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Federated Condition Generalization on Low-dose CT reconstruction via Cross-domain Learning 1

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26th International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Chen, Shixuan Cao, Boxuan Du, Yinda Zhang, Yaoduo He, Ji Bian, Zhaoying Zeng, Dong Ma, Jianhua Southern Med Univ Sch Biomed Engn Guangzhou Guangdong Peoples R China Southern Med Univ Zhujiang Hosp Dept Radiol Guangzhou Guangdong Peoples R China Pazhou Lab Huangpu Guangzhou Guangdong Peoples R China Guangzhou Med Univ Sch Biomed Engn Guangzhou Guangdong Peoples R China

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

The harmful radiation dose associated with CT imaging is a major concern because it can cause genetic diseases. Acquiring CT data at low radiation doses has become a pressing goal. Deep learning (DL)-based methods have proven to suppress noise-induced artifacts and promote image quality in low-dose CT imaging. However, it should be noted that most of the DL-based methods are constructed based on the CT data from a specific condition, i.e., specific imaging geometry and specific dose level. Then these methods might generalize poorly to the other conditions, i.e., different imaging geometries and other radiation doses, due to the big data heterogeneity. In this study, to address this issue, we propose a condition generalization method under a federated learning framework (FedCG) to reconstruct CT images on two conditions: three different dose levels and different sampling shcemes at three different geometries. Specifically, the proposed FedCG method leverages a cross-domain learning approach: individual-client sinogram learning and cross-client image reconstruction for condition generalization. In each individual client, the sinogram at each condition is processed similarly to that in the iRadonMAP. Then the CT images at each client are learned via a condition generalization network in the server which considers latent common characteristics in the CT images at all conditions and preserves the client-specific characteristics in each condition. Experiments show that the proposed FedCG outperforms the other competing methods on two imaging conditions in terms of qualitative and quantitative assessments.

关键词： low-dose CT image reconstruction federal learning generalization generalization

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Unsupervised Learning from incomplete Measurements for Inverse Problems 36

Unsupervised Learning From Incomplete Measurements for Inver...

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36th conference on Neural Information Processing Systems (NeurIPS)

作者： Tachella, Julian Chen, Dongdong Davies, Mike CNRS Lab Phys Lyon France ENSL Lyon France Univ Edinburgh Sch Engn Edinburgh Midlothian Scotland

ISBN: (纸本)9781713871088

In many real-world inverse problems, only incomplete measurement data are available for training which can pose a problem for learning a reconstruction function. Indeed, unsupervised learning using a fixed incomplete measurement process is impossible in general, as there is no information in the nullspace of the measurement operator. This limitation can be overcome by using measurements from multiple operators. While this idea has been successfully applied in various applications, a precise characterization of the conditions for learning is still lacking. In this paper, we fill this gap by presenting necessary and sufficient conditions for learning the underlying signal model needed for reconstruction which indicate the interplay between the number of distinct measurement operators, the number of measurements per operator, the dimension of the model and the dimension of the signals. Furthermore, we propose a novel and conceptually simple unsupervised learning loss which only requires access to incomplete measurement data and achieves a performance on par with supervised learning when the sufficient condition is verified. We validate our theoretical bounds and demonstrate the advantages of the proposed unsupervised loss compared to previous methods via a series of experiments on various imaging inverse problems, such as accelerated magnetic resonance imaging, compressed sensing and image inpainting.

关键词： Unsupervised learning

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SwinMM: Masked Multi-view with Swin Transformers for 3D Medical image Segmentation 1

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26th International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Wang, Yiqing Li, Zihan Mei, Jieru Wei, Zihao Liu, Li Wang, Chen Sang, Shengtian Yuille, Alan L. Xie, Cihang Zhou, Yuyin Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Washington Seattle WA USA Johns Hopkins Univ Baltimore MD USA Univ Calif Santa Cruz Santa Cruz CA 95064 USA Tsinghua Univ Beijing Peoples R China Stanford Univ Stanford CA 94305 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Recent advancements in large-scale Vision Transformers have made significant strides in improving pre-trained models for medical image segmentation. However, these methods face a notable challenge in acquiring a substantial amount of pre-training data, particularly within the medical field. To address this limitation, we present Masked Multiview with Swin Transformers (SwinMM), a novel multi-view pipeline for enabling accurate and data-efficient self-supervised medical image analysis. Our strategy harnesses the potential of multi-view information by incorporating two principal components. In the pre-training phase, we deploy a masked multi-view encoder devised to concurrently train masked multi-view observations through a range of diverse proxy tasks. These tasks span image reconstruction, rotation, contrastive learning, and a novel task that employs a mutual learning paradigm. This new task capitalizes on the consistency between predictions from various perspectives, enabling the extraction of hidden multi-view information from 3D medical data. In the fine-tuning stage, a cross-view decoder is developed to aggregate the multi-view information through a cross-attention block. Compared with the previous state-of-the-art self-supervised learning method Swin UNETR, SwinMM demonstrates a notable advantage on several medical image segmentation tasks. It allows for a smooth integration of multi-view information, significantly boosting both the accuracy and data-efficiency of the model. Code and models are available at https://***/UCSC-VLAA/SwinMM/.

