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Abdominal computed tomography localizer image generation: A deep learning approach

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022年 214卷 106575-106575页

作者： Liu, Zongxi Zhao, Huimin Fang, Xiang Huo, Donglai Univ Wisconsin Sheldon B Lubar Sch Business 3202 N Maryland Ave Milwaukee WI 53201 USA Univ Colorado Sch Med Dept Radiol 13001 East 17th Pl Aurora CO 80045 USA

Background and Objective: Computed Tomography (CT) has become an important clinical imaging modality, as well as the leading source of radiation dose from medical imaging procedures. Modern CT exams are usually led by two quick orthogonal localization scans, which are used for patient positioning and diagnostic scan parameter definition. These two localization scans contribute to the patient dose but are not used for diagnosis purposes. In this study, we investigate the possibility of using deep learning models to reconstruct one localization scan image from the other, thus reducing the patient dose and simplifying the clinical workflow. Methods: We propose a modified encoder-decoder network and a scaled mixture loss function specifically for the focal task. In this study, 12,487 clinical abdominal exams were retrieved from a clinical medical imaging storage system and randomly split for training, validation, and test in the ratio of 7:1:2. Reconstructed images were compared with the ground truth in terms of location prediction error, profile prediction error, and attenuation prediction error. Results: The average location error, profile error, and attenuation error were 1.02 +/- 3.37 mm, 4.43 +/- 2.02%, and 6.2 +/- 2.94% for lateral prediction, and 6.46 +/- 6.43 mm, 3.9 +/- 2.32%, and 7.12 +/- 3.54% for AP prediction, respectively. Conclusions: We conclude that although the reconstructed abdominal CT localization images may lack some details on the internal organ structures, they could be used effectively for tube current modulation calculation and patient positioning purposes, leading to a reduction of radiation dose and scan time in clinical CT exams. (C) 2021 Elsevier B.V. All rights reserved.

关键词： CT localizer image Image generation Deep learning encoder-decoder network Scaled mixture loss

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Microseismic First-Arrival Picking Using Fine-Tuning Feature Pyramid networks

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Liu, Naihao Chen, Jiamin Wu, Hao Li, Fangyu Gao, Jinghuai Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Shaanxi Peoples R China China Univ Geosci Sch Earth Resources Wuhan 430074 Hubei Peoples R China Beijing Univ Technol Fac Informat Technol Engn Res Ctr Digital CommunityMinist Educ Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Urban Mass Transit Beijing 100124 Peoples R China

Microseismic event picking is one of the key steps in seismic processing and imaging. Manually picking is a widely used way to pick the microseismic events, which is time-consuming. The standard short-term average/long-term average (STA/LTA) is a traditional method to pick the microseismic first arrivals, which would lead to inaccurate first-arrival picks in case of low signal-to-noise ratio (SNR). We developed a workflow to automatically pick the microseismic first arrivals by using the feature pyramid networks (FPNs). To train the proposed model, we first randomly select part of the microseismic traces and manually pick the time index of the first arrivals. Next, we segment every selected trace into two parts based on the time index of the manual picking and then assign each part a label. Afterward, we train the proposed fine-tuning FPN model by using the training data and the corresponding labels. It should be noticed that we proposed a loss function, named the point-aware loss, for solving the microseismic first-arrival picking issue. Finally, we predict the microseismic first arrivals by using the well-trained fine-tuning FPN model. The numerical examples demonstrate that our proposed model successfully identifies the microseismic first arrivals. The microseismic first arrivals predicted by using our proposed model are more robust and more accurate than those obtained by using the STA/LTA and the encoder-decoder network.

关键词： Data models Predictive models Noise measurement Numerical models Computational modeling Training data Feature extraction Deep learning encoder-decoder network feature pyramid networks (FPNs) first-arrival picking

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FGAM: A pluggable light-weight attention module for medical image segmentation

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 146卷 105628-105628页

作者： Qiu, Zhongxi Hu, Yan Zhang, Jiayi Chen, Xiaoshan Liu, Jiang Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 51805 Guangdong Peoples R China Chinese Acad Sci Cixi Inst Biomed Engn Beijing Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain inspired Intelligent Shenzhen 51805 Guangdong Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 51805 Guangdong Peoples R China

