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Brain extraction network trained with "silver standard" data and fine-tuned with manual annotation for improved segmentation 32

Brain extraction network trained with "silver standard" data...

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32nd SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI) / symposium on Virtual and Augmented Reality (SVR)

作者： Souza, Roberto Lucena, Oeslle Bento, Mariana Garrafa, Julia Rittner, Leticia Appenzeller, Simone Lotufo, Roberto Frayne, Richard Univ Calgary Hotchkiss Brain Inst Radiol & Clin Neurosci Calgary AB Canada Foothills Med Ctr Seaman Family MR Res Ctr Calgary AB Canada Kings Coll London Fac Life Sci & Med Sch Biomed Engn & Imaging Sci London England Foothills Med Ctr Calgary Image Proc & Anal Ctr Calgary AB Canada Univ Estadual Campinas Fac Med Sci Div Rheumatol Campinas SP Brazil Univ Estadual Campinas Med Imaging & Comp Lab Campinas SP Brazil

ISBN: (纸本)9781728152271

Training convolutional neural networks (CNNs) for medical image segmentation often requires large and representative sets of images and their corresponding annotations. Obtaining annotated images usually requires manual intervention, which is expensive and time consuming, as it typically requires a specialist. An alternative approach is to leverage existing automatic segmentation tools and combine them to create consensus-based "silver-standards" annotations. A drawback to this approach is that silver-standards are usually smooth and this smoothness is transmitted to the output segmentation of the network. Our proposal is to use a two-staged approach. First, silver-standard datasets are used to generate a large set of annotated images in order to train the brain extraction network from scratch. Second, fine-tuning is performed using much smaller amounts of manually annotated data so that the network can learn the finer details that are not preserved in the silver-standard data. As an example, our two-staged brain extraction approach has been shown to outperform seven state-of-the-art techniques across three different public datasets. Our results also suggest that CNNs can potentially capture inter-rater annotation variability between experts who annotate the same set of images following the same guidelines, and also adapt to different annotation guidelines.

关键词： Brain MRI Segmentation

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A manycore vision processor architecture for embedded applications

A manycore vision processor architecture for embedded applic...

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brazilian symposium on Computing System Engineering (SBESC)

作者： Bruno Almeida da Silva Arthur Mendes Lima Jones Yudi Automation and Control Group - University of Brasilia Brazil

ISBN: (数字)9781728182865

ISBN: (纸本)9781728182872

Real-Time image processing and computer Vision systems are now in the mainstream of technologies enabling applications for Cyber-Physical Systems, Internet of Things, Augmented Reality, and Industry 4.0. These applications bring the need for Smart Camera for local real-time processing of images and videos. However, the massive amount of data to be processed within short deadlines cannot be handled by most commercial cameras. In this work, we show the design and implementation of a many-core vision processor architecture to be used in Smart Cameras. With massive parallelism exploration and application-specific characteristics, our architecture is composed of distributed processing Elements and Memories connected through a Network-on-Chip. The architecture was implemented as an FPGA overlay, focusing on optimized hardware utilization. The parameterized architecture was characterized by its hardware occupation, maximum operating frequency, and processing frame rate. Different configurations ranging from one to four hundred processing Elements were implemented and compared to several works from the literature. The results show that the proposed architecture successfully allies programmability and performance, being a suitable alternative for future Smart Cameras.

关键词： Parallel processing computer architecture Field programmable gate arrays image processing graphics processing units Visualization Streaming media

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Approach for Accelerating image Enhancement Processes: Optimized OpenCL Architecture 4

Approach for Accelerating Image Enhancement Processes: Optim...

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4th International symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020

作者： Aslan, Menduh Furkan Goksu, Tuna Antalya Bilim Üniversitesi Elektrik-Elektronik Mühendisliǧi Bölümü Antalya Turkey Isparta Uygulamalı Bilimler Üniversitesi Elektrik-Elektronik Mühendisliǧi Bölümü Antalya Turkey

ISBN: (纸本)9781728190907

computer technology, which continues to develop today, often has difficulties in meeting the needs of signal and image processing software. As a result of the developing technology, software needs larger memory and faster processor. Parallel programming method has been developed to solve the speed problems of processors. In this study, OpenCL based image enhancement applications that can work in parallel on the graphics processor unit have been implemented.. The OpenCL architecture has been optimized to maximize the amount of acceleration. Appropriate image enhancement applications have been tested to observe that the designed algorithm and architecture are successful in simple or complex operations. In order to make sense of the speed gain, the same applications were developed with serial programming technique and the results obtained were compared with the applications developed in parallel. It is supported by the comparison results that parallel programming is better in terms of performance. Due to the parallel programming for the hardware used, it was observed that the calculation times were reduced by 1.58 times to 561 times. © 2020 IEEE.

