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Hybrid-supervised deep learning for domain transfer 3D protoacoustic image reconstruction

PHYSICS IN MEDICINE AND BIOLOGY 2024年第8期69卷 085007-085007页

作者： Lang, Yankun Jiang, Zhuoran Sun, Leshan Xiang, Liangzhong Ren, Lei Univ Maryland Dept Radiat Oncol Phys Baltimore MD 21201 USA Duke Univ Dept Radiat Oncol Durham NC 27710 USA Univ Calif Irvine Dept Biomed Engn & Radiol Irvine CA 92617 USA

Objective. Protoacoustic imaging showed great promise in providing real-time 3D dose verification of proton therapy. However, the limited acquisition angle in protoacoustic imaging induces severe artifacts, which impairs its accuracy for dose verification. In this study, we developed a hybrid-supervised deep learning method for protoacoustic imaging to address the limited view issue. Approach. We proposed a Recon-Enhance two-stage deep learning method. In the Recon-stage, a transformer-based network was developed to reconstruct initial pressure maps from raw acoustic signals. The network is trained in a hybrid-supervised approach, where it is first trained using supervision by the iteratively reconstructed pressure map and then fine-tuned using transfer learning and self-supervision based on the data fidelity constraint. In the enhance-stage, a 3D U-net is applied to further enhance the image quality with supervision from the ground truth pressure map. The final protoacoustic images are then converted to dose for proton verification. Main results. The results evaluated on a dataset of 126 prostate cancer patients achieved an average root mean squared errors (RMSE) of 0.0292, and an average structural similarity index measure (SSIM) of 0.9618, out-performing related start-of-the-art methods. Qualitative results also demonstrated that our approach addressed the limit-view issue with more details reconstructed. Dose verification achieved an average RMSE of 0.018, and an average SSIM of 0.9891. Gamma index evaluation demonstrated a high agreement (94.7% and 95.7% for 1%/3 mm and 1%/5 mm) between the predicted and the ground truth dose maps. Notably, the processing time was reduced to 6 s, demonstrating its feasibility for online 3D dose verification for prostate proton therapy. Significance. Our study achieved start-of-the-art performance in the challenging task of direct reconstruction from radiofrequency signals, demonstrating the great promise of PA imaging as a high

关键词： proton therapy dose verification protoacoustic reconstruction self-supervised deep learning

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deep learning-based target decomposition for markerless lung tumor tracking in radiotherapy

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MEDICAL PHYSICS 2024年第6期51卷 4271-4282页

作者： Fu, Yabo Zhang, Pengpeng Fan, Qiyong Cai, Weixing Pham, Hai Rimner, Andreas Cuaron, John Cervino, Laura Moran, Jean M. Li, Tianfang Li, Xiang Mem Sloan Kettering Canc Ctr Dept Med Phys New York NY USA Mem Sloan Kettering Canc Ctr Dept Radiat Oncol New York NY USA Room 1251 650 Commack Rd Smithtown NY 11725 USA

BackgroundIn radiotherapy, real-time tumor tracking can verify tumor position during beam delivery, guide the radiation beam to target the tumor, and reduce the chance of a geometric miss. Markerless kV x-ray image-based tumor tracking is challenging due to the low tumor visibility caused by tumor-obscuring structures. Developing a new method to enhance tumor visibility for real-time tumor tracking is *** introduce a novel method for markerless kV image-based tracking of lung tumors via deep learning-based target *** utilized a conditional Generative Adversarial Network (cGAN), known as Pix2Pix, to build a patient-specific model and generate the synthetic decomposed target image (sDTI) to enhance tumor visibility on the real-time kV projection images acquired by the onboard kV imager equipped on modern linear accelerators. We used 4DCT simulation images to generate the digitally reconstructed radiograph (DRR) and DTI image pairs for model training. We augmented the training dataset by randomly shifting the 4DCT in the superior-inferior, anterior-posterior, and left-right directions during the DRR and DTI generation process. We performed real-time 2D tumor tracking via template matching between the DTI generated from the CT simulation and the sDTI generated from the real-time kV projection images. We validated the proposed method using nine patients' datasets with implanted beacons near the *** sDTI can effectively improve the image contrast around the lung tumors on the kV projection images for the nine patients. With the beacon motion as ground truth, the tracking errors were on average 0.8 +/- 0.7 mm in the superior-inferior (SI) direction and 0.9 +/- 0.8 mm in the in-plane left-right (IPLR) direction. The percentage of successful tracking, defined as a tracking error less than 2 mm in the SI direction, is 92.2% on the 4312 tested images. The patient-specific model took approximately 12 h to train. During testing, i

