A GPU framework for parallel segmentation of volumetric images using discrete deformable models

Despite the ability of current GPU processors to treat heavy parallel computation tasks, its use for solving medical image segmentation problems is still not fully exploited and remains challenging. A lot of difficulties may arise related to, for example, the different image modalities, noise and artifacts of source images, or the shape and appearance variability of the structures to segment. Motivated by practical problems of image segmentation in the medical field, we present in this paper a GPU framework based on explicit discrete deformable models, implemented over the NVidia CUDA architecture, aimed for the segmentation of volumetric images. The framework supports the segmentation in parallel of different volumetric structures as well as interaction during the segmentation process and real-time visualization of the intermediate results. Promising results in terms of accuracy and speed on a real segmentation experiment have demonstrated the usability of the syste

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

We focus on the challenging task of real-time semantic segmentation in this paper. It finds many practical applications and yet is with fundamental difficulty of reducing a large portion of computation for pixel-wise label inference. We propose an image cascade network (ICNet) that incorporates multi-resolution branches under proper label guidance to address this challenge. We provide in-depth analysis of our framework and introduce the cascade feature fusion unit to quickly achieve high-quality segmentation. Our system yields real-time inference on a single GPU card with decent quality results evaluated on challenging datasets like Cityscapes, CamVid and COCO-Stuff.Comment: ECCV 201

A GPU framework for parallel segmentation of volumetric images using discrete deformable models

Despite the ability of current GPU processors to treat heavy parallel computation tasks, its use for solving medical image segmentation problems is still not fully exploited and remains challenging. A lot of difficulties may arise related to, for example, the different image modalities, noise and artifacts of source images, or the shape and appearance variability of the structures to segment. Motivated by practical problems of image segmentation in the medical field, we present in this paper a GPU framework based on explicit discrete deformable models, implemented over the NVidia CUDA architecture, aimed for the segmentation of volumetric images. The framework supports the segmentation in parallel of different volumetric structures as well as interaction during the segmentation process and real-time visualization of the intermediate results. Promising results in terms of accuracy and speed on a real segmentation experiment have demonstrated the usability of the system.85-95Pubblicat

Quasar: rapid prototyping for image/video processing on heterogeneous hardware.

In this Show&Tell session, we present Quasar, a new framework for heterogeneous programming on (multi-)CPU/GPU systems. GPUs are increasingly used because of the large performance gains they offer for calculations with large amounts of data (e.g., in image and video processing). However, GPU programming is challenging as it requires significant programming expertise and because the corresponding tools are not well suited for rapid prototyping. Quasar is aimed at alleviating these challenges, while still delivering significant acceleration. This is achieved by employing a high-level language, with a similar abstraction level as Matlab or Python. We will demonstrate the Quasar language and IDE by showing several well-known (computationally intensive) image processing algorithms, such as pyramidal optical flow, voxel carving, volumetric ray tracing, geometric active contours, superpixel segmentation, image restoration... We highlight debugging, visualization and profiling capabilities while running the algorithms in real-time. The audience is encouraged to change algorithmic parameters and see their algorithmic influence in real-time. For more information, see http://gepura.i

Efficient Computation of Morphological Greyscale Reconstruction

Morphological reconstruction is an important image operator from mathematical morphology. It is very often used for filtering, segmentation, and feature extraction. However, its computation can be very time-consuming for some input data. In this paper we review several efficient algorithms to compute the reconstruction, and compare their performance on real 3D images of large sizes. Furthermore, we propose a GPU implementation which performs up to 15x faster than the CPU methods. To our best knowledge, this is the first GPU implementation of the morphological reconstruction, described in literature