关键词： image reconstruction

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Integration of Depth Normal Consistency and Depth Map Refinement for MVS reconstruction 17th

Integration of Depth Normal Consistency and Depth Map Refine...

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17th Chinese conference on image and Graphics Technologies and Applications, IGTA 2022

作者： Yang, Lifang Bai, Zhengyao Liu, Huijie School of Information Science and Engineering Yunnan University Kunming650500 China

ISBN: (纸本)9789811950957

To address the problem of incomplete Multi-view Stereo (MVS) reconstruction, the initial depth and loss function of the depth residual iterative network are investigated, and a new multi-view stereo reconstruction network integrating depth normal consistency and depth map thinning is presented. Firstly, downsampling the input image to create an image pyramid and extracting a feature map from the image pyramid;Then, constructing a cost volume from the 2D feature map, adding the depth normal consistency to the initial cost volume to optimize the depth map. On the DTU data set, the network is tested and compared to traditional reconstruction approaches and MVS networks based on deep learning. The experimental results show that the proposed MVS reconstruction network was produced the better results in completeness and increased the quality of MVS reconstruction. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Stereo image processing

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Enhanced three-dimensional reconstruction by photometric stereo 5

Enhanced three-dimensional reconstruction by photometric ste...

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5th International conference on Pattern Analysis and Intelligent Systems, PAIS 2023

作者： Abada, Lyes Hannachi, Islam Laallam, Mohamed Wail Aouat, Saliha Bab-Ezzouar Artificial Intelligence and Data Science Department Algiers Algeria

ISBN: (纸本)9798350381450

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. © 2023 IEEE.

关键词： Singular value decomposition

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Deep Learning based image Enhancing Environment with Noise Suppression 2

Deep Learning based Image Enhancing Environment with Noise S...

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2nd International conference on Electronics and Renewable Systems, ICEARS 2023

作者： Sivanantham Reddy, Basireddy Satish Kumar Saignaneswar, J. Balaji, Kalahasti Vivek Reddy, K. Siva Reddy, Kusam Lokesh Svec Computer Science and Systems Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798350346640

A deep learning approach will be used to recover ancient pictures that have suffered significant damage. Unlike typical reconstruction processes that are easily handled by supervised learning methods, real-world picture degradation seems to be complex, and the system is unable to generalize due to domain differences between synthetic pictures and actual old pictures. Therefore, using huge amounts of synthetic image pairs combined with real photos, Therefore, using huge amounts of synthetic picture pairs combined with real photos, A unique triplet domain translation network. Two variational autoencoders (VAEs) have been trained to create latent spaces from both fresh and old images, respectively. The translation between two regions is thenmanaged to learn using artificially paired data. This translation normalizes well to actual photographs as the domain gap is filled in the compact latent space. The translation between these two various latent regions has been taught using artificially paired data. This translation normalizes well to images found in the real world because the compact latent space is filled with the domain gap. A global division with an incomplete nonlocal block will target structural issues like cuts and bruises and a local division attacking unstructured defects like unwanted noise and poor contrast to handle the various degradations mixed throughout an old photograph. The latent space fusion of two branches increases the ability to correct numerous flaws in old images. Convolutional neural networks (CNNs) outperform multiple-layer sequenced neural network models at identifying distinct marks, forms, and patterns in images, making them the most efficient method for processing data. The filters are applied by CNN to every pixel in the image. When it comes to visual quality, the suggested method for repairing old photographs performs better than cutting-edge techniques. © 2023 IEEE.

关键词： image reconstruction

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Level-Set Method for Limited-data reconstruction in CT using Dictionary-Based Compressed Sensing 15

Level-Set Method for Limited-Data Reconstruction in CT using...

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15th International conference on Computer and Automation Engineering, ICCAE 2023

作者： Ali, Haytham A. Kudo, Hiroyuki University of Tsukuba Graduate School of Systems and Information Engineering Ibaraki Japan

ISBN: (纸本)9798350396225

Compressed sensing using a dictionary is known to be effective for reconstructing CT images from incomplete projection data (eg. limited-angle CT and sparse-view CT) and its practical applications are increasing. However, when the measurement conditions are insufficient, its performance in image quality is still insufficient and the computational time is long. In this paper, to overcome these limitations, we propose a new method that can dramatically improve the performance by using a priori knowledge about the gray levels of the image to be reconstructed. But, the main problem with using prior information is that a standard formulation leads to a non-convex optimization problem that is difficult to solve. In this study, we succeeded in overcoming this problem based on deep theoretical consideration. Specifically, we formulate a convex optimization problem that can be stably and successfully solved based on an image model that expresses the boundary of the images as a level-set function consisting of linear combinations of the dictionary elements. We create the dictionary that determines the performance by preparing a small number of basic shapes followed by applying the geometric transformations to each shape to construct the dictionary elements. The simulation results for synthetic images and real data shown that the proposed method compared favorably to Total Variation, DART and Dual problem. © 2023 IEEE.

关键词： Compressed sensing

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Phenotypic Parameters Extraction of Panax notoginseng with NeRF 12

Phenotypic Parameters Extraction of Panax notoginseng with N...