Medical image segmentation is fundamental for computer-aided diagnosis or surgery. Various attention modules are proposed to improve segmentation results, which exist some limitations for medical image segmentation, such as large computations, weak framework applicability, etc. To solve the problems, we propose a new attention module named FGAM, short for Feature Guided Attention Module, which is a simple but pluggable and effective module for medical image segmentation. The FGAM tries to dig out the feature representation ability in the encoder and decoder features. Specifically, the decoder shallow layer always contains abundant information, which is taken as a queryable feature dictionary in the FGAM. The module contains a parameter-free activator and can be deleted after various encoder-decoder networks' training. The efficacy of the FGAM is proved on various encoder-decoder models based on five datasets, including four publicly available datasets and one inhouse dataset.

关键词： Medical image segmentation Attention mechanism encoder-decoder network

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A FCN Approach to Blockage Correction in Radars 19

A FCN Approach to Blockage Correction in Radars

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6th IEEE Cyber Science and Technology Congress (CyberSciTech)

作者： Wu, Hao Liu, Qi Liu, Xiaodong Zhang, Yonghong Xu, Xiaolong Bilal, Muhammad Nanjing Univ Informat Sci & Technol Sch Comp & Software Engn Res Ctr Digital Forens Minist Educ Nanjing Peoples R China Edinburgh Napier Univ Sch Comp Edinburgh EH11 4BN Midlothian Scotland Nanjing Univ Informat Sci Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Peoples R China Hankuk Univ Foreign Studies Dept Comp & Elect Syst Engn Seoul South Korea

ISBN: (纸本)9781665421744

Doppler weather radar is the most widely used convection detector with the highest resolution in the ground. Echo reflectance data from the weather radar is the key reference for the meteorological department to carry out severe convective weather forecast and early warning, quantitative precipitation estimation(QPE) and quantitative precipitation forecast(QPF). However, in the process of radar detection, it is inevitable to be affected by obstacles, ground object echo interference, radar echo attenuation and other phenomena, resulting in poor data quality of detection results. Therefore, it is very important to correct the missing or disturbed data. On the other hand, with the rapid development of artificial intelligence technology in recent years, more and more meteorological researchers begin to introduce deep learning and other machine learning methods into the research of meteorological field such as weather radar data processing. In this paper, a deep convolutional encoderdecoder network is proposed to correct the beam blocking of weather radar. In this study, the correction of radar beam blockage is regarded as an image inpainting problem. It's the first trying to use deep learning to realize the correction of radar beam blockage. Experiment shows that the method proposed in this paper is significantly better than the traditional method in accuracy, error rate, false alarm rate and other aspects. The method can directly identify and correct the blocking area, and the operation procedure is simple compared traditional methods.

关键词： Deep learning convolutional neural networks encoder-decoder network weather radar image inpainting blockage correction

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Robust Adaptive Pulse Compression Under Encode-decoder network for Synthetic Aperture Radar 7

Robust Adaptive Pulse Compression Under Encode-Decoder Netwo...

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7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Lu, Qianrong Wang, Xiji Wang, Zhicheng Ma, Xianchao Yang, Na Shanghai Radio Equipment Res Inst Shanghai Peoples R China

ISBN: (纸本)9781728173337

The matched filter is widely used in traditional synthetic aperture radar system with the criteria of maximum output signal-noise-ratio (SNR). However, matched filter has high-level side lobes (for example, 13.6dB without windowing) resulting in severe masking for adjacent weak targets. Recently, an adaptive pulse compression (APC) was developed based on minimum mean square error (MMSE) between true ground profiles and compressed outputs. Though has better compression results, it requires tremendous amount of time for processing, which hinders its applications. In this paper, a fast implementation named iterative adaptive pulse compression (IAPC), is designed whereby the respective tap coefficients of all filters are jointly updated with a recursive way and subsequently we incorporate it into the classic range Doppler algorithm via changing the processing sequence, which is shown to be obviously efficient with pulse duration increasing. Moreover, an encoder-decoder architecture is developed for improving the targets' energy to solve the SNR-dependence of both APC and IAPC. As a result, through various stressing experiments including simulation and real data validation, the proposed method is shown to be superior to the original APC and matched filter in the views of time efficiency and peak sidelobe ratio (PSLR) respectively.

