OSIRIX: An Open Source Platform for Advanced Multimodality Medical Imaging

2006 ◽  
Author(s):  
Osman Ratib Faha
Keyword(s):  
Medical Imaging ◽  
Open Source

Open Source Applications for Image Visualization and Processing in Neuroimaging Training

2015 ◽  
pp. 1319-1332
Author(s):  
Juan A. Juanes ◽  
Pablo Ruisoto ◽  
Alberto Prats-Galino ◽  
Andrés Framiñán
Keyword(s):  
Magnetic Resonance ◽  
Medical Imaging ◽  
Open Source ◽  
Medical Images ◽  
Three Dimensional ◽  
Mr Images ◽  
Image Visualization ◽  
3D Slicer ◽  
Volumetric Reconstruction ◽  
Key Features

The aim of this paper is to demonstrate the major role and potential of three of the most powerful open source computerized tools for the advanced processing of medical images, in the study of neuroanatomy. DICOM images were acquired with radiodiagnostic equipment using 1.5 Tesla Magnetic Resonance (MR) images. Images were further processed using the following applications: first, OsiriXTM version 4.0 32 bits for OS; Second, 3D Slicer version 4.3; and finally, MRIcron, version 6. Advanced neuroimaging processing requires two key features: segmentation and three-dimensional or volumetric reconstruction. Examples of identification and reconstruction of some of the most complex neuroimaging elements such vascular ones and tractographies are included in this paper. The three selected applications represent some of the most versatile technologies within the field of medical imaging.

scholarly journals Somatic Narratives about Illness. Biometric Visualization of Diseased and Disabled Bodies in Art and Science Projects

Humanities ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 19
Author(s):  
Ewelina Twardoch-Raś
Keyword(s):  
Medical Imaging ◽  
Open Source ◽  
The Other ◽  
Art And Science ◽  
Science Projects ◽  
Other Hand ◽  
Affective Experiences ◽  
The One ◽  
Tools And Methods

This article proposes investigating how the problem of chronic and deadly diseases and bodily injuries is explored in selected contemporary artistic projects based on biometric technologies and medical imaging. All of the projects that will be analysed use specific medical tools and methods (e.g., roentgenography or bio-tracking) to provide detailed, affective images of disability and illness. Nonetheless, these projects were created as pieces of art that combine visual and verbal elements: photographs, collages, and other illustrated stories (e.g., “biometric diaries” or open-source art). On the one hand, they show the “inner” and often invisible face of illness and suffering, but on the other hand they also raise questions related to algorithmic reductionism and politicization of such forms of representation of disease. This article will focus on artistic projects created by Diane Covert, Salvatore Iaconesi and Laurie Frick. It refers to the ‘ethos of health’ and the conception of ethopolitics (Nicolas Rose) to show the place in contemporary biopolitical society of illness (Thomas Lemke), which can be seen as an exceptional form of the body’s condition. Moreover, it considers the problem of the politicization of the biological body and affective experiences (Britta Timm Knudsen and Carsten Stage) and the category of untold histories explored by Joanne Garde-Hansen and Kristyn Gorton.

scholarly journals Rapid Development of Medical Imaging Tools with Open-Source Libraries

2007 ◽  
Vol 20 (S1) ◽  
pp. 83-93 ◽  
Author(s):  
Jesus J. Caban ◽  
Alark Joshi ◽  
Paul Nagy
Keyword(s):  
Medical Imaging ◽  
Open Source ◽  
Rapid Development

scholarly journals Medical imaging deep learning with differential privacy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexander Ziller ◽  
Dmitrii Usynin ◽  
Rickmer Braren ◽  
Marcus Makowski ◽  
Daniel Rueckert ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Medical Imaging ◽  
Open Source ◽  
Differential Privacy ◽  
Medical Image Segmentation ◽  
Stochastic Gradient Descent ◽  
Neural Network Training ◽  
Network Training ◽  
Computational Performance

