9 research outputs found
A Data-Analysis and Sensitivity-Optimization Framework for the KATRIN Experiment
Presently under construction, the Karlsruhe TRitium Neutrino (KATRIN) experiment is the next generation tritium beta-decay experiment to perform a direct kinematical measurement of the electron neutrino mass with an unprecedented sensitivity of 200 meV (90% C.L.). This thesis describes the implementation of a consistent data analysis framework, addressing technical aspects of the data taking process and statistical challenges of a neutrino mass estimation from the beta-decay electron spectrum
Kassiopeia: A Modern, Extensible C++ Particle Tracking Package
The Kassiopeia particle tracking framework is an object-oriented software
package using modern C++ techniques, written originally to meet the needs of
the KATRIN collaboration. Kassiopeia features a new algorithmic paradigm for
particle tracking simulations which targets experiments containing complex
geometries and electromagnetic fields, with high priority put on calculation
efficiency, customizability, extensibility, and ease of use for novice
programmers. To solve Kassiopeia's target physics problem the software is
capable of simulating particle trajectories governed by arbitrarily complex
differential equations of motion, continuous physics processes that may in part
be modeled as terms perturbing that equation of motion, stochastic processes
that occur in flight such as bulk scattering and decay, and stochastic surface
processes occuring at interfaces, including transmission and reflection
effects. This entire set of computations takes place against the backdrop of a
rich geometry package which serves a variety of roles, including initialization
of electromagnetic field simulations and the support of state-dependent
algorithm-swapping and behavioral changes as a particle's state evolves. Thanks
to the very general approach taken by Kassiopeia it can be used by other
experiments facing similar challenges when calculating particle trajectories in
electromagnetic fields. It is publicly available at
https://github.com/KATRIN-Experiment/Kassiopei
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Prior to the deep learning era, shape was commonly used to describe the
objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are
predominantly diverging from computer vision, where voxel grids, meshes, point
clouds, and implicit surface models are used. This is seen from numerous
shape-related publications in premier vision conferences as well as the growing
popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915
models). For the medical domain, we present a large collection of anatomical
shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument,
called MedShapeNet, created to facilitate the translation of data-driven vision
algorithms to medical applications and to adapt SOTA vision algorithms to
medical problems. As a unique feature, we directly model the majority of shapes
on the imaging data of real patients. As of today, MedShapeNet includes 23
dataset with more than 100,000 shapes that are paired with annotations (ground
truth). Our data is freely accessible via a web interface and a Python
application programming interface (API) and can be used for discriminative,
reconstructive, and variational benchmarks as well as various applications in
virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present
use cases in the fields of classification of brain tumors, facial and skull
reconstructions, multi-class anatomy completion, education, and 3D printing. In
future, we will extend the data and improve the interfaces. The project pages
are: https://medshapenet.ikim.nrw/ and
https://github.com/Jianningli/medshapenet-feedbackComment: 16 page
Kassiopeia: a modern, extensible C++ particle tracking package
The Kassiopeia particle tracking framework is an object-oriented software package using modern C++ techniques, written originally to meet the needs of the KATRIN collaboration. Kassiopeia features a new algorithmic paradigm for particle tracking simulations which targets experiments containing complex geometries and electromagnetic fields, with high priority put on calculation efficiency, customizability, extensibility, and ease-of-use for novice programmers. To solve Kassiopeia's target physics problem the software is capable of simulating particle trajectories governed by arbitrarily complex differential equations of motion, continuous physics processes that may in part be modeled as terms perturbing that equation of motion, stochastic processes that occur in flight such as bulk scattering and decay, and stochastic surface processes occurring at interfaces, including transmission and reflection effects. This entire set of computations takes place against the backdrop of a rich geometry package which serves a variety of roles, including initialization of electromagnetic field simulations and the support of state-dependent algorithm-swapping and behavioral changes as a particle's state evolves. Thanks to the very general approach taken by Kassiopeia it can be used by other experiments facing similar challenges when calculating particle trajectories in electromagnetic fields. It is publicly available at https://github.com/KATRIN-Experiment/Kassiopeia.United States. Department of Energy. Office of Nuclear Physics (Award FG02-97ER41041)United States. Department of Energy. Office of Nuclear Physics (Award DE-FG02-06ER-41420
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac