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3d Single Vessel Fractional Moving Blood Volume (3d-svFMBV): Fully Automated Tissue Perfusion Estimation Using Ultrasound

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IEEE TRANSACTIONS ON MEdICAL IMAGING 2024年第7期43卷 2707-2717页

作者： Yin, Yi Clark, Alys R. Collins, Sally L. Univ Oxford Nuffield Dept Womens & Reprod Hlth Oxford OX1 2JD England Univ Oxford Big Data Inst Oxford OX1 2JD England Univ Auckland Auckland Bioengn Inst Auckland 1010 New Zealand John Radcliffe Hosp Womens Ctr Fetal Med Unit Oxford OX3 9DU England

Power doppler ultrasound (Pd-US) is the ideal modality to assess tissue perfusion as it is cheap, patient-friendly and does not require ionizing radiation. However, meaningful inter-patient comparison only occurs if differences in tissue-attenuation are corrected for. This can be done by standardizing the Pd-US signal to a blood vessel assumed to have 100% vascularity. The original method to do this is called fractional moving blood volume (FMBV). We describe a novel, fully-automated method combining image processing, numerical modelling, and deep learning to estimate three-dimensional single vessel fractional moving blood volume (3d-svFMBV). We map the Pd signals to a characteristic intensity profile within a single large vessel to define the standardization value at the high shear vessel margins. This removes the need for mathematical correction for background signal which can introduce error. The 3d-svFMBV was first tested on synthetic images generated using the characteristics of uterine artery and physiological ultrasound noise levels, demonstrating prediction of standardization value close to the theoretical ideal. Clinical utility was explored using 143 first-trimester placental ultrasound volumes. More biologically plausible perfusion estimates were obtained, showing improved prediction of pre-eclampsia compared with those generated with the semi-automated original 3d-FMBV technique. The proposed 3d-svFMBV method overcomes the limitations of the original technique to provide accurate and robust placental perfusion estimation. This not only has the potential to provide an early pregnancy screening tool but may also be used to assess perfusion of different organs and tumors.

关键词： Standardization Blood Ultrasonic imaging image segmentation Three-dimensional displays Pregnancy Estimation Perfusion power doppler ultrasound vessel segmentation uterine vasculature

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Improvement of 2d cine image quality using 3d priors and cycle generative adversarial network for low field MRI-guided radiation therapy

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MEdICAL PHYSICS 2024年第5期51卷 3495-3509页

作者： dong, Yuyan Yang, Fei Wen, Jie Cai, Jing Zeng, Feiyan Liu, Mengqiu Li, Shuang Wang, Jiangtao Ford, John Chetley Portelance, Lorraine Yang, Yidong Univ Sci & Technol China Dept Engn & Appl Phys Hefei Anhui Peoples R China Univ Miami Miller Sch Med Miami FL USA Univ Sci & Technol China Affiliated Hosp USTC 1 Dept Radiol Div Life Sci & Med Hefei Anhui Peoples R China Hong Kong Polytech Univ Dept Hlth Technol & Informat Kowloon Hong Kong Peoples R China Sichuan Acad Med Sci Canc Ctr Sichuan Prov Peoples Hosp Chengdu Sichuan Peoples R China Univ Sci & Technol China Affiliated Hosp USTC 1 Dept Radiat Oncol Div Life Sci & Med Hefei Anhui Peoples R China Univ Sci & Technol China Sch Phys Sci Med Phys Program Hefei Anhui Peoples R China

BackgroundCine magnetic resonance (MR) images have been used for real-time MR guided radiation therapy (MRgRT). However, the onboard MR systems with low-field strength face the problem of limited image *** improve the quality of cine MR images in MRgRT using prior image information provided by the patient planning and positioning MR *** study employed MR images from 18 pancreatic cancer patients who received MR-guided stereotactic body radiation therapy. Planning 3d MR images were acquired during the patient simulation, and positioning 3d MR images and 2d sagittal cine MR images were acquired before and during the beam delivery, respectively. A deep learning-based framework consisting of two cycle generative adversarial networks (CycleGAN), denoising CycleGAN and Enhancement CycleGAN, was developed to establish the mapping between the 3d and 2d MR images. The denoising CycleGAN was trained to first denoise the cine images using the time domain cine image series, and the Enhancement CycleGAN was trained to enhance the spatial resolution and contrast by taking advantage of the prior image information from the planning and positioning images. The denoising performance was assessed by signal-to-noise ratio (SNR), structural similarity index measure, peak SNR, blind/reference-less image spatial quality evaluator (BRISQUE), natural image quality evaluator, and perception-based image quality evaluator scores. The quality enhancement performance was assessed by the BRISQUE and physician visual scores. In addition, the target contouring was evaluated on the original and processed *** differences were found for all evaluation metrics after denoising CycleGAN processing. The BRISQUE and visual scores were also significantly improved after sequential denoising and Enhancement CycleGAN processing. In target contouring evaluation, dice similarity coefficient, centroid distance, Hausdorff distance, and average surface distance val

关键词： cine MRI CycleGAN image quality positioning MR images prior images

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Uni-3dAd: Gan-inversion aided universal 3d anomaly detection on model-free products

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 272卷

作者： Liu, Jiayu Mou, Shancong Gaw, Nathan Institute, Yinan Wang Rensselaer Polytech Inst Dept Ind & Syst Engn Troy NY 12180 USA Univ Minnesota Twin Cities Dept Ind & Syst Engn Minneapolis MN 55455 USA Air Force Inst Technol Dept Operat Sci Minneapolis OH 45433 USA

Anomaly detection is a long-standing challenge in manufacturing systems, aiming to locate surface defects and improve product quality. Traditionally, anomaly detection has relied on human inspectors or image- based methods. However, 3d point clouds have gained attention due to their robustness to environmental factors and their ability to represent geometric data. Existing 3d anomaly detection methods generally fall into two categories. One compares scanned 3d point clouds with design files, assuming these files are always available. However, such assumptions are often violated in many real-world applications where model-free products exist, such as fresh produce (i.e., "Cookie", "Bagel", "Potato", etc.), dentures, bone, etc. The other category compares patches of scanned 3d point clouds with a library of normal patches named memory bank. However, those methods usually fail to detect incomplete shapes, which is a fairly common defect type (i.e., missing pieces of different products). The main challenge is that, unlike missing regions in images, which manifest as different pixel values or patterns compared to a normal image patch, missing areas in 3d point clouds represent the absence of scanned points. This makes it infeasible to compare the missing region (with no representation in the recorded 3d Scan) with existing 3d point clouds patches in the memory bank. To address these two challenges, we proposed a unified, unsupervised 3d anomaly detection framework capable of identifying all types of defects on model-free products. Our method integrates two detection modules: a feature-based detection module and a reconstruction-based detection module. Feature-based detection covers geometric defects, such as dents, holes, and cracks, while the reconstruction-based method detects missing regions. Additionally, we employ a One-class Support Vector Machine (OCSVM) to fuse the detection results from both modules. The results demonstrate that (1) our proposed method outperfor

关键词： Unsupervised learning 3d point clouds Anomaly detection

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Joint Spatial and Frequency domain Learning for Lightweight Spectral image demosaicing

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IEEE TRANSACTIONS ON image processing 2025年 34卷 1119-1132页

作者： Wu, Fangfang Huang, Tao Xu, Junwei Cao, Xun dong, Weisheng dong, Le Shi, Guangming Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Yealink Xiamen 361009 Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Peoples R China

Conventional spectral image demosaicing algorithms rely on pixels' spatial or spectral correlations for reconstruction. due to the missing data in the multispectral filter array (MSFA), the estimation of spatial or spectral correlations is inaccurate, leading to poor reconstruction results, and these algorithms are time-consuming. deep learning-based spectral image demosaicing methods directly learn the nonlinear mapping relationship between 2d spectral mosaic images and 3d multispectral images. However, these learning-based methods focused only on learning the mapping relationship in the spatial domain, but neglected valuable image information in the frequency domain, resulting in limited reconstruction quality. To address the above issues, this paper proposes a novel lightweight spectral image demosaicing method based on joint spatial and frequency domain information learning. First, a novel parameter-free spectral image initialization strategy based on the Fourier transform is proposed, which leads to better initialized spectral images and eases the difficulty of subsequent spectral image reconstruction. Furthermore, an efficient spatial-frequency transformer network is proposed, which jointly learns the spatial correlations and the frequency domain characteristics. Compared to existing learning-based spectral image demosaicing methods, the proposed method significantly reduces the number of model parameters and computational complexity. Extensive experiments on simulated and real-world data show that the proposed method notably outperforms existing spectral image demosaicing methods.

关键词： Frequency-domain analysis Imaging image reconstruction Transformers Feature extraction Correlation Fourier transforms Interpolation Three-dimensional displays Optimization Spectral image demosaicing multispectral filter array (MSFA) deep learning spatial domain frequency domain transformer

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CLS-3d: Content-Wise LidAR-Camera Fusion and Slot Reweighting Transformer for 3d Object detection in Autonomous Vehicles

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IEEE ACCESS 2025年 13卷 69840-69856页

作者： Mushtaq, Husnain Latif, Sohaib Bin Ilyas, Muhammad Saad Mohsin, Syed Muhammad Ali, Mohammed Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Grand Asian Univ Dept Comp Sci & Software Engn Sialkot 51040 Pakistan Univ Chenab Dept Comp Sci Gujranwala 50700 Pakistan COMSATS Univ Islamabad Dept Comp Sci Islamabad 45550 Pakistan Virtual Univ Pakistan Stand Coll Modern Sci Lahore 55150 Pakistan King Khalid Univ Dept Comp Sci Abha 61421 Saudi Arabia King Khalid Univ Ctr Artificial Intelligence CAI Abha 61421 Saudi Arabia

Accurate 3d object detection is vital in autonomous driving. Single-modal detectors, using either camera or LidAR, struggle with issues like limited depth perception or difficulty in distinguishing semantically similar objects. While multimodal approaches aim to address these limitations by combining LidAR and camera data, they often face complexities in integrating sparse and uneven point cloud distributions, resulting in inefficient feature fusion. To tackle these challenges, we propose CLS-3d (Content-wise LidAR-Camera Fusion and Slot Reweighting Transformer for 3d Object detection). This novel framework fuses LidAR and camera features using a single multi-modal backbone and augments them with the semantic probabilities obtained from the image stream. Our method captures local and global spatial relationships through a slot reweighting mechanism and incorporates I3C-IoU loss for precise box regression. The semantically augmented features, via a single multi-modal backbone, are embedded using a content-based transformer and processed through a slot-wise auto-encoder structure with channel-wise positional embeddings and a feed-forward MLP network. Our model improves temporal consistency and detection accuracy by dynamically adjusting feature relevance through slot-wise reweighting. We further define a I3C-IoU metric, considering centre, overlap, and scale for enhanced box regression accuracy. This mechanism allows the model to focus on significant temporal information, enhancing its ability to learn complex sequences and improving the overall performance of 3d object detection, especially in challenging scenarios such as occlusion and long-range detection. Extensive experiments on the KITTI and nuScenes benchmark demonstrate that CLS-3d achieves state-of-the-art performance, with 89.52% 3d mAP and 94.08% BEV mAP, outperforming existing methods.

关键词： Three-dimensional displays Object detection Point cloud compression Transformers Feature extraction Accuracy Laser radar Cameras Semantics Object recognition deep learning 3d object detection LidarViT channel attention autonomous vehicles

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PMMNet: A dual Branch Fusion Network of Point Cloud and Multi-View for Intracranial Aneurysm Classification and Segmentation

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IEEE JOURNAL OF BIOMEdICAL ANd HEALTH INFORMATICS 2025年第5期29卷 3137-3147页

作者： Cao, Ruifen Zhang, dongwei Wei, Pijing ding, Yun Zheng, Chunhou Tan, dayu Zhou, Chao Anhui Univ Sch Comp Sci & Technol Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei 230601 Peoples R China Anhui Univ Sch Artificial Intelligence Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Energy Hefei 230031 Anhui Peoples R China

