A Streaming Volumetric Image Generation Framework for Development and Evaluation of Out-of-Core Methods

by   Dominik Drees, et al.

Advances in 3D imaging technology in recent years have allowed for increasingly high resolution volumetric images of large specimen. The resulting datasets of hundreds of Gigabytes in size call for new scalable and memory efficient approaches in the field of image processing, where some progress has been made already. At the same time, quantitative evaluation of these new methods is difficult both in terms of the availability of specific data sizes and in the generation of associated ground truth data. In this paper we present an algorithmic framework that can be used to efficiently generate test (and ground truth) volume data, optionally even in a streaming fashion. As the proposed nested sweeps algorithm is fast, it can be used to generate test data on demand. We analyze the asymptotic run time of the presented algorithm and compare it experimentally to alternative approaches as well as a hypothetical best-case baseline method. In a case study, the framework is applied to the popular VascuSynth software for vascular image generation, making it capable of efficiently producing larger-than-main memory volumes which is demonstrated by generating a trillion voxel (1TB) image. Implementations of the presented framework are available online in the form of the modified version of Vascusynth and the code used for the experimental evaluation. In addition, the test data generation procedure has been integrated into the popular volume rendering and processing framework Voreen.


page 1

page 8


Hierarchical Random Walker Segmentation for Large Volumetric Biomedical Data

The random walker method for image segmentation is a popular tool for se...

Memory-efficient GAN-based Domain Translation of High Resolution 3D Medical Images

Generative adversarial networks (GANs) are currently rarely applied on 3...

Registration of serial sections: An evaluation method based on distortions of the ground truths

Registration of histological serial sections is a challenging task. Seri...

Diffusion Models for Memory-efficient Processing of 3D Medical Images

Denoising diffusion models have recently achieved state-of-the-art perfo...

Variational Distribution Learning for Unsupervised Text-to-Image Generation

We propose a text-to-image generation algorithm based on deep neural net...

Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Generation

Whereas conventional state-of-the-art image processing systems of record...

Flexible Conditional Image Generation of Missing Data with Learned Mental Maps

Real-world settings often do not allow acquisition of high-resolution vo...

Please sign up or login with your details

Forgot password? Click here to reset