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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Xinwen Liu Jing Wang S. Kevin Zhou Craig Engstrom Shekhar S. Chandra School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia The Commonwealth Scientific and Industrial Research Organisation Canberra Australia Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) School of Biomedical Engineering & Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Human Movement and Nutrition Sciences The University of Queensland Brisbane Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Deep neural networks have been widely studied to predict a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables, and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of various modalities may not always be of high quality, and each modality may have only partial information of medical condition. Thus, predictions from different modalities can be in conflict, and the final prediction may fail in the presence of such a conflict. Therefore, it is important to account for the reliability of each source data and the prediction output when making a final decision. In this paper, we propose an evidence-aware multimodal data fusion framework based on the Dempster-Shafer theory (DST). The backbone models contain an image branch, a non-image branch and a fusion branch. For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch. The output probabilities along with the evidence scores from multiple branches are combined with the Dempster's combination rule to make a final prediction. Experimental results on the public OA initiative (OAI) dataset for the TKR prediction task show that the proposed method has better performance by accounting for conflicts from various modalities.

关键词： Medical conditions Soft sensors Digital images Data integration Imaging Artificial neural networks Reliability theory Feature extraction Reliability engineering Knee replacement

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Towards Accurate Unified Anomaly Segmentation

Towards Accurate Unified Anomaly Segmentation

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Wenxin Ma Qingsong Yao Xiang Zhang Zhelong Huang Zihang Jiang S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) USTC Suzhou Institute for Advance Research Suzhou Jiangsu China Stanford University Palo Alto California United State School of Medicine Shanghai University Shanghai China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Unsupervised anomaly detection (UAD) from images strives to model normal data distributions, creating discriminative representations to distinguish and precisely localize anomalies. Despite recent advancements in the efficient and unified one-for-all scheme, challenges persist in accurately segmenting anomalies for further monitoring. Moreover, this problem is obscured by the widely-used AUROC metric under imbalanced UAD settings. This motivates us to emphasize the significance of precise segmentation of anomaly pixels using pAP and DSC as metrics. To address the unsolved segmentation task, we introduce the Unified Anomaly Segmentation (UniAS). UniAS presents a multi-level hybrid pipeline that progressively enhances normal information from coarse to fine, incorporating a novel multi-granularity gated CNN (MGG-CNN) into Transformer layers to explicitly aggregate local details from different granularities. UniAS achieves state-of-the-art anomaly segmentation performance, attaining 65.12/59.33 and 40.06/32.50 in pAP/DSC on the MVTec-AD and VisA datasets, respectively, surpassing previous methods significantly. The codes are shared at https://***/Mwxinnn/UniAS.

关键词： Measurement Image segmentation Computer vision Codes Pipelines Logic gates Transformers Data models Monitoring Anomaly detection

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SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Shin, Hyungseob Kim, Hyeongyu Kim, Sewon Jun, Yohan Eo, Taejoon Hwang, Dosik School of Electrical and Electronic Engineering Yonsei University Korea Republic of Department of Oral and Maxillofacial Radiology College of Dentistry Yonsei University Korea Republic of Department of Radiology Center for Clinical Imaging Data Science College of Medicine Yonsei University Korea Republic of Naver AI Lab Naver Cloud Probe Medical Inc. United Kingdom Martinos Center for Biomedical Imaging United States Harvard Medical School United States Center for Healthcare Robotics Korea Institute of Science and Technology Korea Republic of

Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance in fully supervised manner. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical imaging fields. Unsupervised domain adaptation (UDA) can alleviate this problem, which makes it possible to use annotated data in one imaging modality to train a network that can successfully perform segmentation on target imaging modality with no labels. In this work, we propose SDC-UDA, a simple yet effective volumetric UDA framework for Slice-Direction Continuous cross-modality medical image segmentation which combines intra- and inter-slice self-attentive image translation, uncertainty-constrained pseudo-label refinement, and volumetric self-training. Our method is distinguished from previous methods on UDA for medical image segmentation in that it can obtain continuous segmentation in the slice direction, thereby ensuring higher accuracy and potential in clinical practice. We validate SDC-UDA with multiple publicly available cross-modality medical image segmentation datasets and achieve state-of-the-art segmentation performance, not to mention the superior slice-direction continuity of prediction compared to previous studies. © 2023, CC BY.