关键词： markerless tracking x-ray

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Feature Extraction and Classification of Photovoltaic Panels Based on Convolutional Neural Network

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Computers, Materials & Continua 2023年第1期74卷 1437-1455页

作者： S.Prabhakaran R.Annie Uthra J.Preetharoselyn Department of Computational Intelligence SRM Institute of Science and TechnologyChengalpattu603203India Department of Electrical Engineering SRM Institute of Science and TechnologyChengalpattu603203India

Photovoltaic(PV)boards are a perfect way to create eco-friendly power from *** defects in the PV panels are caused by various conditions;such defective PV panels need continuous *** recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV *** general,conventional procedures are used to identify the faulty modules earlier and to avoid declines in power *** existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming *** increase the accuracy and to reduce the processing time,a new Convolutional Neural Network(CNN)architecture is ***,in the present work,a new real-time Multi Variant deep learning Model(RMVDM)architecture is proposed,and it extracts the image features and classifies the defects in PV panels quickly with high *** defects that arise in the PV panels are identified by the CNN based RMVDM using RGB *** biggest difference between CNN and its predecessors is that CNN automatically extracts the image features without any help from a *** technique is quantitatively assessed and compared with existing faulty PV board identification approaches on the large real-time *** results show that 98%of the accuracy and recall values in the fault detection and classification process.

关键词： Photovoltaic panels deep learning defect feature extraction RMVDM

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Comparative study of cloud evolution for rainfall nowcasting using AI-based deep learning algorithms

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JOURNAL OF HYDROLOGY 2024年 639卷

作者： Jiang, Xianqi Chen, Ji Chen, Xunlai Wong, Wai-kin Wang, Mingjie Wang, Shuxin Univ Hong Kong Dept Civil Engn Hong Kong Peoples R China Meteorol Bur Shenzhen Municipal Shenzhen Key Lab Severe Weather South China Shenzhen Peoples R China Hong Kong Observ Hong Kong Peoples R China

It is a critical need to provide timely and valuable alerts of rainstorms and floods to the public. However, it still remains a world-class challenge to achieve serviceable nowcasting rainstorms with even a short lead time of one hour. Different deep learning algorithms have been adopted to improve nowcasting accuracy. Unfortunately, it is still a question which algorithm is more suitable and how to interpret the rainstorm nowcasting results from deep learning. To this end, this paper focuses on modelling the evolution of rainstorm clouds using deep learning algorithms that can be applied to nowcast rainstorms for the next few hours. Adopting three deep learning algorithms, the study provides a detailed analysis of the nowcasting results of three typical cases of different rainfall intensities from a radar echo mosaic image dataset. The dataset was collected in Guangdong, China, and the analysis interprets the performance differences. The analysis further discloses that an AI-based method can provide more skilful nowcasting for medium and strong rainfall cases than for weak ones. Moreover, a deep learning algorithm trained by the dataset for one region can be skilfully used to nowcast rainfall for another region with a similar weather system. This explains the nowcasting capability of deep learning algorithms as well as their robustness. Besides, experiments on the number of iterations reveal that more iterations do not achieve higher nowcasting accuracy. With improved interpretability of deep learning from the perspective of real-world application in the study, it is expected that the algorithms producing higher accuracy and longer lead time nowcasts will be made possible.

关键词： Zhang Associate Editor?

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The analysis of rural tourism image optimization under the internet of things and deep learning

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SCIENTIFIC REPORTS 2024年第1期14卷 1-25页

作者： Wang, Xinghua Tourism Coll Changchun Univ Changchun 130607 Jilin Peoples R China Northeast Asia Res Ctr Leisure Econ Changchun 130607 Jilin Peoples R China Jilin Prov Res Ctr Cultural Tourism Educ & Enterp Changchun 130607 Jilin Peoples R China Changchun Ind Convergence Res Ctr Culture & Touri Changchun 130607 Jilin Peoples R China Changchun Ice & Snow Ind Res Inst Changchun 130607 Jilin Peoples R China

This study aims to utilize deep learning technology to optimize rural tourism image, enhance visitor experience, and promote sustainable development. By deploying sensors for real-time monitoring of the environment and visitor flow in rural scenic areas, combined with a Dense Convolutional Neural Network (DenseNet), automatic identification and analysis of rural landscapes are achieved. Using rural tourism along the Yellow River as a case study, this study constructs a tourism image evaluation and optimization model based on big data. The results indicate that the model performs excellently in terms of accuracy and robustness, significantly improving the presentation of rural tourism images. The study shows that realism and service facilities have the greatest impact on rural tourism image, underscoring the value of technological means in optimizing the rural tourism image.