关键词： Iterative adaptive pulse compression encoder-decoder network Synthetic aperture radar

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MwUnet: A semantic segmentation deep learning method for the ultrasonic image of hydronephrosis in children

MwUnet: A semantic segmentation deep learning method for the...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Peng, Haoran Guan, Yu Li, Jianqiang Xu, Xi Wen, Pengceng Yang, Jijiang Jia, Yanhe Xie, Xianghui Li, Minglei Wang, Xiaoman Xin, Yue He, Yuzhu Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Tsinghua Univ Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China Beijng Informat Sci & Technol Univ Sch Econ & Management Beijing 100192 Peoples R China Capital Med Univ Beijing Childrens Hosp Natl Ctr Childrens Hlth Beijing 100045 Peoples R China

ISBN: (纸本)9781665442077

Hydronephrosis may lead to many potential diseases, and the diagnosis of hydronephrosis is time-consuming and laborious. To assist physicians in hydronephrosis diagnosis and treatment planning, an accurate and automatic kidney segmentation method is highly required in clinical practice. In recent years, deep convolutional neural networks such as Unet plays a key role in the field of image segmentation, but Unet itself cannot adjust the receptive field actively, which may result in poor attention to the characteristics of the segmented target. We propose an encoder-decoder network with weighted skip connections and the idea of hierarchical equal resolution that can manually control the receptive field. We evaluated our method by comparing it with various classical networks using a dataset of 1850 annotated images. The MPA of the model is 94.12 and the MIoU is 89.49, which outperformed other classical networks we compared to.

关键词： B-ultrasound image renal ultrasound image ureteropelvic junction obstruction (UPJO) hydronephrosis encoder-decoder network

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Unabridged adjacent modulation for clothing parsing

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PATTERN RECOGNITION 2022年 127卷 108594-108594页

作者： Zhang, Dong Zuo, Chengting Wu, Qianhao Fu, Liyong Xiang, Xinguang Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Chinese Acad Forestry Res Inst Forest Resource Informat Tech Beijing 10 0 091 Beijing 100091 Peoples R China Natl Forestry & Grassland Adm Key Lab Forest Management & Growth Modeling Beijing 100091 Peoples R China

Clothing parsing has made tremendous progress in the domain of computer vision recently. Most state-ofthe-art methods are based on the encoder-decoder architecture. However, the existing methods mainly neglect problems of feature uncalibration within blocks and semantics dilution between blocks. In this work, we propose an unabridged adjacent modulation network (UAM-Net) to aggregate multi-level features for clothing parsing. We first build an unabridged channel attention (UCA) mechanism on feature maps within each block for feature recalibration. We further design a top-down adjacent modulation (TAM) for decoder blocks. By deploying TAM, high-level semantic information and visual contexts can be gradually transferred into lower-level layers without loss. The joint implementation of UCA and TAM ensures that the encoder has an enhanced feature representation ability, and the low-level features of the decoders contain abundant semantic contexts. Quantitative and qualitative experimental results on two challenging benchmarks (i.e., colorful fashion parsing and the modified fashion clothing) declare that our proposed UAM-Net can achieve competitive high-accurate performance with the state-of-the-art methods. The source codes are available at: https://***/ctzuo/UAM-Net .(c) 2022 Elsevier Ltd. All rights reserved.

关键词： encoder-decoder network Clothing parsing Attention learning Features modulation Self-supervised learning

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Dual-stream pyramid registration network

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MEDICAL IMAGE ANALYSIS 2022年第0期78卷 1页

作者： Kang, Miao Hu, Xiaojun Huang, Weilin Scott, Matthew R. Reyes, Mauricio Malong LLC Wilmington DE 19890 USA Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Univ Bern ARTORG Ctr Biomed Engn Res Bern Switzerland