关键词： Medical imaging

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Transferability of a Sensing Mattress for Posture Classification from Research into Clinics

Transferability of a Sensing Mattress for Posture Classifica...

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： Oriella Gnarra Alexander Breuss Lorenzo Rossi Manuel Fujs Samuel E.J. Knobel Jan D. Warncke Stephan M. Gerber Claudio L.A. Bassetti Robert Riener Tobias Nef Markus H. Schmidt Department of Neurology Sleep-Wake Epilepsy Center University Hospital of Bern Switzerland Department of Health Sciences and Technology Sensory-Motor Systems Lab Institute of Robotics and Intelligent Systems ETH Zurich ETH Zurich Switzerland Gerontechnology Rehabilitation Group ARTORG Centre for Biomedical Engineering Research University of Bern Switzerland Spinal Cord Injury Center University Hospital Balgrist Zurich Switzerland Ohio Sleep Medicine Institute Dublin Ohio USA

Sleep is crucial in rehabilitation processes, promoting neural plasticity and immune functions. Nocturnal body postures can indicate sleep quality and frequent repositioning is required to prevent bedsores for bedridden patients after a stroke or spinal cord injury. Polysomnography (PSG) is considered the gold standard for sleep assessment. Unobtrusive methods for classifying sleep body postures have been presented with similar accuracy to PSG, but most evaluations have been done in research lab environments. To investigate the challenges in the usability of a previously validated device in a clinical setting, we recorded the sleep posture of 17 patients with a sensorized mattress. Ground-truth labels were collected automatically from a PSG device. In addition, we manually labeled the body postures using video data. This allowed us also to evaluate the quality of the PSG labels. We trained neural networks based on the VGG-3 architecture to classify lying postures and used a self-label correction method to account for noisy labels in the training data. The models trained with the video labels achieved a higher classification accuracy than those trained with the PSG labels (0.79 vs. 0.68). The self-label correction could further increase the models' scores based on video and PSG labels to 0.80 and 0.70, respectively. Unobtrusive sensors validated in clinics can, therefore, potentially improve the quality of care for bedridden patients and advance the field of rehabilitation.

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Highly accelerated multi-shot intravoxel incoherent motion diffusion-weighted imaging in brain enabled by parametric POCS-based multiplexed sensitivity encoding

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NMR in Biomedicine 2024年第3期37卷 e5063-e5063页

作者： Chen, Shihui Chu, Mei-Lan Liang, Liyuan Liu, Yi-Jui Chen, Nan-Kuei Wang, He Juan, Chun-Jung Chang, Hing-Chiu Department of Biomedical Engineering The Chinese University of Hong Kong Shatin Hong Kong Department of Diagnostic Radiology Li Ka Shing Faculty of Medicine The University of Hong Kong Pokfulam Hong Kong Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Biomedical Engineering University of Arizona TucsonAZ United States Brain Imaging and Analysis Center Duke University Medical Center DurhamNC United States Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Ministry of Education Shanghai China Human Phenome Institute Fudan University Shanghai China Department of Medical Imaging China Medical University Hsinchu Hospital Hsinchu Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging China Medical University Hospital Taichung Taiwan Multi-Scale Medical Robotics Center Shatin Hong Kong