关键词： Heterogeneous Programming OpenCL Parallel Programming Performance Analysis

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Multi-level Graph Label Propagation for image Segmentation

Multi-level Graph Label Propagation for Image Segmentation

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Ivar Vargas Belizario Joao Batista Neto Institute of Mathematics and Computer Science (ICMC) University of Sao Paulo (USP) São Carlos SP Brazil

ISBN: (数字)9781728192741

ISBN: (纸本)9781728192758

This article introduces a multi-level automatic image segmentation method based on graphs and Label Propagation (LP), originally proposed for the detection of communities in complex networks, namely MGLP. To reduce the number of graph nodes, a super-pixel strategy is employed, followed by the computation of color descriptors. Segmentation is achieved by a deterministic propagation of vertex labels at each level. Several experiments with real color images of the BSDS500 dataset were performed to evaluate the method. Our method outperforms related strategies in terms of segmentation quality and processing time. Considering the Covering metric for image segmentation quality, for example, MGLP outperforms LPCI-SP, its most similar counterpart, in 38.99%. In term of processing times, MGLP is 1.07 faster than LPCI-SP.

关键词： image segmentation image edge detection image color analysis Complexity theory Computational efficiency Clustering algorithms Clustering methods

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A Conservative Sharpening Filter for Interpolated images

A Conservative Sharpening Filter for Interpolated Images

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Leonardo R. Emmendorfer Center for Computational Science Federal University of Rio Grande Rio Grande RS Brazil

ISBN: (数字)9781728192741

ISBN: (纸本)9781728192758

Interpolation methods are computationally efficient and rather effective for magnification from low-resolution images in order to generate higher resolution images. However, blur and other undesirable visual artifacts are often produced during the process. This work evaluates a sharpening filter that enhances the quality of interpolated images by preserving information from the original low-resolution images. The novel filter was shown to achieve statistically significant increments in both the PSNR and SSIM of magnified RGB images using two image sets from the literature.

关键词： Interpolation image edge detection image color analysis Visualization Computational modeling image reconstruction Histograms

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cudaIFT: 180x Faster image Foresting Transform for Waterpixel Estimation using CUDA 14

cudaIFT: 180x Faster Image Foresting Transform for Waterpixe...

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14th International Joint Conference on computer Vision, Imaging and computer graphics Theory and Applications (VISAPP)

作者： Goncalves, Henrique M. de Vasconcelos, Gustavo J. Q. Rangel, Paola R. R. Carvalho, Murilo Archilha, Nathaly L. Spina, Thiago, V Brazilian Ctr Res Energy & Mat Brazilian Synchrotron Light Lab Campinas SP Brazil Brazilian Ctr Res Energy & Mat Brazilian Bioscences Natl Lab Campinas SP Brazil Univ Estadual Campinas Inst Comp Campinas SP Brazil Univ Estadual Campinas Inst Geosci Campinas SP Brazil

ISBN: (纸本)9789897583544

We propose a GPU-based version of the image Foresting Transform by Seed Competition (IFT-SC) operator and instantiate it to produce compact watershed-based superpixels (Waterpixels). Superpixels are usually applied as a pre-processing step to reduce the amount of processed data to perform object segmentation. However, recent advances in image acquisition techniques can easily produce 3D images with billions of voxels in roughly 1 second, making the time necessary to compute Waterpixels using the CPU-version of the IFT-SC quickly escalate. We aim to address this fundamental issue, since the efficiency of the entire object segmentation methodology may be hindered by the initial process of estimating superpixels. We demonstrate that our CUDA-based version of the sequential IFT-SC operator can speed up computation by a factor of up to 180x for 2D images, with consistent optimum-path forests without requiring additional CPU post-processing.

关键词： image Foresting Transform GPU Watershed image Segmentation Superpixels

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BTF image recovery based on U-net and texture interpolation

BTF image recovery based on U-net and texture interpolation

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2020 Material Appearance Conference, MAAP 2020

作者： Tada, Naoki Hirai, Keita Department of Imaging Sciences Chiba University 1-33 Yayoicho Inage Ward Chiba-shi Chiba263-8522 Japan

Bidirectional Texture Function (BTF) is one of the methods to reproduce realistic images in computer graphics (CG). This is a technique that can be applied to texture mapping with changing lighting and viewing directions and can reproduce realistic appearance by a simple and high-speed processing. However, in the BTF method, a large amount of texture data is generally measured and stored in advance. In this paper, in order to address the problems related to the measurement time and the texture data size in the BTF reproduction, we a method to generate a BTF image dataset using deep learning. We recovery texture images under various azimuth lighting conditions from a single texture image. For achieving this goal, we applied the U-Net to our BTF recovery. The restored and original texture images are compared using SSIM. It will be confirmed that the reproducibility of fabric and wood textures is high. © 2020, Society for Imaging Science and Technology.