Intracranial aneurysm (IA) is a vascular disease of the brain arteries caused by pathological vascular dilation, which can result in subarachnoid hemorrhage if ruptured. Automatically classification and segmentation of intracranial aneurysms are essential for their diagnosis and treatment. However, the majority of current research is focused on two-dimensional images, ignoring the 3d spatial information that is also critical. In this work, we propose a novel dual-branch fusion network called the Point Cloud and Multi-View Medical Neural Network (PMMNet) for IA classification and segmentation. Specifically, one branch based on 3d point clouds serves the purpose of extracting spatial features, whereas the other branch based on multi-view images acquires 2d pixel features. Ultimately, the two types of features are fused for IA classification and segmentation. To extract both local and global features from 3d point clouds, Multilayer Perceptron (MLP) and the attention mechanism are used in parallel. In addition, a SPSA module is proposed for multi-view image feature learning, which extracts more exquisite channel and spatial multi-scale features from 2d images. Experiments conducted on the IntrA dataset outperform other state-of-the-art methods, demonstrating that the proposed PMMNet exhibits strong superiority on the medical 3d dataset. We also obtain competitive results on public datasets, including ModelNet40, ModelNet10, and ShapeNetPart, which further validate the robustness and generality of the PMMNet.

关键词： Three-dimensional displays Feature extraction Point cloud compression Aneurysm image segmentation data mining Convolutional neural networks 3d data deep learning intracranial aneurysm multi-view point cloud

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Adaptive Attention Based on Mixture distribution for Zero-Shot Non-Line-of-Sight Imaging

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IEEE SIGNAL processing LETTERS 2025年 32卷 1690-1694页

作者： Zhang, Qinghua Liu, Jun duan, Yuping Beijing Normal Univ Sch Math Sci Lab Math & Complex Syst Minist Educ China Beijing 100875 Peoples R China

Non-line-of-sight (NLOS) imaging is an ill-posed problem to reconstruct hidden 3d scenes by leveraging photon time-of-flight information from diffusely reflected light. In the existing regularization models, the spatial residuals were handled by a single distribution, failing to account for the distinct characteristics of background and target objects. In this paper, we propose a novel NLOS reconstruction method that models the non-Gaussian residuals with a mixture distribution. Through a dual method, we derive an adaptive weighted residual model, where the weights generated in the dual space act as a zero-shot attention mechanism to control the contributions of different regions. The corresponding optimization problem can be effectively solved using the alternating minimization algorithm. Numerical experiments on both synthetic and real-world datasets demonstrate that our method surpasses the related existing approaches, achieving state-of-the-art performance.

关键词： image reconstruction Imaging Gold Three-dimensional displays Attention mechanisms Vectors Probability density function Training Signal processing algorithms Lighting Non-line-of-sight imaging mixture distribution duality theory attention mechanism

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Orthoscopic elemental image synthesis for 3d light field display using lens design software and real-world captured neural radiance field

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OPTICS EXPRESS 2024年第5期32卷 7800-7815页

作者： Rabia, Sedick Allain, Guillaume Tremblay, Rosalie Thibault, Simon Univ Laval Ctr Opt Photon & Lasers COPL Quebec City PQ G1V 0A6 Canada

The elemental images (EIs) generation of complex real -world scenes can be challenging for conventional integral imaging (InIm) capture techniques since the pseudoscopic effect, characterized by a depth inversion of the reconstructed 3d scene, occurs in this process. To address this problem, we present in this paper a new approach using a custom neural radiance field (NeRF) model to form real and/or virtual 3d image reconstruction from a complex real -world scene while avoiding distortion and depth inversion. One of the advantages of using a NeRF is that the 3d information of a complex scene (including transparency and reflection) is not stored by meshes or voxel grid but by a neural network that can be queried to extract desired data. The Nerfstudio API was used to generate a custom NeRF-related model while avoiding the need for a bulky acquisition system. A general workflow that includes the use of ray -tracing -based lens design software is proposed to facilitate the different processing steps involved in managing NeRF data. Through this workflow, we introduced a new mapping method for extracting desired data from the custom -trained NeRF-related model, enabling the generation of undistorted orthoscopic EIs. An experimental 3d reconstruction was conducted using an InIm-based 3d light field display (LFd) prototype to validate the effectiveness of the proposed method. A qualitative comparison with the actual real -world scene showed that the 3d reconstructed scene is accurately rendered. The proposed work can be used to manage and render undistorted orthoscopic 3d images from custom -trained NeRF-related models for various InIm applications.