关键词： Rural tourism image Internet of things deep learning Dense convolutional neural network Integrated development

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IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4354-4363页

作者： Roy, Ayush Mohiuddin, S.K. Sarkar, Ram Jadavpur University Department of Electrical Engineering Kolkata700032 India Asutosh College Department of Computer Science Kolkata700026 India Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

The process of modifying digital images has been made significantly easier by the availability of several image editing software. However, in a variety of contexts, including journalism, judicial processes, and historical documentation, the authenticity of images is of utmost importance. In particular, copy-move forgery is a distinct type of image manipulation, where a portion of an image is copied and pasted into another area of the same image, creating a fictitious or altered version of the original. In this research, we present a lightweight MultiResUnet architecture with the similarity-based positional attention module (SPAM) attention module for copy-move forgery detection (CMFD). By using a similarity measure across the patches of the features, this attention module identifies the patches, where a forged region is present. The lightweight network also aids in resource-efficient training and transforms the model into one that can be used in real time. We have employed four commonly used but extremely difficult CMFD datasets, namely CoMoFoD, COVERAGE, CASIA v2, and MICC-F600, to assess the effectiveness of our model. The proposed model significantly lowers false positives, thereby improving the pixel-level accuracy and dependability of CMFD tools. © 2020 IEEE.

关键词： real time systems

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Self-FuseNet: Data Free Unsupervised Remote Sensing image Super-Resolution

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 1710-1727页

作者： Mishra, Divya Hadar, Ofer Ben Gur Univ Negev Sch Elect & Comp Engn Beer Sheva Israel

real-world degradations deviate from ideal degradations, as most deep learning-based scenarios involve the ideal synthesis of low-resolution (LR) counterpart images by popularly used bicubic interpolation. Moreover, supervised learning approaches rely on many high-resolution (HR) and LR image pairings to reconstruct missing information based on their association, developed by complex long hours of deep neural network training. Additionally, the trained model's generalizability on various image datasets with various distributions is not guaranteed. To overcome this challenge, we proposed our novel Self-FuseNet, particularly for extremely poor-resolution satellite images. Also, the network exhibits strong generalization performance on additional datasets (both "ideal " and "nonideal " scenarios). The network is especially for those image datasets suffering from the following two significant limitations: 1) nonavailability of ground truth HR images;2) limitation of a large count of the unpaired dataset for deep neural network training. The benefit of the proposed model is threefold: 1) it does not require any significant extensive training data, either paired or unpaired but only a single LR image without prior knowledge of its distribution;2) it is a simple and effective model for super-resolving very poor-resolution images, saving computational resources and time;3) using UNet, the processing of data are accelerated by the network's wide skip connections, allowing image reconstruction with fewer parameters. Rather than using an inverse approach, as common in most deep learning scenarios, we introduced a forward approach to super-resolve exceptionally LR remote sensing images. This demonstrates its supremacy over recently proposed state-of-the-art methods for unsupervised single real-world image blind super-resolution.

关键词： Feature extraction Superresolution Satellites Training image reconstruction Spatial resolution deep learning Blind image super-resolution (SR) data fusion deep learning self-fusion unsupervised image super-resolution (SR)

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GPU-based key-frame selection of pulmonary ultrasound images to detect COVID-19

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JOURNAL OF real-time image processing 2024年第4期21卷 1-14页

作者： Torti, Emanuele Gazzoni, Marco Marenzi, Elisa Leporati, Francesco Univ Pavia Dept Comp Elect & Biomed Engn Via Ferrata 5 I-27100 Pavia Italy

In the last decades, technological advances have led to a considerable increase in computing power constraints to simulate complex phenomena in various application fields, among which are climate, physics, genomics and medical diagnosis. Often, accurate results in real time, or quasi real time, are needed, especially if related to a process requiring rapid interventions. To deal with such demands, more sophisticated approaches have been designed, including GPUs, multicore processors and hardware accelerators. Supercomputers manage high amounts of data at a very high speed;however, despite their considerable performance, their limitations are due to maintenance costs, rapid obsolescence and notable energy consumption. New processing architectures and GPUs in the medical field can provide diagnostic and therapeutic support whenever the patient is subject to risk. In this context, image processing as an aid to diagnosis, in particular pulmonary ultrasound to detect COVID-19, represents a promising diagnostic tool with the ability to discriminate between different degrees of disease. This technique has several advantages, such as no radiation exposure, low costs, the availability of follow-up tests and the ease of use even with limited resources. This work aims to identify the best approach to optimize and parallelize the selection of the most significant frames of a video which is given as the input to the classification network that will differentiate between healthy and COVID patients. Three approaches have been evaluated: histogram, entropy and ResNet-50, followed by a K-means clustering. Results highlight the third approach as the most accurate, simultaneously showing GPUs significantly lowering all processing times.

关键词： ResNet K-means High-performance computing Key-frame selection Artificial intelligence Machine learning

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Self-attention residual network-based spatial super-resolution synthesis for time-varying volumetric data

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IET image processing 2024年第6期18卷 1579-1597页

作者： Ma, Ji Ye, Yuhao Chen, Jinjin Zhejiang Univ Technol Sch Comp Sci & Technol Hangzhou Peoples R China Commun Univ Zhejiang Sch Design & Art Hangzhou Peoples R China Macau Univ Sci & Technol Fac Humanities & Arts Macau Peoples R China

In the field of scientific visualization, the upscaling of time-varying volume is meaningful. It can be used in in situ visualization to help scientists overcome the limitations of I/O speed and storage capacity when analysing and visualizing large-scale, time-varying simulation data. This paper proposes self-attention residual network-based spatial super-resolution (SARN-SSR), a spatial super-resolution model based on self-attention residual networks that can generate time-varying data with temporal coherence. SARN-SSR consists of two components: a generator and a discriminator. The generator takes the low-resolution volume sequences as the input and gives the corresponding high-resolution volume sequences as the output. The discriminator takes both synthesized and real high-resolution volume sequence as the input and gives a matrix to predict the realness as the output. To verify the validity of SARN-SSR, four sets of time-varying volume datasets are applied from scientific simulation. In addition, SARN-SSR is compared on these datasets, both qualitatively and quantitatively, with two deep learning-based techniques and one traditional technique. The experimental results show that by using this method, the closest time-varying data to the ground truth can be obtained. This paper proposes a novel self-attention residual network-based spatial super-resolution (SARN-SSR) framework for upscaling time-varying volume data in scientific visualization. It utilizes a generator and discriminator based on generative adversarial networks to generate high-resolution volume sequences. Comparative evaluations demonstrate that SARN-SSR outperforms state-of-the-art techniques in generating accurate time-varying volume datasets. image

关键词： computer graphics data visualisation image processing

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real-time Multi-Object Detection Using Enhanced Yolov5-7S on Multi-GPU for High-Resolution Video

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INTERNATIONAL JOURNAL OF image AND GRAPHICS 2024年第2期24卷

作者： Shaikh, Shakil A. Chopade, Jayant J. Sardey, Mohini Pramod Matoshri Coll Engn & Res Ctr Dept Elect & Telecommun Nasik India Savitribai Phule Pune Univ Pune Maharashtra India AISSMS IOIT Dept Elect & Telecommun Pune India

Multiple objects tracking in a video sequence can be performed by detecting and distinguishing the objects that appear in the sequence. In the context of computer vision, the robust multi-object tracking problem is a difficult problem to solve. Visual tracking of multiple objects is a vital part of an autonomous driving vehicle's vision technology. Wide-area video surveillance is increasingly using advanced imaging devices with increased megapixel resolution and increased frame rates. As a result, there is a huge increase in demand for high-performance computation system of video surveillance systems for real-time processing of high-resolution videos. As a result, in this paper, we used a single stage framework to solve the MOT problem. We proposed a novel architecture in this paper that allows for the efficient use of one and multiple GPUs are used to process Full High Definition video in real time. For high-resolution video and images, the suggested approach is real-time multi-object detection based on Enhanced Yolov5-7S on Multi-GPU Vertex. We added one more layer at the top in backbone to increase the resolution of feature extracted image to detect small object and increase the accuracy of model. In terms of speed and accuracy, our proposed approach outperforms the state-of-the-art techniques.

关键词： Multi-object tracking YOLOv5 GPU_NVIDIA object detection deep learning computer vision

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