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NEAT-Net: An Unsupervised Cross-View Prior Inpainting Network for CBCT Metal Artifact Reduction

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NEAT-Net: An Unsupervised Cross-View Prior Inpainting Networ...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yang Yang Zhan Tong Xinyun Zhong Yijie Shi Yan Xi Wenxue Yu Zhan Wu Yang Chen Laboratory of Image Science and Technology the School of Computer Science and Engineering Southeast University Nanjing China First-Imaging Tech Shanghai China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Intraoperative Cone-Beam Computed Tomography (CBCT) facilitates intraoperative navigation for Minimally Invasive Spine Surgery (MISS). However, high-attenuation metal implants used in MISS often cause metal artifacts in the reconstructed CBCT images. Current algorithms do not consider the cross-view information in the projection-domain for metal artifact reduction (MAR). Inaccurate projection-domain inpainting results in CBCT MAR lead to tissue blurring and secondary artifacts, significantly compromising the accuracy of CBCT-guided MISS and increasing surgical risks. To address the above challenge, in this paper, we propose a novel unsupervised cross-view prior inpainting network for CBCT Metal Artifact Reduction named NEAT-Net. Firstly, a cross-view prior multi-scale inpainting module is constructed to learn the inter-view complementary information. Secondly, a hybrid feature attention module is proposed to adaptively fuse cross-view features. In addition, an unsupervised training approach is proposed to directly learn from metal-affected data. Extensive experiments are conducted to verify the effectiveness of our algorithm on a real clinical dataset.

关键词： Training Minimally invasive surgery Accuracy Navigation Fuses Computed tomography Metals Implants Engineering in medicine and biology image reconstruction

Infectious Probability Analysis on COVID-19 Spreading with Wireless Edge Networks

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arXiv 2022年

作者： Li, Xuran Guo, Shuaishuai Dai, Hong-Ning Li, Dengwang Shandong Key Laboratory of Medical Physics and Image Processing School of Physics and Electronics Shandong Normal University Jinan250061 China School of Control Science and Engineering Shandong University Jinan250061 China Shandong Provincial Key Laboratory of Wireless Communication Technologies Jinan China The Department of Computer Science Hong Kong Baptist University Hong Kong

The emergence of infectious disease COVID-19 has challenged and changed the world in an unprecedented manner. The integration of wireless networks with edge computing (namely wireless edge networks) brings opportunities to address this crisis. In this paper, we aim to investigate the prediction of the infectious probability and propose precautionary measures against COVID-19 with the assistance of wireless edge networks. Due to the availability of the recorded detention time and the density of individuals within a wireless edge network, we propose a stochastic geometry-based method to analyze the infectious probability of individuals. The proposed method can well keep the privacy of individuals in the system since it does not require to know the location or trajectory of each individual. Moreover, we also consider three types of mobility models and the static model of individuals. Numerical results show that analytical results well match with simulation results, thereby validating the accuracy of the proposed model. Moreover, numerical results also offer many insightful implications. Thereafter, we also offer a number of countermeasures against the spread of COVID-19 based on wireless edge networks. This study lays the foundation toward predicting the infectious risk in realistic environment and points out directions in mitigating the spread of infectious diseases with the aid of wireless edge networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

On the Performance Trade-off of Distributed Integrated Sensing and communication Networks

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arXiv 2023年

作者： Li, Xuran Guo, Shuaishuai Li, Tuo Zou, Xiaofeng Li, Dengwang Shandong Key Laboratory of Medical Physics and Image Processing School of Physics and Electronics Shandong Normal University Jinan250061 China School of Control Science and Engineering Shandong University Jinan250061 China Shandong Provincial Key Laboratory of Wireless Communication Technologies China Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Co. Ltd. China

In this letter, we analyze the performance tradeoff in distributed integrated sensing and communication (ISAC) networks. Specifically, with the aid of stochastic geometry theory, we derive the probability of detection of that of the coverage given user number. Based on the analytical derivations, we provide a quantitative description of the performance limits and the performance trade-off between sensing and communication in a distributed ISAC network under the given transmit power and bandwidth budget. Extensive simulations are conducted and the numerical results validate the accuracy of our derivations. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

A multi-stage semi-supervised learning for ankle fracture classification on CT images

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arXiv 2024年

作者： Liu, Hongzhi Li, Guicheng Nie, Jiacheng Tang, Hui Yang, Chunfeng Feng, Qianjin Xu, Hailin Chen, Yang The School of Computer Science and Engineering Southeast University Nanjing210096 China The Peking University People's Hospital Beijing100000 China The Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University Guangzhou510515 China The Key Laboratory of New Generation Artificial Intelligence Technology and its Interdisciplinary Applications Southeast University Ministry of Education China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China

Because of the complicated mechanism of ankle injury, it is very difficult to diagnose ankle fracture in clinic. In order to simplify the process of fracture diagnosis, an automatic diagnosis model of ankle fracture was proposed. Firstly, a tibia-fibula segmentation network is proposed for the joint tibiofibular region of the ankle joint, and the corresponding segmentation dataset is established on the basis of fracture data. Secondly, the image registration method is used to register the bone segmentation mask with the normal bone mask. Finally, a semi-supervised classifier is constructed to make full use of a large number of unlabeled data to classify ankle fractures. Experiments show that the proposed method can segment fractures with fracture lines accurately and has better performance than the general method. At the same time, this method is superior to classification network in several indexes. Copyright © 2024, The Authors. All rights reserved.

关键词： image classification

FULL-LENGTH-BODY CBCT IMAGING IN UPRIGHT POSITION WITH ROBOTIC-ARM SYSTEM: A SIMULATION STUDY

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arXiv 2023年

作者： Lin, Tong Lyu, Tianling Wu, Zhan Xi, Yan Zhu, Wentao Chen, Yang Laboratory of Image Science and Technology Southeast University Nanjing China Research Center of Augmented Intelligence Zhejiang Lab Hangzhou China Shanghai First-Imaging Tech Shanghai China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

Upright position CT scans make it possible for full-length-body imaging at conditions more relevant to daily situations, but the substantial weight of the upright CT scanners increases the risks to floor’s stability and patients’ safety. Robotic-arm CBCT systems are supposed to be a better solution for this task, but such systems still face challenges including long scanning time and low reconstruction quality. To address the above challenges, this paper proposes a novel method to calculate optimal scanning pitch based on data completeness analysis, which can complete the whole-body scan in the shortest time without a significant decline in image quality. Besides, an FDK-style reconstruction method based on normalized projections is proposed to obtain fast image reconstruction. Extensive experiments prove the effectiveness of the proposed optimal scanning trajectory. Qualitative and quantitative comparisons with FDK and iterative algorithms show that the proposed reconstruction method can obtain high imaging quality with reasonable computation costs. The method proposed in this paper is expected to promote the application of robotic-arm CBCT systems in orthopedic functional analysis. © 2023, CC BY-NC-SA.

关键词： Robotic arms

Intra-Guided Dual-Energy Material Decomposition Method for Dual-Source CBCT Systems

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Intra-Guided Dual-Energy Material Decomposition Method for D...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xinyun Zhong Tianling Lyu Zhan Wu Wei Gao Yan Xi Wei Zhao Wentao Zhu Yang Chen Laboratory of Image Science and Technology School of Computer Science and Engineering Southeast University Nanjing China Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Beihang University Beijing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Dual-energy CBCT imaging plays a crucial role in advanced imaging applications due to its ability to quantify material components. Although there are multiple established systems for dual-energy imaging, they often come with high deployment costs. This study investigates the feasibility of dual-energy material decomposition on a less expensive dual-source CBCT system. Due to the specific geometry of dual-source CBCT, cone-beam (CB) artifacts seriously degrade the material decomposition quality when the direct inversion method is adopted. In this paper, a novel dual-source intra-guided material decomposition framework is proposed to suppress CB artifacts in the material maps. Firstly, a dual-source two-pass algorithm is developed for artifact pre-correction during image reconstruction. Then, the structure of small cone-angle regions from one X-ray source is utilized to guide cone beam artifact removal in large cone-angle regions from another source. Finally, direct matrix inverse material decomposition is applied to the filtered image. Extensive experiments have been conducted on the authentic human phantom and demonstrate that the proposed framework achieves excellent performance in cone beam artifact reduction compared to other methods, and significantly enhances the quality of the material maps.

关键词： Geometry Head Costs Imaging Phantoms Filtering algorithms Matrix decomposition Engineering in medicine and biology image reconstruction X-ray imaging

Full-Length-Body CBCT Imaging in Upright Position with Robotic-arm System: A Simulation Study

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Full-Length-Body CBCT Imaging in Upright Position with Robot...

IEEE International Symposium on Biomedical Imaging

作者： Tong Lin Tianling Lyu Zhan Wu Yan Xi Wentao Zhu Yang Chen Laboratory of Image Science and Technology Southeast University Nanjing China Research Center of Augmented Intelligence Zhejiang Lab Hangzhou China Shanghai First-Imaging Tech Shanghai China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

关键词： image quality Computed tomography Reconstruction algorithms Stability analysis Trajectory Safety Iterative methods

HU-Net: Hessian-incorporated U-Net for CBCT projection-domain metal segmentation with guidance from SAM

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HU-Net: Hessian-incorporated U-Net for CBCT projection-domai...

IEEE Conference on Cybernetics and Intelligent Systems

作者： Chen Jiang Tianling Lyu Gege Ma Yan Xi Yang Chen Wentao Zhu Zhejiang Lab Hangzhou China Hangzhou Institute for Advanced Study of UCAS Hangzhou China Shanghai First-Imaging Tech. Shanghai China Laboratory of Image Science and Technology Southeast University Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Metal artifact is a prevailing factor reducing the image quality of CBCT in minimally invasive spine surgery. In CBCT, conventional metal artifact reduction algorithms pay more attention to the inpainting of the metal traces, but metal segmentation is also challenging. Despite the current success in image segmentation with deep learning, the substantial expense associated with annotating metal traces in the projection domain makes most of these approaches impractical for this task. To address this, we propose a Hessian-incorporated U-Net (HU-Net) for CBCT projection-domain metal segmentation with guidance from SAM. The proposed method has been tested on both digital phantom data and clinical CBCT data. According to the experimental results, our method has demonstrated notable improvement in the segmentation of metal traces across both datasets. This paper presents a feasible approach for metal trace segmentation in the projection domain with deep learning and has the potential to be applied in clinical applications.

关键词： Deep learning image quality image segmentation Minimally invasive surgery Mechatronics Metals Phantoms

Slnspeech: Solving Extended Speech Separation Problem with the Help of Sign Language

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SSRN

SSRN 2023年

作者： Wu, Jiasong Li, Xuan Li, Taotao Meng, Fanman Kong, Youyong Yang, Guanyu Senhadji, Lotfi Shu, Huazhong Laboratory of Image Science and Technology Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing210096 China Univ Rennes INSERM LTSI - UMR 1099 RennesF-35000 France Univ Rennes INSERM RennesF-35000 France

Previous audio-visual speech separation methods use the synchronization of the speaker's facial movement and speech in the video to supervise the speech separation in a self-supervised way. In this paper, we propose a model to solve the speech separation problem assisted by both face and sign language, which we call the extended speech separation problem. We design a general deep learning network for learning the combination of three modalities, audio, face, and sign language information, for better solving the speech separation problem. To train the model, we introduce a large-scale dataset named the Sign Language News Speech (SLNSpeech) dataset in which three modalities of audio, face, and sign language coexist. Experiment results show that the proposed model has better performance and robustness than the usual audio-visual system. Besides, sign language modality can also be used alone to supervise speech separation task, and the introduction of sign language is helpful for hearing-impaired people to learn and communicate. Last, our model is a general speech separation framework and can achieve very competitive separation performance on two open-source audio-visual datasets. The code is available at https://***/iveveive/SLNSpeech. © 2023, The Authors. All rights reserved.

关键词： Audition