关键词： Aperture synthesis Integral imaging Lens design Optical systems Three dimensional imaging Three dimensional reconstruction

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Occluded object 6d pose estimation using foreground probability compensation

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IET COMPUTER VISION 2024年第8期18卷 1325-1337页

作者： Ren, Meihui Jia, Junying Lu, Xin Shenyang Univ Technol Sch Informat Sci & Engn Shenyang Peoples R China

6d object pose estimation usually refers to acquiring the 6d pose information of 3d objects in the sensor coordinate system using computer vision techniques. However, the task faces numerous challenges due to the complexity of natural scenes. One of the most significant challenges is occlusion, which is an unavoidable situation in 3d scenes and poses a significant obstacle in real-world applications. To tackle this issue, we propose a novel 6d pose estimation algorithm based on RGB-d images, aiming for enhanced robustness in occluded environments. Our approach follows the basic architecture of keypoint-based pose estimation algorithms. To better leverage complementary information of RGB-d data, we introduce a novel foreground probability-guided sampling strategy at the network's input stage. This strategy mitigates the sampling ratio imbalance between foreground and background points due to smaller foreground objects in occluded environments. Moreover, considering the impact of occlusion on semantic segmentation networks, we introduce a new object segmentation module. This module utilises traditional image processing techniques to compensate for severe semantic segmentation errors of deep learning networks. We evaluate our algorithm using the Occlusion LineMOd public dataset. Experimental results demonstrate that our method is more robust in occlusion environments compared to existing state-of-the-art algorithms. It maintains stable performance even in scenarios with no or low occlusion. This paper focuses on the problem of 6d object pose estimation in occluded environments. We propose a novel 6d pose estimation algorithm based on RGB-d images, which utilises conventional image processing techniques to generate a distance map and uses it as the compensation information for pose estimation. Specifically, we introduce a new sampling strategy and add an object segmentation module based on foreground probability to improve the algorithm's robustness to occluded environm

关键词： computer vision image processing pose estimation

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Luojia-HSSR:A high spatial-spectral resolution remote sensing dataset for land-cover classification with a new 3d-HRNet

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Geo-Spatial information Science 2023年第3期26卷 289-301页

作者： Yue Xu Jianya Gong Xin Huang Xiangyun Hu Jiayi Li Qiang Li Min Peng School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina Hubei Luojia Laboratory WuhanChina Institute of Artificial Intelligence in Geomatics Wuhan UniversityWuhanChina Data Center Shenyang Surveying and Mapping Institute Co.LtdShenyangChina

High Spatial and Spectral Resolution(HSSR)remote-sensing images can provide rich spectral bands and detailed ground information,but there is a relative lack of research on this new type of remote-sensing *** there are already some HSSR datasets for deep learning model training and testing,the data volume of these datasets is small,resulting in low classification accuracy and weak generalization ability of the trained *** this paper,an HSSR dataset Luojia-HSSR is constructed based on aerial hyperspectral imagery of southern Shenyang City of Liaoning Province in *** our knowledge,it is the largest HSSR dataset to date,with 6438 pairs of 256×256 sized samples(including 3480 pairs in the training set,2209 pairs in the test set,and 749 pairs in the validation set),covering area of 161 km2 with spatial resolution 0.75 m,249 Visible and Near-Infrared(VNIR)spectral bands,and corresponding to 23 classes of field-validated ground *** is an ideal experimental data for spatial-spectral feature ***,a new deep learning model 3d-HRNet for interpreting HSSR images is *** conv-neck in HRNet is modified to better mine the spatial information of the ***,a 3d convolution module with attention mechanism is designed to capture the global-local fine spectral information ***,the 3d convolution is inserted into the HRNet to optimize the *** experiments show that the 3d-HRNet model has good interpreting ability for the Luojia-HSSR dataset with the Frequency Weighted Intersection over Union(FWIoU)reaching 80.54%,indicating that the Luojia-HSSR dataset constructed in this paper and the proposed 3d-HRnet model have good applicable prospects for processing HSSR remote sensing images.

关键词： High Spatial and Spectral Resolution(HSSR) remotesensing image classification deep learning Convolutional Neural Network(CNN)

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