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arXiv 2021年

作者： Luo, Xiaoyuan Liu, Shaolei Fu, Kexue Wang, Manning Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention

Deep neural networks have achieved promising performance in supervised point cloud applications, but manual annotation is extremely expensive and time-consuming in supervised learning schemes. Unsupervised domain adaptation (UDA) addresses this problem by training a model with only labeled data in the source domain but making the model generalize well in the target domain. Existing studies show that self-supervised learning using both source and target domain data can help improve the adaptability of trained models, but they all rely on hand-crafted designs of the self-supervised tasks. In this paper, we propose a learnable self-supervised task and integrate it into a self-supervision-based point cloud UDA architecture. Specifically, we propose a learnable nonlinear transformation that transforms a part of a point cloud to generate abundant and complicated point clouds while retaining the original semantic information, and the proposed self-supervised task is to reconstruct the original point cloud from the transformed ones. In the UDA architecture, an encoder is shared between the networks for the self-supervised task and the main task of point cloud classification or segmentation, so that the encoder can be trained to extract features suitable for both the source and the target domain data. Experiments on PointDA-10 and PointSegDA datasets show that the proposed method achieves new state-of-the-art performance on both classification and segmentation tasks of point cloud UDA. Code will be made publicly available. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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Shape Estimation of Flexible Probe using Spatial-Attention FlexShapeNet

Shape Estimation of Flexible Probe using Spatial-Attention F...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Xue Gao Lihong Huang Yuanyuan Wang Yi Guo Department of Electronic Engineering Fudan University Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai China

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Ultrasound imaging with flexible probes requires the knowledge of element array geometry, which is variable and unknown. Here, we propose a spatial-attention shape estimation model, named FlexShapeNet, to estimate accurate element array geometry of flexible probes from radio-frequency (RF) data. A spatial attention (SA) module is introduced to capture deformation information resulting from variations in the shape of the flexible probe, which effectively reduces the significant disparities in shape estimation accuracy among different imaging targets. The delay-and-sum (DAS) beamformer is then used to reconstruct ultrasound images with estimated shape and the raw RF data. Experimental results demonstrate that FlexShapeNet achieves the highest shape estimation accuracy for flexible probes without the need for multiple iterations of beamforming.

关键词： Radio frequency Geometry Accuracy Ultrasonic imaging Shape Deformation Estimation Imaging Arrays Probes

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Adaptive scaled Wiener postfilter beamformer for ultrasound imaging

Adaptive scaled Wiener postfilter beamformer for ultrasound ...

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URSI Asia-Pacific Radio Science Conference (URSI AP-RASC)

作者： Saeid Aliabadi Yuanyuan Wang Jinhua Yu Department of Electronic Engineering Fudan University Shanghai China The key laboratory of medical imaging computing and computer assisted intervention of shanghai Shanghai China

In ultrasound imaging Wiener postfilter and coherent factor (CF) concepts were combined with minimum variance (MV) and eigenspace-based MV (ESBMV) beamformers to future suppress sidelobes and to improve the imaging contrast and the spatial resolution. CF, which can be considered as a scaled Wiener postfilter with high noise power weighting, provides high imaging resolution. The high resolution, however, is achieved with the contrast as a sacrifice. On other hand, Wiener postfilter, provides better signal intensity restoration and imaging contrast than CF but with lower imaging resolution. To pursue the optimal imaging contrast of Wiener postfilter and the high resolution of CF at the same time, an adaptive scaled Wiener postfilter beamformer (ASC-Wiener) is proposed. In proposed method, the noise power weighting of Wiener postfilter is adjusted adaptively so that related to the output SNR inversely needless to estimate the input SNR. Simulation and real data experiment for target point imaging show the high performance of the proposed beamformer in terms of the imaging resolution and sidelobes suppression. Cyst phantom imaging also shows that the ASC-Wiener preserves the speckle intensity and improves the imaging contrast. The contrast ratio (CR) and the contrast-to-noise ratio (CNR) are improved by 6.2/0.35 dB over ESBMV-Wiener and by 20.7/0.65 over ESBMV-CF. These results solidly prove that the imaging contrast and the imaging resolution are improved significantly and simultaneously.

关键词： Imaging Radar imaging image resolution Signal resolution Meteorology Ultrasonic imaging

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Improvement of ultrasound contrast imaging with adaptive beamformer based on pulse inversion plane wave transmission

Improvement of ultrasound contrast imaging with adaptive bea...

