The Visualization Toolkit (VTK): Rewriting the rendering code for modern graphics cards

Hanwell, Marcus D.; Martin, Kenneth; Chaudhary, Aashish; Avila, Lisa

doi:10.1016/j.softx.2015.04.001

Cited by 47 publications

(26 citation statements)

References 3 publications

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“…A particularly popular framework in scientific visualization is VTK [10]: VTK offers rendering of both geometric and volumetric data, using an OpenGL 2.1 renderer. However, VTK's complexity has also grown over the years and its API is becoming more complex, making it difficult to change internals without breaking existing applications (G 3 ).…”

Section: Related Workmentioning

confidence: 99%

scenery: Flexible Virtual Reality Visualization on the Java VM

Günther

Pietzsch

Gupta

et al. 2019

2019 IEEE Visualization Conference (VIS)

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Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data from analysis of such data or simulations. Visualization is often the first step in making sense of data, and a crucial part of building and debugging analysis pipelines. It is therefore important that visualizations can be quickly prototyped, as well as developed or embedded into full applications. In order to better judge spatiotemporal relationships, immersive hardware, such as Virtual or Augmented Reality (VR/AR) headsets and associated controllers are becoming invaluable tools. In this work we introduce scenery, a flexible VR/AR visualization framework for the Java VM that can handle mesh and large volumetric data, containing multiple views, timepoints, and color channels. scenery is free and open-source software, works on all major platforms, and uses the Vulkan or OpenGL rendering APIs. We introduce scenery's main features and example applications, such as its use in VR for microscopy, in the biomedical image analysis software Fiji, or for visualising agent-based simulations.

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Section: Related Workmentioning

confidence: 99%

scenery: Flexible Virtual Reality Visualization on the Java VM

Günther

Pietzsch

Gupta

et al. 2019

2019 IEEE Visualization Conference (VIS)

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“…Treatment planning system is the software part of APSDP, which was specifically developed for this method. It was developed in C++ using Visualization Toolkit 22 and Insight Segmentation and Registration Toolkit. 23 Based on a previous description of APSDP, TPS was developed with several functions to cover the entire procedure of the seed implantation treatment, as shown in Fig.…”

Section: A | Auto-positioning System Driven By Preoperative Planningmentioning

confidence: 99%

“…It was developed in C++ using Visualization Toolkit 22 and Insight Segmentation and Registration Toolkit. It was developed in C++ using Visualization Toolkit 22 and Insight Segmentation and Registration Toolkit.…”

mentioning

confidence: 99%

A novel auto‐positioning method in Iodine‐125 seed brachytherapy driven by preoperative planning

Yang

Jiang

et al. 2019

J Applied Clin Med Phys

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Iodine‐125 seed brachytherapy has great potential in the treatment of malignant tumors. However, the success of this treatment is highly dependent on the ability to accurately position the coplanar template. The aim of this study was to develop an auto‐positioning system for the template with a design focus on efficiency and accuracy. In this study, an auto‐positioning system was presented, which was composed of a treatment planning system (TPS) and a robot‐assisted system. The TPS was developed as a control system for the robot‐assisted system. Then, the robot‐assisted system was driven by the output of the TPS to position the template. Contrast experiments for error validation were carried out in a computed tomography environment to compare with the traditional positioning method (TPM). Animal experiments on Sprague–Dawley rats were also carried out to evaluate the auto‐positioning system. The error validation experiments and animal experiments with this auto‐positioning system were successfully carried out with improved efficiency and accuracy. The error validation experiments achieved a positioning error of 1.04 ± 0.19 mm and a positioning time of 23.15 ± 2.52 min, demonstrating a great improvement compared with the TPM (2.55 ± 0.21 mm and 40.35 ± 2.99 min, respectively). The animal experiments demonstrated that the mean deviation of the seed position was 0.75 mm. The dose‐volume histogram of the preoperative planning showed the same as the postoperative dosimetry validation. A novel auto‐positioning system driven by preoperative planning was established, which exhibited higher efficiency and accuracy compared with the TPM.

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“…Although originally designed for graphics applications, the massively parallel design of GPUs allows data processing much more efficiently than is typically possible with traditional CPUs, while at the same time reducing the hardware footprint to the size of a graphics card. Given the increased computational capabilities, using the GPU for both processing and rendering is faster and more flexible compared to earlier approaches on CPUs or fixed-function GPUs [8, 9]. GPU-accelerated computation is, however, not as ubiquitous as it could be, as the development of parallel algorithms can be prohibitively difficult, especially for those not familiar with the different programming model [10].…”

Section: Introductionmentioning

confidence: 99%

Instant Feedback Rapid Prototyping for GPU-Accelerated Computation, Manipulation, and Visualization of Multidimensional Data

Malek

Sensen

2018

International Journal of Biomedical Imaging

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Objective We have created an open-source application and framework for rapid GPU-accelerated prototyping, targeting image analysis, including volumetric images such as CT or MRI data. Methods A visual graph editor enables the design of processing pipelines without programming. Run-time compiled compute shaders enable prototyping of complex operations in a matter of minutes. Results GPU-acceleration increases processing the speed by at least an order of magnitude when compared to traditional multithreaded CPU-based implementations, while offering the flexibility of scripted implementations. Conclusion Our framework enables real-time, intuition-guided accelerated algorithm and method development, supported by built-in scriptable visualization. Significance This is, to our knowledge, the first tool for medical data analysis that provides both high performance and rapid prototyping. As such, it has the potential to act as a force multiplier for further research, enabling handling of high-resolution datasets while providing quasi-instant feedback and visualization of results.

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The Visualization Toolkit (VTK): Rewriting the rendering code for modern graphics cards

Cited by 47 publications

References 3 publications

scenery: Flexible Virtual Reality Visualization on the Java VM

scenery: Flexible Virtual Reality Visualization on the Java VM

A novel auto‐positioning method in Iodine‐125 seed brachytherapy driven by preoperative planning

Instant Feedback Rapid Prototyping for GPU-Accelerated Computation, Manipulation, and Visualization of Multidimensional Data

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