The Experts below are selected from a list of 21189 Experts worldwide ranked by ideXlab platform
Ender Konukoglu - One of the best experts on this subject based on the ideXlab platform.
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Clinical evaluation of Fully Automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain
Magnetic Resonance Materials in Physics Biology and Medicine, 2019Co-Authors: Jana Kemnitz, Anja Ruhdorfer, Wolfgang Wirth, Sebastian K. Eder, Felix Eckstein, Akshay Chaudhari, Christian F Baumgartner, Ender KonukogluAbstract:Objective Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a Fully Automated Approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study. Materials and methods The segmentation method is based on U-Net architecture trained on 250 manually segmented thighs from the Osteoarthritis Initiative (OAI). The clinical evaluation is performed on a hold-out test set bilateral thighs of 48 subjects with unilateral knee pain. Results The segmentation time of the method is
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clinical evaluation of Fully Automated thigh muscle and adipose tissue segmentation using a u net deep learning architecture in context of osteoarthritic knee pain
Magnetic Resonance Materials in Physics Biology and Medicine, 2019Co-Authors: Jana Kemnitz, Anja Ruhdorfer, Wolfgang Wirth, Sebastian K. Eder, Akshay S Chaudhari, Felix Eckstein, Christian F Baumgartner, Ender KonukogluAbstract:Objective Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a Fully Automated Approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study.
Pascal Fua - One of the best experts on this subject based on the ideXlab platform.
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a Fully Automated Approach to segmentation of irregularly shaped cellular structures in em images
Medical Image Computing and Computer-Assisted Intervention, 2010Co-Authors: Aurelien Lucchi, Kevin Smith, Radhakrishna Achanta, Vincent Lepetit, Pascal FuaAbstract:While there has been substantial progress in segmenting natural images, state-of-the-art methods that perform well in such tasks unfortunately tend to underperform when confronted with the different challenges posed by electron microscope (EM) data. For example, in EM imagery of neural tissue, numerous cells and subcellular structures appear within a single image, they exhibit irregular shapes that cannot be easily modeled by standard techniques, and confusing textures clutter the background. We propose a Fully Automated Approach that handles these challenges by using sophisticated cues that capture global shape and texture information, and by learning the specific appearance of object boundaries. We demonstrate that our Approach significantly outperforms state-of-the-art techniques and closely matches the performance of human annotators.
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MICCAI (2) - A Fully Automated Approach to segmentation of irregularly shaped cellular structures in EM images
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2010Co-Authors: Aurelien Lucchi, Kevin Smith, Radhakrishna Achanta, Vincent Lepetit, Pascal FuaAbstract:While there has been substantial progress in segmenting natural images, state-of-the-art methods that perform well in such tasks unfortunately tend to underperform when confronted with the different challenges posed by electron microscope (EM) data. For example, in EM imagery of neural tissue, numerous cells and subcellular structures appear within a single image, they exhibit irregular shapes that cannot be easily modeled by standard techniques, and confusing textures clutter the background. We propose a Fully Automated Approach that handles these challenges by using sophisticated cues that capture global shape and texture information, and by learning the specific appearance of object boundaries. We demonstrate that our Approach significantly outperforms state-of-the-art techniques and closely matches the performance of human annotators.
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An all-in-one solution to geometric and photometric calibration
2006Co-Authors: Julien Pilet, Vincent Lepetit, Andreas Geiger, Pascal Lagger, Pascal FuaAbstract:We propose a Fully Automated Approach to calibrating multiple cameras whose fields of view may not all overlap. Our technique only requires waving an arbitrary textured planar pattern in front of the cameras, which is the only manual intervention that is required. The pattern is then automatically detected in the frames where it is visible and used to simultaneously recover geometric and photometric camera calibration parameters. In other words, even a novice user can use our system to extract all the information required to add virtual 3D objects into the scene and light them convincingly. This makes it ideal for Augmented Reality applications and we distribute the code under a GPL license.