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URSI Asia-Pacific Radio Science Conference (URSI AP-RASC)

作者： Yurong Huang Jinhua Yu Yuanyuan Wang Shuying Li Yi Chen Department of Electronic Engineering Fudan University Shanghai China The key laboratory of medical imaging computing and computer assisted intervention of shanghai Shanghai China

Study of ultrasound contrast imaging is of great importance in recent years thanks to its wide application in ultrasonic diagnosis. Plane wave imaging has been considered as a good method with high frame rate and low mechanical index. High frame rate can improve the temporal resolution of contrast imaging. Meanwhile, low mechanical index is essential to contrast imaging since high mechanical index in traditional B-mode imaging could break micro-bubbles easily. However, plane wave contrast imaging suffers from low signal-to-noise ratio (SNR) and contrast (CR) which leads to poor image quality. In this paper, adaptive beamformer and pulse inversion (PI) technique were combined to solve the shortcomings of the plane wave contrast imaging. For the adaptive beamformer, eigenspace based minimum variance (ESBMV) with coherence factor (ESBMVCF) was proposed. Experimental results show that ESBMVCF offers an enhancement of 1.1dB, 13dB and 0.8dB in CR, contrast-to-tissue ratio (CTR) and contrast-to-noise ratio (CNR) respectively compared with minimum variance and coherence factor (MVCF). Besides, PI offers 1.5dB, 15dB and 15dB enhancement in CR, CTR and CNR compared with traditional pulse-echo imaging.

关键词： Radar imaging Meteorology Imaging Meteorological radar image resolution Radar antennas

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Ventricular Wave Feature Extraction of ECG Signal based on Synthesized Algorithm

Ventricular Wave Feature Extraction of ECG Signal based on S...

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2015 Global Conference on Biological Engineering and Biomedical(CBEB 2015)

作者： Wang Cong Wu Xiaomei Department of Electric Engineering School of InformationFudan University Digital Medical Research Center of Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention

Electrocardiogram(ECG) is a record of the electrical activity of the heart. Using computer to extract feature points of ventricular wave of ECG automatically is of great significance, for it can indicate plenty of heart diseases. In this paper,a synthesized algorithm is proposed to analyse ventricular wave of ECG. After multiresolution wavelet decomposition,QRS complex and T wave are reconstructed with different coefficients based on the spectrum characteristics of different portion of ECG. Self-adaptive threshold is applied in peak detection. Maximum and minimum modulus is used to obtain QRS onset and offset. Area integration is adopted to acquire T offset. This synthesized algorithm achieves a preferable detection result in QT database of Physionet with higher accuracy and precision, which will lay a better foundation for analysis and prediction of ventricular heart diseases.

关键词： ventricular wave of ECG signal analysis,feature extraction,synthesized algorithm

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The theoretical and experimental analysis of ultrasound contrast agent micro bubbles: The Sonovue

The theoretical and experimental analysis of ultrasound cont...

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International Conference on Biomedical Engineering and Informatics (BMEI)

作者： Shuying Li Yurong Huang Yi Cheng Jinhua Yu Yuanyuan Wang Department of Electronic Engineering Fudan University Shanghai China The key laboratory of medical imaging computing and computer assisted intervention of shanghai China

Sonovue is a kind of ultrasound contrast agent (UCA) with a stable shell made by lipid, within which is SF6. Due to the specific acoustic characteristics, UCA has been widely used in ultrasound diagnosis and therapy. In some cases, the 2nd harmonic wave of the scattered signal should be strong enough to help form a high quality image. To better utilize the bubbles to get enough intensity of the 2nd harmonic wave of the scattered signal, numerical simulation and corresponding experiments based on the several models proposed before have been analyzed to study the behavior of the bubbles. In this paper, we use the ratio of the second harmonic wave and fundamental wave to evaluate the oscillation of the bubbles. Besides, we have compared different simulation models and designed corresponding platform of phantom study. Finally, we find that the second harmonic wave will get a remarkable growth when the driving pressure is under 150KPa in the simulation or under 200KPa in the experiment.

关键词： Mathematical model Harmonic analysis Numerical models Acoustics Liquids Phantoms Ultrasonic imaging

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Evaluation of fatty proportion in fatty liver using least squares method with constraints

Evaluation of fatty proportion in fatty liver using least sq...

