Registration inputs for the paper titled: Deformable image registration based on single or multi-atlas methods for automatic muscle segmentation and the generation of augmented imaging datasets fixed (red channel), moving (green channel), and fixed segmentations (blue channel) are combined in one easy to use 3D dicom file. There are 20 combinations presented, with each of 5 subjects used as both the fixed and the moving subject. The results.xcel file highlights the numerical results for the automatic segmentation we found using an in house registration toolkit. If one would like to test others, simply download and register the fixed and moving images, and apply the deformation to the blue channel segmentations. See read me for details...
Accurate registration of images is an important and often crucial step in many areas of image proces...
International audienceThis chapter describes how segmentation and registration can be used to synthe...
3D deformable registration, Two-level method, Statistical atlas Abstract: We propose a two-level met...
which permits unrestricted use, distribution, and reproduction in any medium, provided the original ...
Deformable registration methods are essential for multimodality imaging. Many different methods exis...
Registering medical images of different individuals is difficult due to inherent anatomical variabil...
Deformable image registration is a fundamental task in medical image processing. Among its most impo...
This dissertation presents work on deformable registration of medical images. Deformable registratio...
Research has raised a growing concern about the accuracy of rescaled generic musculoskeletal models ...
Machine learning techniques have shown to be a viable means of analyzing medical images for tumor se...
International audienceManual and automated segmentation of individual muscles in magnetic resonance ...
We propose a meta-algorithm for registration improvement by combining deformable image registrations...
Over the past 20 years, the field of medical image registration has significantly advanced from mult...
We present INSPIRE, a top-performing general-purpose method for deformable image registration. INSPI...
Registration is a key component in multi-atlas approaches to medical image segmentation. Current sta...
Accurate registration of images is an important and often crucial step in many areas of image proces...
International audienceThis chapter describes how segmentation and registration can be used to synthe...
3D deformable registration, Two-level method, Statistical atlas Abstract: We propose a two-level met...
which permits unrestricted use, distribution, and reproduction in any medium, provided the original ...
Deformable registration methods are essential for multimodality imaging. Many different methods exis...
Registering medical images of different individuals is difficult due to inherent anatomical variabil...
Deformable image registration is a fundamental task in medical image processing. Among its most impo...
This dissertation presents work on deformable registration of medical images. Deformable registratio...
Research has raised a growing concern about the accuracy of rescaled generic musculoskeletal models ...
Machine learning techniques have shown to be a viable means of analyzing medical images for tumor se...
International audienceManual and automated segmentation of individual muscles in magnetic resonance ...
We propose a meta-algorithm for registration improvement by combining deformable image registrations...
Over the past 20 years, the field of medical image registration has significantly advanced from mult...
We present INSPIRE, a top-performing general-purpose method for deformable image registration. INSPI...
Registration is a key component in multi-atlas approaches to medical image segmentation. Current sta...
Accurate registration of images is an important and often crucial step in many areas of image proces...
International audienceThis chapter describes how segmentation and registration can be used to synthe...
3D deformable registration, Two-level method, Statistical atlas Abstract: We propose a two-level met...