AbstractThe successful training of deep learning models for diagnostic deployment in medical imaging applications requires large volumes of data. Such data cannot be procured without consideration for patient privacy, mandated both by legal regulations and ethical requirements of the medical profession. Differential privacy (DP) enables the provision of information-theoretic privacy guarantees to patients and can be implemented in the setting of deep neural network training through the differentially private stochastic gradient descent (DP-SGD) algorithm. We here present deepee, a free-and-open-source framework for differentially private deep learning for use with the PyTorch deep learning framework. Our framework is based on parallelised execution of neural network operations to obtain and modify the per-sample gradients. The process is efficiently abstracted via a data structure maintaining shared memory references to neural network weights to maintain memory efficiency. We furthermore offer specialised data loading procedures and privacy budget accounting based on the Gaussian Differential Privacy framework, as well as automated modification of the user-supplied neural network architectures to ensure DP-conformity of its layers. We benchmark our framework’s computational performance against other open-source DP frameworks and evaluate its application on the paediatric pneumonia dataset, an image classification task and on the Medical Segmentation Decathlon Liver dataset in the task of medical image segmentation. We find that neural network training with rigorous privacy guarantees is possible while maintaining acceptable classification performance and excellent segmentation performance. Our framework compares favourably to related work with respect to memory consumption and computational performance. Our work presents an open-source software framework for differentially private deep learning, which we demonstrate in medical imaging analysis tasks. It serves to further the utilisation of privacy-enhancing techniques in medicine and beyond in order to assist researchers and practitioners in addressing the numerous outstanding challenges towards their widespread implementation.

scholarly journals Tetrahedral mesh generation for medical imaging

2005 ◽  
Author(s):  
Andriy Fedorov ◽  
Nikos Chrisochoides ◽  
Ron Kikinis ◽  
Simon Warfield
Keyword(s):  
Medical Imaging ◽  
Open Source ◽  
Mesh Generation ◽  
Tetrahedral Mesh ◽  
Deformable Objects ◽  
Mr Images ◽  
Mesh Generator ◽  
Preliminary Results ◽  
Tetrahedral Mesh Generation

We describe the open source implementation of an adaptive tetrahedral mesh generator particularly targeted for non-rigid FEM registration of MR images. While many medical imaging applications require robust mesh generation, there are few codes available. Moreover, most of the practical implementations are commercial. The algorithm we have implemented has been previously evaluated for simulations of highly deformable objects, and the preliminary results show its applicability to the targeted application. The implementation we describe is open source and will be available within Insight Toolkit.

scholarly journals Three-dimensional semi-automated volumetric assessment of the pulp space of teeth following regenerative dental procedures

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Heeresh Shetty ◽  
Shishir Shetty ◽  
Adesh Kakade ◽  
Aditya Shetty ◽  
Mohmed Isaqali Karobari ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Open Source ◽  
Three Dimensional ◽  
Volume Estimation ◽  
Permanent Teeth ◽  
3D Slicer ◽  
Link Type ◽  
Volumetric Assessment ◽  
Critical Measure ◽  
Significant Difference

AbstractThe volumetric change that occurs in the pulp space over time represents a critical measure when it comes to determining the secondary outcomes of regenerative endodontic procedures (REPs). However, to date, only a few studies have investigated the accuracy of the available domain-specialized medical imaging tools with regard to three-dimensional (3D) volumetric assessment. This study sought to compare the accuracy of two different artificial intelligence-based medical imaging programs namely OsiriX MD (v 9.0, Pixmeo SARL, Bernex Switzerland, https://www.osirix-viewer.com) and 3D Slicer (http://www.slicer.org), in terms of estimating the volume of the pulp space following a REP. An Invitro assessment was performed to check the reliability and sensitivity of the two medical imaging programs in use. For the subsequent clinical application, pre- and post-procedure cone beam computed tomography scans of 35 immature permanent teeth with necrotic pulp and periradicular pathosis that had been treated with a cell-homing concept-based REP were processed using the two biomedical DICOM software programs (OsiriX MD and 3D Slicer). The volumetric changes in the teeth’s pulp spaces were assessed using semi-automated techniques in both programs. The data were statistically analyzed using t-tests and paired t-tests (P = 0.05). The pulp space volumes measured using both programs revealed a statistically significant decrease in the pulp space volume following the REP (P < 0.05), with no significant difference being found between the two programs (P > 0.05). The mean decreases in the pulp space volumes measured using OsiriX MD and 3D Slicer were 25.06% ± 19.45% and 26.10% ± 18.90%, respectively. The open-source software (3D Slicer) was found to be as accurate as the commercially available software with regard to the volumetric assessment of the post-REP pulp space. This study was the first to demonstrate the step-by-step application of 3D Slicer, a user-friendly and easily accessible open-source multiplatform software program for the segmentation and volume estimation of the pulp spaces of teeth treated with REPs.

scholarly journals An open source toolkit for medical imaging de-identification

2010 ◽  
Vol 20 (8) ◽  
pp. 1896-1904 ◽  
Author(s):  
David Rodríguez González ◽  
Trevor Carpenter ◽  
Jano I. van Hemert ◽  
Joanna Wardlaw
Keyword(s):  
Medical Imaging ◽  
Open Source
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