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作者： Li, Xingsong Deng, Yinhui Yu, Jinhua Wang, Yuanyuan Shamdasani, Vijay Department of Electronic Engineering Fudan University HenglongPhysic Building Room 212 Shanghai200433 China Department of Healthcare Philips Research China Shanghai200233 China Key Laboratory of Medical Imaging Computing Computer Assisted Intervention of Shanghai China Department of Healthcare Philips Research China Bothell United States

Backscatter and attenuation parameters are not easily measured in clinical applications due to tissue inhomogeneity in the region of interest (ROI). A least squares method(LSM) that fits the echo signal power spectra from a ROI to a 3- parameter tissue model was used to get attenuation coefficient imaging in fatty liver. Since fat's attenuation value is higher than normal liver parenchyma, a reasonable threshold was chosen to evaluate the fatty proportion in fatty liver. Experimental results using clinical data of fatty liver illustrate that the least squares method can get accurate attenuation estimates. It is proved that the attenuation values have a positive correlation with the fatty proportion, which can be used to evaluate the syndrome of fatty liver. © 2014 - IOS Press and the authors.

关键词： Diseases

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POS-BERT: Point Cloud One-Stage BERT Pre-Training

arXiv

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arXiv 2022年

作者： Fu, Kexue Gao, Peng Liu, Shaolei Zhang, Renrui Qiao, Yu Wang, Manning Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai AI Lab China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China

Recently, the pre-training paradigm combining Transformer and masked language modeling has achieved tremendous success in NLP, images, and point clouds, such as BERT. However, directly extending BERT from NLP to point clouds requires training a fixed discrete Variational AutoEncoder (dVAE) before pre-training, which results in a complex two-stage method called Point-BERT. Inspired by BERT and MoCo, we propose POS-BERT, a one-stage BERT pre-training method for point clouds. Specifically, we use the mask patch modeling (MPM) task to perform point cloud pre-training, which aims to recover masked patches information under the supervision of the corresponding tokenizer output. Unlike Point-BERT, its tokenizer is extra-trained and frozen. We propose to use the dynamically updated momentum encoder as the tokenizer, which is updated and outputs the dynamic supervision signal along with the training process. Further, in order to learn high-level semantic representation, we combine contrastive learning to maximize the class token consistency between different transformation point clouds. Extensive experiments have demonstrated that POS-BERT can extract high-quality pre-training features and promote downstream tasks to improve performance. Using the pretraining model without any fine-tuning to extract features and train linear SVM on ModelNet40, POS-BERT achieves the state-of-the-art classification accuracy, which exceeds Point-BERT by 3.5%. In addition, our approach has significantly improved many downstream tasks, such as fine-tuned classification, few-shot classification, part segmentation. The code and trained-models will be available at: https://***/fukexue/POS-BERT. Copyright © 2022, The Authors. All rights reserved.

关键词： Natural language processing systems

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Robust point cloud registration framework based on deep graph matching

arXiv

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arXiv 2021年

作者： Fu, Kexue Liu, Shaolei Luo, Xiaoyuan Wang, Manning Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention China

3D point cloud registration is a fundamental problem in computer vision and robotics. There has been extensive research in this area, but existing methods meet great challenges in situations with a large proportion of outliers and time constraints, but without good transformation initialization. Recently, a series of learning-based algorithms have been introduced and show advantages in speed. Many of them are based on correspondences between the two point clouds, so they do not rely on transformation initialization. However, these learning-based methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by singular value decomposition. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on registering clean, noisy, partial-to-partial and unseen category point clouds show that the proposed method achieves state-of-the-art performance. The code will be made publicly available at https://***/fukexue/RGM. Copyright © 2021, The Authors. All rights reserved.

关键词： Surface measurement

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Chest-Diffusion: A Light-Weight Text-to-image Model for Report-to-CXR Generation

Chest-Diffusion: A Light-Weight Text-to-Image Model for Repo...

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IEEE International Symposium on Biomedical Imaging

作者： Peng Huang Xue Gao Lihong Huang Jing Jiao Xiaokang Li Yuanyuan Wang Yi Guo Department of Electronic Engineering Fudan University Shanghai China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Text-to-image generation has important implications for generation of diverse and controllable images. Several attempts have been made to adapt Stable Diffusion (SD) to the medical domain. However, the large distribution difference between medical reports and natural texts, as well as high computational complexity in common stable diffusion limit the authenticity and feasibility of the generated medical images. To solve above problems, we propose a novel light-weight transformer-based diffusion model learning framework, Chest-Diffusion, for report-to-CXR generation. Chest-Diffusion employs a domain-specific text encoder to obtain accurate and expressive text features to guide image generation, improving the authenticity of the generated images. Meanwhile, we introduce a light-weight transformer architecture as the denoising model, reducing the computational complexity of the diffusion model. Experiments demonstrate that our Chest-Diffusion achieves the lowest FID score 24.456, under the computation budget of 118.918 GFLOPs, which is nearly one-third of the computational complexity of SD.

关键词： Accuracy Computational modeling Noise reduction Text to image computer architecture Transformers Diffusion models

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