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ISMAR - An all-in-one solution to geometric and photometric calibration
2006 IEEE ACM International Symposium on Mixed and Augmented Reality, 2006Co-Authors: Julien Pilet, Vincent Lepetit, Andreas Geiger, Pascal Lagger, Pascal FuaAbstract:We propose a Fully Automated Approach to calibrating multiple cameras whose fields of view may not all overlap. Our technique only requires waving an arbitrary textured planar pattern in front of the cameras, which is the only manual intervention that is required. The pattern is then automatically detected in the frames where it is visible and used to simultaneously recover geometric and photometric camera calibration parameters. In other words, even a novice user can use our system to extract all the information required to add virtual 3D objects into the scene and light them convincingly. This makes it ideal for Augmented Reality applications and we distribute the code under a GPL license.
Jana Kemnitz - One of the best experts on this subject based on the ideXlab platform.
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Clinical evaluation of Fully Automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain
Magnetic Resonance Materials in Physics Biology and Medicine, 2019Co-Authors: Jana Kemnitz, Anja Ruhdorfer, Wolfgang Wirth, Sebastian K. Eder, Felix Eckstein, Akshay Chaudhari, Christian F Baumgartner, Ender KonukogluAbstract:Objective Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a Fully Automated Approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study. Materials and methods The segmentation method is based on U-Net architecture trained on 250 manually segmented thighs from the Osteoarthritis Initiative (OAI). The clinical evaluation is performed on a hold-out test set bilateral thighs of 48 subjects with unilateral knee pain. Results The segmentation time of the method is
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clinical evaluation of Fully Automated thigh muscle and adipose tissue segmentation using a u net deep learning architecture in context of osteoarthritic knee pain
Magnetic Resonance Materials in Physics Biology and Medicine, 2019Co-Authors: Jana Kemnitz, Anja Ruhdorfer, Wolfgang Wirth, Sebastian K. Eder, Akshay S Chaudhari, Felix Eckstein, Christian F Baumgartner, Ender KonukogluAbstract:Objective Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a Fully Automated Approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study.
Christian F Baumgartner - One of the best experts on this subject based on the ideXlab platform.
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Clinical evaluation of Fully Automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain
Magnetic Resonance Materials in Physics Biology and Medicine, 2019Co-Authors: Jana Kemnitz, Anja Ruhdorfer, Wolfgang Wirth, Sebastian K. Eder, Felix Eckstein, Akshay Chaudhari, Christian F Baumgartner, Ender KonukogluAbstract:Objective Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a Fully Automated Approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study. Materials and methods The segmentation method is based on U-Net architecture trained on 250 manually segmented thighs from the Osteoarthritis Initiative (OAI). The clinical evaluation is performed on a hold-out test set bilateral thighs of 48 subjects with unilateral knee pain. Results The segmentation time of the method is
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clinical evaluation of Fully Automated thigh muscle and adipose tissue segmentation using a u net deep learning architecture in context of osteoarthritic knee pain
Magnetic Resonance Materials in Physics Biology and Medicine, 2019Co-Authors: Jana Kemnitz, Anja Ruhdorfer, Wolfgang Wirth, Sebastian K. Eder, Akshay S Chaudhari, Felix Eckstein, Christian F Baumgartner, Ender KonukogluAbstract:Objective Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a Fully Automated Approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study.
Anja Ruhdorfer - One of the best experts on this subject based on the ideXlab platform.
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Clinical evaluation of Fully Automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain
Magnetic Resonance Materials in Physics Biology and Medicine, 2019Co-Authors: Jana Kemnitz, Anja Ruhdorfer, Wolfgang Wirth, Sebastian K. Eder, Felix Eckstein, Akshay Chaudhari, Christian F Baumgartner, Ender KonukogluAbstract:Objective Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a Fully Automated Approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study. Materials and methods The segmentation method is based on U-Net architecture trained on 250 manually segmented thighs from the Osteoarthritis Initiative (OAI). The clinical evaluation is performed on a hold-out test set bilateral thighs of 48 subjects with unilateral knee pain. Results The segmentation time of the method is
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clinical evaluation of Fully Automated thigh muscle and adipose tissue segmentation using a u net deep learning architecture in context of osteoarthritic knee pain
Magnetic Resonance Materials in Physics Biology and Medicine, 2019Co-Authors: Jana Kemnitz, Anja Ruhdorfer, Wolfgang Wirth, Sebastian K. Eder, Akshay S Chaudhari, Felix Eckstein, Christian F Baumgartner, Ender KonukogluAbstract:Objective Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a Fully Automated Approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study.
