CFD-Based and Bernoulli-Based Pressure Drop Estimates across the Aortic Valve Enabled by Shape-Constrained Deformable Segmentation of Cardiac CT Images

Lecture Notes in Computer Science, 2014

Research paper thumbnail of SVM-based failure detection of GHT localizations

Medical Imaging 2016: Image Processing, 2016

Research paper thumbnail of Semantic Text Clusters and Word Classes – the Dualism of Mutual Information and Maximum Likelihood

Dynamically modeling the word distribution in a variety of texts is a goal with various applicati... more Dynamically modeling the word distribution in a variety of texts is a goal with various applications. For speech recognition a dynamic unigram may efficiently be used for the adaptation of longer ranging language models. For information retrieval it may be a good starting point to predict the most characteristic words in document dependent queries. This short paper presents two approaches for adaptive unigram language models and illustrates their relation in a more general information theoretic framework.

Research paper thumbnail of An icosahedral quasicrystalline model for amorphous semiconductors

Zeitschrift für Physik B Condensed Matter

Research paper thumbnail of CFD- and Bernoulli-based pressure drop estimates: A comparison using patient anatomies from heart and aortic valve segmentation of CT images

Research paper thumbnail of Anatomy-Related Image-Context-Dependent Applications for Efficient Diagnosis

Research paper thumbnail of LM studies on filled pauses in spontaneous medical dictation

Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology companion volume of the Proceedings of HLT-NAACL 2003--short papers - NAACL '03, 2003

Research paper thumbnail of Method of generating a maximum entropy speech model

Research paper thumbnail of Clustering of Text for Structuring of Text Documents and Training of Language Models

Research paper thumbnail of Establishing a Contour of a Structure Based on Image Information

Research paper thumbnail of Method for Control of a Device

Research paper thumbnail of Object Image Labeling Apparatus, Method and Program

Research paper thumbnail of Training methode of the free parameters in a maximum entropy language model

Research paper thumbnail of System for Rapid and Accurate Quantitative Assessment of Traumatic Brain Injury

Research paper thumbnail of Automatic Text Correction

Research paper thumbnail of Method for the generation of a maximum entropy speech model

Research paper thumbnail of Intelligent speech recognition with user interfaces

Research paper thumbnail of Toward fully automatic object detection and segmentation

An automatic procedure for detecting and segmenting anatomical objects in 3-D images is necessary... more An automatic procedure for detecting and segmenting anatomical objects in 3-D images is necessary for achieving a high level of automation in many medical applications. Since today's segmentation techniques typically rely on user input for initialization, they do not allow for a fully automatic workflow. In this work, the generalized Hough transform is used for detecting anatomical objects with well defined shape in 3-D medical images. This well-known technique has frequently been used for object detection in 2-D images and is known to be robust and reliable. However, its computational and memory requirements are generally huge, especially in case of considering 3-D images and various free transformation parameters. Our approach limits the complexity of the generalized Hough transform to a reasonable amount by (1) using object prior knowledge during the preprocessing in order to suppress unlikely regions in the image, (2) restricting the flexibility of the applied transformation to only scaling and translation, and (3) using a simple shape model which does not cover any inter-individual shape variability. Despite these limitations, the approach is demonstrated to allow for a coarse 3-D delineation of the femur, vertebra and heart in a number of experiments. Additionally it is shown that the quality of the object localization is in nearly all cases sufficient to initialize a successful segmentation using shape constrained deformable models.

Research paper thumbnail of Left Atrial Segmentation Challenge 2013: MRI training

<p>This fileset is associated with the Left Atrial Segmentation Challenge 2013 (LASC'13... more <p>This fileset is associated with the Left Atrial Segmentation Challenge 2013 (LASC'13). LASC'13 was part of the STACOM'13 workshop, held in conjunction with MICCAI'13. Seven international research groups, comprising 11 algorithms, participated in the challenge.</p> <p>For a detailed report, please refer to:</p> <p>Tobon-Gomez C, Geers AJ, Peters, J, Weese J, Pinto K, Karim R, Ammar M, Daoudi A, Margeta J, Sandoval Z, Stender B, Zheng Y, Zuluaga, MA, Betancur J, Ayache N, Chikh MA, Dillenseger J-L, Kelm BM, Mahmoudi S, Ourselin S, Schlaefer A, Schaeffter T, Razavi R, Rhode KS. Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets. IEEE Transactions on Medical Imaging, 34(7):1460–1473, 2015.</p> <p>The challenge is also featured on <a href="http://www.cardiacatlas.org/challenges/left-atrium-segmentation-challenge/&quot; target="_blank">http://www.cardiacatlas.org/challenges/left-atrium-segmentation-challenge/</a></p><p></p><p>The data and code of the challenge have been made publicly available to serve as a benchmark for left atrial segmentation algorithms. Code is hosted on <a href="https://github.com/catactg/lasc&quot; target="_blank">https://github.com/catactg/lasc</a></p&gt; <p>Feel free to contact us with any questions.</p> <p> </p> <p>This fileset consists of 10 MRI datasets for training segmentation algorithms. Included are the image and GT segmentation.</p> <p>gt_binary.mhd + gt_binary.raw: Binary image representation of GT</p> <p>image.mhd + image.raw: Image for training</p

