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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data III"

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Customized Total Variation Algorithm for Metal Artifact Reduction in Computed Tomography 43

Customized Total Variation Algorithm for Metal Artifact Redu...

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43rd Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Deng, Ziheng Zhou, Yufu Zhang, Weikang Lin, Zefan Zhao, Jun Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai Peoples R China

ISBN: (纸本)9781728111797

Metal artifact reduction (MAR) is a challenge for commercial CT systems. The metal objects of high density adversely affect the measurement process and bring difficulties to image reconstruction. Compressed sensing (CS) reconstruction algorithms have been successfully applied in MAR. Ideally, the desired anatomical information can be restored from incomplete projection data. However, in most practical cases, these conventional CS algorithms may instead introduce severe secondary artifacts due to improper prior information. In this paper, we propose a customized total variation (CTV) method to reduce the metal artifacts based on the specific pattern of the artifacts. The gradient operator within the TV norm is redefined according to the distribution of both the metal objects and tissues for each MAR case. We also provide a weighting strategy to further protect the fine details. Experimental results show that the CTV method achieves better performances than those of the conventional methods.

关键词： TV Density measurement Computed tomography Metals reconstruction algorithms Biology image restoration

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Measuring Robustness in Deep Learning Based Compressive Sensing 38

Measuring Robustness in Deep Learning Based Compressive Sens...

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International conference on Machine Learning (ICML)

作者： Darestani, Mohammad Zalbagi Chaudhari, Akshay S. Heckel, Reinhard Rice Univ Dept Elect & Comp Engn Houston TX 77251 USA Stanford Univ Dept Radiol Stanford CA 94305 USA Stanford Univ Dept Biomed Data Sci Stanford CA 94305 USA Tech Univ Munich Dept Elect & Comp Engn Munich Germany

ISBN: (纸本)9781713845065

Deep neural networks give state-of-the-art accuracy for reconstructing images from few and noisy measurements, a problem arising for example in accelerated magnetic resonance imaging (MRI). However, recent works have raised concerns that deep-learning-based image reconstruction methods are sensitive to perturbations and are less robust than traditional methods: Neural networks (i) may be sensitive to small, yet adversarially-selected perturbations, (ii) may perform poorly under distribution shifts, and (iii) may fail to recover small but important features in an image. In order to understand the sensitivity to such perturbations, in this work, we measure the robustness of different approaches for image reconstruction including trained and un-trained neural networks as well as traditional sparsity-based methods. We find, contrary to prior works, that both trained and un-trained methods are vulnerable to adversarial perturbations. Moreover, both trained and un-trained methods tuned for a particular dataset suffer very similarly from distribution shifts. Finally, we demonstrate that an image reconstruction method that achieves higher reconstruction quality, also performs better in terms of accurately recovering fine details. Our results indicate that the state-of-the-art deep-learning-based image reconstruction methods provide improved performance than traditional methods without compromising robustness.

关键词： Magnetic resonance imaging

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Air Fraction Correction in PET Imaging of Lung Disease - Kernel Determination

Air Fraction Correction in PET Imaging of Lung Disease - Ker...

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2022 IEEE Nuclear Science Symposium, Medical Imaging conference, and Room Temperature Semiconductor Detector conference, IEEE NSS MIC RTSD 2022

作者： Leek, Francesca Robinson, Andrew P. Moss, Robert M. Wilson, Frederick J. Hutton, Brian F. Thielemans, Kris Ucl Institute of Nuclear Medicine LondonNW1 2BU United Kingdom Npl Middlesex TeddingtonW11 0LW United Kingdom The Christie Nhs Foundation Trust Npl Christie Medical Physics and Engineering United Kingdom The University of Manchester Schuster Laboratory School of Physics and Astronomy United Kingdom Ucl Department of Medical Physics and Biomedical Engineering Gower Street LondonWC1E 6BT United Kingdom GlaxoSmithKline Research & Development Limited Gunnels Wood Road Herts StevenageSG1 2NY United Kingdom Ucl Centre for Medical Image Computing United Kingdom

ISBN: (纸本)9781665488723

Accurate quantification of radiotracer uptake from lung PET/CT is challenging due to large variations in fractions of tissue, air, blood, and water. The air fraction correction (AFC) determines voxel-wise air fractions (AF) from the CT acquired for attenuation correction (AC) to correct for the variable air content. However, the resolution mismatch between PET and CT can cause artefacts in the AF-corrected image. In this work, we compare different methodologies for determining the optimal kernel to smooth the CT for AFC, when PET images were reconstructed with iterative reconstruction methods, and in the case where AF-corrected lungs exhibit uniform uptake. Noiseless simulations and non-TOF MLEM reconstructions were performed with STIR via SIRF using a digital test-object constructed from a CT scan of a patient with idiopathic pulmonary fibrosis. The optimal smoothing for AFC was determined via i) the point-source insertion-and-subtraction-method, hpts;ii) artefact reduction in the AF-corrected volume-of-interest (VOI), hAFC;iii) smoothing a ground-truth image to match the reconstructed image within the VOI, hPVC. Each of the three kernels were used to smooth the mu-map for AFC of the reconstructed emission data and the RMSE of each of the AF-corrected VOIs, with respect to the ground-truth, was assessed. Applicability of the preferred method was assessed with phantom acquisitions on a clinical scanner. Results demonstrated that, for iterative reconstruction methods, image resolution is dependent on iteration number and VOI density/uptake. Smoothing by hpts resulted in the least quantitatively accurate AF-corrected images at fewer than 40 iterations. For both hAFC and hPVC, RMSE in the AF-corrected IPF regions is fairly stable across iterations. hPVC has the potential to be utilised for determining the appropriate kernel for AFC on clinical scanners for application to patient PET/CT lung scans. © 2022 IEEE.

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Enhanced three-dimensional reconstruction by photometric stereo

Enhanced three-dimensional reconstruction by photometric ste...

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International conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Lyes Abada Islam Hannachi Mohamed Wail Laallam Saliha Aouat Artificial Intelligence and Data Science Department University of Science and Technology Houari Boumediene (USTHB) Bab-Ezzouar Algiers Algeria

Computer vision is a very wide field, although, research is advancing rapidly. But there are still many incomplete areas of research. For this reason we are interested on 3D reconstruction field, which consists of creating a 3D object from one or more 2D images. In this paper, we propose two fully convolutional deep networks. These architectures are a combination between the PS-FCN model and the residual neural network architecture. Our two proposed architectures are improvements of an existing work (PS-FCN). We take as input an arbitrary number of 2D images of an object captured from the same camera position and each image is taken with a different light source position. As output they predict the needle map of the object. According to our study on datasets with different training parameters we show that our proposal outperforms the PS-FCN approach which is considered as a very powerful existing technique and gives effective results to solve the photometric stereo problem.

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PGRID: Power Grid reconstruction in Informal Developments Using High-Resolution Aerial imagery

PGRID: Power Grid Reconstruction in Informal Developments Us...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Simone Fobi Nsutezo Amrita Gupta Duncan Kebut Seema Iyer Luana Marotti Rahul Dodhia Juan M. Lavista Ferres Anthony Ortiz Microsoft AI for Good Research Lab HOTOSM USA for UNHCR

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

As of 2023, a record 117 million people have been dis-placed worldwide, more than double the number from a decade ago [22]. Of these, 32 million are refugees under the UNHCR's mandate, with 8.7 million residing in refugee camps. A critical issue faced by these populations is the lack of access to electricity, with 80% of the 8.7 million refugees and displaced persons in camps globally relying on traditional biomass for cooking and lacking reliable power for essential tasks such as cooking and charging phones. Often, the burden of collecting firewood falls on women and children, who frequently travel up to 20 kilometers into dan-gerous areas, increasing their vulnerability. [7] Electricity access could significantly alleviate these challenges, but a major obstacle is the lack of accurate power grid infrastructure maps, particularly in resource-constrained environments like refugee camps, needed for energy access planning. Existing power grid maps are often outdated, incomplete, or dependent on costly, complex technologies, limiting their practicality. To address this issue, PGRID is a novel application-based approach, which utilizes high-resolution aerial imagery to detect electrical poles and segment electrical lines, creating precise power grid maps. PGRID was tested in the Turkana region of Kenya, specifically the Kakuma and Kalobeyei Camps, cov-ering 84 km 2 and housing over 200,000 residents. Our findings show that PGRID delivers high-fidelity power grid maps especially in unplanned settlements, with F1-scores of 0.71 and 0.82 for pole detection and line segmentation, respectively. This study highlights a practical application for leveraging open data and limited labels to improve power grid mapping in unplanned settlements, where the growing number of displaced persons urgently need sustainable energy infrastructure solutions.

关键词： image segmentation Accuracy Electricity Source coding Power grids Planning Resource management Reliability Open data image reconstruction

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SAvEM3: Pretrained and Distilled Models for General-purpose 3D Neuron reconstruction

SAvEM3: Pretrained and Distilled Models for General-purpose ...

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Hao Zhai Jinyue Guo Yanchao Zhang Jing Liu Hua Han Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing China School of Future Technology University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With an explosion in the uptake of volume electron microscopy (vEM) across neuroscience and fast-paced advances in imaging protocols, it is timely to introduce general-purpose automation for newly generated large-scale vEM datasets. Recent vision foundation models (e.g., SAM) set a new benchmark for the generalization of 2D segmentation. However, SAM has difficulty handling neurons that are densely packed into 3D volumes. To overcome this obstacle, we consider solutions from both data and model aspects. In terms of data, we introduce a data engine to optimize manual labeling, including (i) human-in-the-loop data cleansing and (ii) model-in-the-loop data unification. In terms of model, we present the SAEM 2 -SAvEM 3 with strategies, including (i) auxiliary learning, which predicts complementary representations for SAM masks and improves performance on dense instances; (ii) full-stage distillation, which integrates ViT embeddings into a 3D U-Net, achieves 2D-to-3D lifting and model slimming at the same time; and (iii) prompt-based graph partitioning, which reuses SAM prompts to assign weights of nodes and edges in the oversegmentation graph. According to evaluations of dense and large-scale sparse neurons, the out-of-distribution performance of our pretrained-distilled models is on par with the state-of-the-art supervised and semi-supervised methods. The overall pipeline provides a possible general-purpose solution for 3D neuron reconstruction in any new vEM data. Our code are available at https://***/JackieZhai/SAvEM3.

关键词： Solid modeling Three-dimensional displays Protocols Biological system modeling Neurons Predictive models data models Electron microscopy image reconstruction Electrons

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SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scans

SPSG: Self-Supervised Photometric Scene Generation from RGB-...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Dai, Angela Siddiqui, Yawar Thies, Justus Valentin, Julien Niesner, Matthias Tech Univ Munich Munich Germany Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We present SPSG, a novel approach to generate high-quality, colored 3D models of scenes from RGB-D scan observations by learning to infer unobserved scene geometry and color in a self-supervised fashion. Our self-supervised approach learns to jointly inpaint geometry and color by correlating an incomplete RGB-D scan with a more complete version of that scan. Notably, rather than relying on 3D reconstruction losses to inform our 3D geometry and color reconstruction, we propose adversarial and perceptual losses operating on 2D renderings in order to achieve high-resolution, high-quality colored reconstructions of scenes. This exploits the high-resolution, self-consistent signal from individual raw RGB-D frames, in contrast to fused 3D reconstructions of the frames which exhibit inconsistencies from view-dependent effects, such as color balancing or pose inconsistencies. Thus, by informing our 3D scene generation directly through 2D signal, we produce high-quality colored reconstructions of 3D scenes, outperforming state of the art on both synthetic and real data.

关键词： Geometry Solid modeling Computer vision Three-dimensional displays image color analysis Rendering (computer graphics) data models

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END-TO-END LEARNING OF VARIATIONAL MODELS AND SOLVERS FOR THE RESOLUTION OF INTERPOLATION PROBLEMS

END-TO-END LEARNING OF VARIATIONAL MODELS AND SOLVERS FOR TH...