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12th International conference on Information Systems and Computing Technology

作者： Wang, Ruoxi Dai, Liang Yang, Qiliang Yang, Ling Kunming Univ Sci & Technol Fac Modern Agr Engn Kunming Yunnan Peoples R China Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming Yunnan Peoples R China

ISBN: (纸本)9798350379860;9798350379877

The growth stage significantly influences both the yield and quality of Panax notoginseng. Accurate plant phenotypic parameters are crucial for the precise management of P. notoginseng cultivation. Currently, traditional methods like manual measurements and tools are used for collecting phenotypic information, but they are inefficient and costly. Manual measurements are prone to subjective biases and can potentially harm plants irreversibly. While 2D images can capture plant phenotypic information, they often suffer from incomplete data and lack precision, especially for plants with complex structures like P. notoginseng. This limitation makes it challenging to achieve comprehensive and accurate measurements. To address these challenges, this study proposes a novel approach using a Neural Radiance Field (NeRF) for extracting phenotypic parameters from P. notoginseng. By capturing video and multi-view image sequences of P. notoginseng, we were able to achieve high-fidelity 3D rendering of the plants and extract point cloud data from them. This approach enabled accurate measurement of plant height and leaf area parameters. The results demonstrate promising accuracy, with an average percentage error of 1.76% for plant height and 1.73% for leaf area based on the point cloud measurements obtained using NeRF. This method leverages advanced computational techniques to overcome the limitations of traditional 2D imaging methods, offering a more comprehensive and precise means of phenotypic characterization for complex plant structures like P. notoginseng.

关键词： component Panax notoginseng image Sequence Neural Radiation Field 3D reconstruction plant phenotype

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Signal Domain Learning Approach for Optoacoustic image reconstruction from Limited View data 5

Signal Domain Learning Approach for Optoacoustic Image Recon...

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5th International conference on Medical Imaging with Deep Learning (MIDL)

作者： Susmelj, Anna Klimovskaia Lafci, Berkan Ozdemir, Firat Davoudi, Neda Dean-Ben, Xose Luis Perez-Cruz, Fernando Razansky, Daniel Swiss Fed Inst Technol Swiss Data Sci Ctr Zurich Switzerland EPFL Zurich Switzerland Univ Zurich Inst Pharmacol & Toxicol Fac Med Zurich Switzerland Univ Zurich Inst Biomed Engn Fac Med Zurich Switzerland Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Inst Biomed Engn Zurich Switzerland Swiss Fed Inst Technol Dept Comp Sci Inst Machine Learning Zurich Switzerland

Multi-spectral optoacoustic tomography (MSOT) relies on optical excitation of tissues with subsequent detection of the generated ultrasound waves. Optimal image quality in MSOT is achieved by detection of signals from a broad tomographic view. However, due to physical constraints and other cost-related considerations, most imaging systems are implemented with probes having limited tomographic coverage around the imaged object, such as linear array transducers often employed for clinical ultrasound (US) imaging. MSOT image reconstruction from limited-view data results in arc-shaped image artifacts and disrupted shape of the vascular structures. Deep learning methods have previously been used to recover MSOT images from incomplete tomographic data, albeit poor performance was attained when training with data from simulations or other imaging modalities. We propose a two-step method consisting of i) style transfer for domain adaptation between simulated and experimental MSOT signals, and ii) supervised training on simulated data to recover missing tomographic signals in realistic clinical data. The method is shown capable of correcting images reconstructed from sub-optimal probe geometries using only signal domain data without the need for training with ground truth (GT) full-view images.

关键词： Optoacoustics Limited View Artifacts Signal Domain Learning Style Transfer Domain Adaptation

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SNeS: Learning Probably Symmetric Neural Surfaces from incomplete data 17th

SNeS: Learning Probably Symmetric Neural Surfaces from Incom...

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17th European conference on Computer Vision (ECCV)

作者： Insafutdinov, Eldar Campbell, Dylan Henriques, Joao F. Vedaldi, Andrea Univ Oxford Oxford OX1 3PJ England

ISBN: (纸本)9783031198236;9783031198243

We present a method for the accurate 3D reconstruction of partly-symmetric objects. We build on the strengths of recent advances in neural reconstruction and rendering such as Neural Radiance Fields (NeRF). A major shortcoming of such approaches is that they fail to reconstruct any part of the object which is not clearly visible in the training image, which is often the case for in-the-wild images and videos. When evidence is lacking, structural priors such as symmetry can be used to complete the missing information. However, exploiting such priors in neural rendering is highly non-trivial: while geometry and non-reflective materials may be symmetric, shadows and reflections from the ambient scene are not symmetric in general. To address this, we apply a soft symmetry constraint to the 3D geometry and material properties, having factored appearance into lighting, albedo colour and reflectivity. We evaluate our method on the recently introduced CO3D dataset, focusing on the car category due to the challenge of reconstructing highly-reflective materials. We show that it can reconstruct unobserved regions with high fidelity and render high-quality novel view images.

关键词： 3D reconstruction Novel view synthesis Neural rendering

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