We propose a Dual-stream Pyramid Registration network (referred as Dual-PRNet) for unsupervised 3D brain image registration. Unlike recent CNN-based registration approaches, such as VoxelMorph, which computes a registration field from a pair of 3D volumes using a single-stream network, we design a two-stream architecture able to estimate multi-level registration fields sequentially from a pair of feature pyramids. Our main contributions are: (i) we design a two-stream 3D encoder-decoder network that computes two convolutional feature pyramids separately from two input volumes;(ii) we propose sequential pyramid registration where a sequence of pyramid registration (PR) modules is designed to predict multi-level registration fields directly from the decoding feature pyramids. The registration fields are refined gradually in a coarse-to-fine manner via sequential warping, which equips the model with a strong capability for handling large deformations;(iii) the PR modules can be further enhanced by computing local 3D correlations between the feature pyramids, resulting in the improved Dual-PRNet(++) able to aggregate rich detailed anatomical structure of the brain;(iv) our Dual-PRNet(++) can be integrated into a 3D segmentation framework for joint registration and segmentation, by precisely warping voxellevel annotations. Our methods are evaluated on two standard benchmarks for brain MRI registration, where Dual-PRNet(++) outperforms the state-of-the-art approaches by a large margin, i.e., improving recent VoxelMorph from 0.511 to 0.748 (Dice score) on the Mindboggle101 dataset. In addition, we further demonstrate that our methods can greatly facilitate the segmentation task in a joint learning framework, by leveraging limited annotations. (c) 2022PublishedbyElsevierB.V.

关键词： Medical image registration encoder-decoder network Deformable registration 3D segmentation Brain MRI

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FastHand: Fast monocular hand pose estimation on embedded systems

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JOURNAL OF SYSTEMS ARCHITECTURE 2022年 122卷

作者： An, Shan Zhang, Xiajie Wei, Dong Zhu, Haogang Yang, Jianyu Tsintotas, Konstantinos A. Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China JD COM Inc Tech & Data Ctr Beijing 100108 Peoples R China Soochow Univ Sch Rail Transportat Suzhou 215006 Peoples R China Democritus Univ Thrace Dept Prod & Management Engn Xanthi 67132 Greece

Hand pose estimation is a fundamental task in many human-robot interaction-related applications. However, previous approaches suffer from unsatisfying hand landmark predictions in real-world scenes and high computation burden. This paper proposes a fast and accurate framework for hand pose estimation, dubbed as "FastHand". Using a lightweight encoder-decoder network architecture, FastHand fulfills the requirements of practical applications running on embedded devices. The encoder consists of deep layers with a small number of parameters, while the decoder uses spatial location information to obtain more accurate results. The evaluation took place on two publicly available datasets demonstrating the improved performance of the proposed pipeline compared to other state-of-the-art approaches. FastHand offers high accuracy scores while reaching a speed of 25 frames per second on an NVIDIA Jetson TX2 graphics processing unit.

关键词： Hand pose estimation Landmark localization Hand detection encoder-decoder network Heatmap regression

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Swin-MFINet: Swin transformer based multi-feature integration network for detection of pixel-level surface defects

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 209卷

作者： Uzen, Huseyin Turkoglu, Muammer Yanikoglu, Berrin Hanbay, Davut Bingo Univ Dept Comp Engn TR-12000 Bingo Turkey Samsun Univ Dept Software Engn TR-55000 Samsun Turkey Sabanci Univ Fac Engn & Nat Sci TR-34956 Istanbul Turkey Inonu Univ Dept Comp Engn TR-44000 Malatya Turkey

Automatic surface defect detection is critical for manufacturing industries, such as steel, fabric, and marble industries. This study proposes a Swin transformer-based model called Multi-Feature Integration network (Swin-MFINet) for pixel-level surface defect detection. The proposed model consists of an encoder, a Swin transformer-based decoder, and Multi-Feature Integration (MFI) modules. In the encoder module of the proposed model, a pre-trained Inception network is used to extract key features from small-size datasets. In the decoder section, global semantic features are obtained from the initial features by using the Swin-transformer block, which is the newest transformer technology of today. In addition, the convolution layer is used in the last step of the decoder, since transformers are limited in acquiring small spatial details such as edges, colors, and textures, which are important in detecting some small defects. In the last module called MFI, feature maps from different decoder stages are combined, and the channel squeeze-spatial excitation block is applied to reveal important features. Finally, a prediction map is obtained by applying a convolution layer and sigmoid activation function to the MFI module output, respectively. The performance of proposed model is analyzed over MT and MVTec datasets containing surface defect images. The proposed model obtained mIoU scores of 81.37%, and 77.07% respectively, for these two datasets These results outperform the state-of-the-art for the surface defect detection problem.

关键词： Pixel-Level Surface Defects Detection Swin Transformers encoder-decoder network Convolutional Neural network

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