关键词： Recovery

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GPU Usage trends in Medical image processing 13

GPU Usage trends in Medical Image processing

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IEEE 13th International symposium on Applied Computational Intelligence and Informatics (SACI)

作者： Benhamida, Abdallah Kozlovszky, Miklos Szenasi, Sandor Obuda Univ BioTech Res Ctr EKIK Budapest Hungary Obuda Univ John von Neumann Fac Informat Budapest Hungary

ISBN: (纸本)9781728106854

graphics processing units (GPUs), mainly named as general-purpose computing on graphics processing units (GPGPUs) became a widely used technology for many computer-aided procedures in different research or industry areas that require high algorithmic computation capabilities for parallel computing tasks. In the field of bioinformatics and especially in medical image processing, GPUs play an essential role to enable high performance and computation capabilities in order to facilitate the tasks related to image processing. This paper highlights the usage of the graphics processing units in medical image processing and three-dimensional medical image processing related applications (computer-aided image processing, volume rendering, and segmentation), such as image segmentation, noise reduction or denoising, and image registration. It gives a deep understanding of the influence of GPU cards on general purpose programming GPGPU in general and on medical image processing specifically. It presents a good starting point for further applications of GPU programming in the field of biomedical applications and mainly for medical image processing that requires high-performance capabilities for medical image treatment shortly after the medical imaging activity such as in the ICU or the emergency services.

关键词： GPU GPGPU medical image processing biomedical

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Long-Range Decoder Skip Connections: Exploiting Multi-Context Information for Cardiac image Segmentation 32

Long-Range Decoder Skip Connections: Exploiting Multi-Contex...

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32nd SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI) / symposium on Virtual and Augmented Reality (SVR)

作者： Gutierrez-Castilla, Nicolas Torres, Ricardo da S. Falcao, Alexandre X. Kozerke, Sebastian Schwitter, Jurg Masci, Pier-Giorgio Montoya-Zegarra, Javier A. Univ Catolica San Pablo Dept Comp Sci Arequipa Peru Univ Estadual Campinas Inst Comp Campinas SP Brazil Swiss Fed Inst Technol Inst Biomed Engn Zurich Switzerland Lausanne Univ Hosp Ctr Cardiac Magnet Resonance Lausanne Switzerland Kings Coll London Rayne Inst Sch Bioengn & Imaging Sci London England

ISBN: (纸本)9781728152271

The heart is one of the most important organs in our body and many critical diseases are associated with its malfunctioning. To assess the risk for heart diseases, Magnetic Resonance Imaging (MRI) has become the golden standard imaging technique, as it provides to the clinicians stacks of images for analyzing the heart structures, such as the ventricles, and thus to make a diagnosis of the patient's health. The problem is that examination of these stacks, often based on the delineation of heart structures, is tedious and error prone due to inter- and intra-variability among manual delineations. For this reason, the investigation of fully automated methods to support heart segmentation is paramount. Most of the successful methods proposed to solve this problem are based on deep-learning solutions. Especially, encoder-decoder architectures, such as the U-Net [1], have demonstrated to be very effective architectures for medical image segmentation. In this paper, we propose to use long-range skip connections on the decoder-part to incorporate multi-context information onto the predicted segmentation masks and also to improve the generalization of the models. In addition, our method obtains smoother segmentations through the combination of feature maps from different stages onto the final prediction layer. We evaluate our approach in the ACDC [2] and LVSC [3] heart segmentation challenges. Experiments performed on both datasets demonstrate that our approach leads to an improvement on both the total Dice score and the Ejection Fraction Correlation, when combined with state-of-the-art encoder-decoder architectures.

关键词： Biomedical imaging Cardiac image analysis Deep learning Semantic image segmentation

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Capturing Pictures from Human Vision Using SSVEP and Lock-in Amplifier

Capturing Pictures from Human Vision Using SSVEP and Lock-in...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Danson Evan Garcia Kai Wen Zheng Yi Summer Tao Yi Liu Steve Mann MannLab Canada 330 Dundas Street West Toronto Ontario

ISBN: (数字)9781728192741

ISBN: (纸本)9781728192758

We present a novel way of using one's eye to capture an image of what it “sees” through the use of steady-state visually-evoked potentials (SSVEP). Existing methods leveraging response patterns for SSVEP visual image reconstruction show lossy reconstruction and have a lengthy scanning process. With our signal acquisition procedure, data collection requirements are significantly decreased while still improving the signal clarity. The data for image reconstruction were collected from the Oz positioned electrode using a low-cost, wearable electroencephalography (EEG) device. For image reconstruction, software-defined lock-in amplifier (LIA) and discrete Fourier transform (DFT) signal processing methods are analyzed and compared.

关键词： Electroencephalography image reconstruction Visualization Time-frequency analysis Cameras Steady-state Monitoring

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