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IEEE International conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Fablet, R. Drumetz, L. Rousseau, F. IMT Atlantique UMR CNRS Lab STICC Brest France IMT Atlantique UMR INSERM LaTIM Brest France

ISBN: (纸本)9781728176055

Variational models are among the state-of-the-art formulations for the resolution of ill-posed inverse problems. Following recent advances in learning-based variational settings, we investigate the end-to-end learning of variational models, more precisely of the regularization term given some observation model, jointly to the associated solver, so that we can optimize the reconstruction performance. In the proposed end-to-end setting, both the variational cost and the gradient-based solver are stated as neural networks using automatic differentiation for the latter. We consider an application to inverse problems with incomplete datasets (image inpainting and multivariate time series interpolation). We experimentally illustrate that this framework can lead to a significant gain in terms of reconstruction performance, including w.r.t. the direct minimization of the variational formulation derived from the known generative model.

关键词： inverse problems missing data variational models regularization optimizer learning end-to-end

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Photometric Stereo Based on Multiple Kernel Learning 11th

Photometric Stereo Based on Multiple Kernel Learning

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11th International conference on image and Graphics (ICIG)

作者： Wang, Yinuo Guo, Yu Yang, Xiaoxiao Zhang, Xuetao Wang, Fei Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China

ISBN: (纸本)9783030873615;9783030873608

Photometric stereo is a widely used surface reconstruction method which can estimate surface normals of an object from its images captured under different lighting conditions by a fixed camera. To deal with non-Lambertian reflections efficiently, kernel regression based photometric stereo has been proposed and achieved promising results. However, in practice, different data-sets often require different kernels, and the existing methods need selecting and tuning the predefined kernel manually. This is not user-friendly since it's hard to find the best kernel for different data-sets. Furthermore, an improper kernel is very likely to degrade the performance. In this work, we adopt multiple kernel learning to handle this problem. The proposed method learns an optimal consensus kernel from multiple predefined kernels by automatically assigning the most suitable weights for different base kernels. The proposed method is tested on various data-sets, and the experiment results show that our multiple kernel based model outperforms the single kernel based method.

关键词： Photometric stereo Surface reconstruction Multiple kernel learning

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Integrating FireFly Fluorescence into image Guidance for the da Vinci Robot

Integrating FireFly Fluorescence into Image Guidance for the...

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conference on Medical Imaging - image-Guided Procedures, Robotic Interventions, and Modeling

作者： Kim, Juyoung Ferguson, James M. Shrand, Jason A. Pitt, E. Bryn Granna, Josephine Nimmagadda, Naren Kavoussi, Nicholas L. Herrell, S. Duke, iii Webster, Robert J., iii Vanderbilt Univ Dept Elect Engn & Comp Sci 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ Dept Mech Engn 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ Med Ctr Dept Urol Surg 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Inst Surg & Engn Nashville TN USA

ISBN: (纸本)9781510640269

Over the past several years, researchers have made significant progress toward providing image guidance for the da Vinci system, utilizing data sources such as robot kinematic data, endoscope image data, and preoperative medical images. One data source that could provide additional subsurface information for use in image guidance is the da Vinci's FireFly camera system. FireFly is a fluorescence imaging feature for the da Vinci system that uses injected indocyanine green dye and special endoscope filters to illuminate subsurface anatomical features as the surgeon operates. FireFly is now standard for many surgical procedures with the da Vinci robot;however, it is currently challenging to understand spatial relationships between pre-operative CT images and intraoperative fluorescence images. Here, we extend our image guidance system to incorporate FireFly information as well, so that the surgeon can view FireFly data in the image guidance display while operating with the da Vinci robot. We present a method for reconstructing 3D models of the FireFly fluorescence data from endoscope images and mapping the models into an image guidance display that also includes segmented, registered preoperative CT images. We analyze the accuracy of our reconstruction and mapping method and present a proof-of-concept application where we reconstruct a fluorescent subsurface blood vessel and map it into our image guidance display. Our method could be used to provide surgeons with additional context for the FireFly fluorescence imaging data or to provide additional data for computing or verifying the registration between the robot and preoperative images.

关键词： FireFly Fluorescence Imaging Medical Robotics image Guidance

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