Research paper thumbnail of User support method by an automatic speech recognition system

Research paper thumbnail of Comparison of CFD-Based and Bernoulli-Based Pressure Drop Estimates across the Aortic Valve Enabled by Shape-Constrained Deformable Segmentation of Cardiac CT Images

Lecture Notes in Computer Science, 2014

Research paper thumbnail of SVM-based failure detection of GHT localizations

Medical Imaging 2016: Image Processing, 2016

Research paper thumbnail of Semantic Text Clusters and Word Classes – the Dualism of Mutual Information and Maximum Likelihood

Dynamically modeling the word distribution in a variety of texts is a goal with various applicati... more Dynamically modeling the word distribution in a variety of texts is a goal with various applications. For speech recognition a dynamic unigram may efficiently be used for the adaptation of longer ranging language models. For information retrieval it may be a good starting point to predict the most characteristic words in document dependent queries. This short paper presents two approaches for adaptive unigram language models and illustrates their relation in a more general information theoretic framework.

Research paper thumbnail of An icosahedral quasicrystalline model for amorphous semiconductors

Zeitschrift für Physik B Condensed Matter

Research paper thumbnail of CFD- and Bernoulli-based pressure drop estimates: A comparison using patient anatomies from heart and aortic valve segmentation of CT images

Research paper thumbnail of Anatomy-Related Image-Context-Dependent Applications for Efficient Diagnosis

Research paper thumbnail of LM studies on filled pauses in spontaneous medical dictation

Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology companion volume of the Proceedings of HLT-NAACL 2003--short papers - NAACL '03, 2003

Research paper thumbnail of Method of generating a maximum entropy speech model

Research paper thumbnail of Clustering of Text for Structuring of Text Documents and Training of Language Models

Research paper thumbnail of Establishing a Contour of a Structure Based on Image Information

Research paper thumbnail of Method for Control of a Device

Research paper thumbnail of Object Image Labeling Apparatus, Method and Program

Research paper thumbnail of Training methode of the free parameters in a maximum entropy language model

Research paper thumbnail of System for Rapid and Accurate Quantitative Assessment of Traumatic Brain Injury

Research paper thumbnail of Automatic Text Correction

Research paper thumbnail of Method for the generation of a maximum entropy speech model

Research paper thumbnail of Intelligent speech recognition with user interfaces

Research paper thumbnail of Toward fully automatic object detection and segmentation

An automatic procedure for detecting and segmenting anatomical objects in 3-D images is necessary... more An automatic procedure for detecting and segmenting anatomical objects in 3-D images is necessary for achieving a high level of automation in many medical applications. Since today's segmentation techniques typically rely on user input for initialization, they do not allow for a fully automatic workflow. In this work, the generalized Hough transform is used for detecting anatomical objects with well defined shape in 3-D medical images. This well-known technique has frequently been used for object detection in 2-D images and is known to be robust and reliable. However, its computational and memory requirements are generally huge, especially in case of considering 3-D images and various free transformation parameters. Our approach limits the complexity of the generalized Hough transform to a reasonable amount by (1) using object prior knowledge during the preprocessing in order to suppress unlikely regions in the image, (2) restricting the flexibility of the applied transformation to only scaling and translation, and (3) using a simple shape model which does not cover any inter-individual shape variability. Despite these limitations, the approach is demonstrated to allow for a coarse 3-D delineation of the femur, vertebra and heart in a number of experiments. Additionally it is shown that the quality of the object localization is in nearly all cases sufficient to initialize a successful segmentation using shape constrained deformable models.