Recently, intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) has also been demonstrated as an imaging tool for applications in neurological and neurovascular diseases. However, the use of single-shot diffusion-weighted echo-planar imaging for IVIM DWI acquisition leads to suboptimal data quality: for instance, geometric distortion and deteriorated image quality at high spatial resolution. Although the recently commercialized multi-shot acquisition methods, such as multiplexed sensitivity encoding (MUSE), can attain high-resolution and high-quality DWI with signal-to-noise ratio (SNR) performance superior to that of the conventional parallel imaging method, the prolonged scan time associated with multi-shot acquisition is impractical for routine IVIM DWI. This study proposes an acquisition and reconstruction framework based on parametric-POCSMUSE to accelerate the four-shot IVIM DWI with 70% reduction of total scan time (13 min 8 s versus 4 min 8 s). First, the four-shot IVIM DWI scan with 17 b values was accelerated by acquiring only one segment per b value except for b values of 0 and 600 s/mm2. Second, an IVIM-estimation scheme was integrated into the parametric-POCSMUSE to enable joint reconstruction of multi-b images from under-sampled four-shot IVIM DWI data. In vivo experiments on both healthy subjects and patients show that the proposed framework successfully produced multi-b DW images with significantly higher SNRs and lower reconstruction errors than did the conventional acceleration method based on parallel imaging. In addition, the IVIM quantitative maps estimated from the data produced by the proposed framework showed quality comparable to that of fully sampled MUSE-reconstructed images, suggesting that the proposed framework can enable highly accelerated multi-shot IVIM DWI without sacrificing data quality. In summary, the proposed framework can make multi-shot IVIM DWI feasible in a routine MRI examination, with reasonable scan time a

关键词： Image reconstruction

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Taming Stable Diffusion for MRI Cross-Modality Translation

Taming Stable Diffusion for MRI Cross-Modality Translation

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yingtai Li Shuo Yang Xiaoyan Wu Shan He S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine USTC Hefei P.R. China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research USTC P.R. China iFLYTEK Research iFLYTEK Co. Ltd Hefei P.R. China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing P.R. China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In this study, we explore using Stable Diffusion (SD) for unsupervised medical image-to-image translation. SD has shown remarkable performances in generating high-quality images and can be easily applied to generate custom contents by injecting standard plug-ins like LoRA, offering a promising solution to tackle the complexity caused by variations in imaging modalities, acquisition parameters, and body parts in medical imaging. However, We empirically find that existing pipelines designed for natural images fail to translate directly to medical images due to weak structural control and inappropriate color preservation. To address these issues, we propose a novel two-branch image translation pipeline. This pipeline decouples the generation of target image along the time axis and employs ControlNet to ensure precise structural preservation. Additionally, we customize SD to generate images of extreme brightness, a common feature in medical imaging. Our results on the BraTS dataset demonstrate that SD with task-specific plug-ins can generate high-quality medical images comparable to those generated by task-specific models. Since the development of these standard plug-ins can be easily done by clinicians without much knowledge of the underlying algorithm, such a mode holds the potential to significantly extend the use of medical image computing algorithms in the clinical environment.

关键词： Translation Image color analysis Computational modeling Biological system modeling Magnetic resonance imaging Pipelines Brightness Complexity theory Standards biomedical imaging

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Centrifugal Microfluidic Based Painless Blood Sample Collection for Hematocrit Measurement

Centrifugal Microfluidic Based Painless Blood Sample Collect...

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biomedical Engineering International Conference (BMEiCON)

作者： Santi Rattanavarin Thanapat Sangkharat Ekachai Juntasaro Witsaroot Sripumkhai Pattaraluck Pattamang Norabadee Ranron Numfon Khemthongcharoen Chompunoot Sinthupibulyakit Chamras Promptmas Chayapitcha Saesue Jiraskul Udarwudhipong Wutthinan Jeamsaksiri Department of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok (KMUTNB) Bangkok Thailand Mechanical Engineering Simulation and Design Group (MESD) Sirindhorn International Thai-German Graduate School of Engineering (TGGS) King Mongkut’s University of Technology North Bangkok (KMUTNB) Bangkok Thailand Thai Microelectronics Center (TMEC) National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Chachoengsao Thailand Infrastructure Development Division (IDD) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand Faculty of Medical Technology Huachiew Chalermprakiet University Samut Prakan Thailand Department of Biomedical Engineering Faculty of Engineering Mahidol University Nakhon Pathom Thailand

This work demonstrates a centrifugal microfluidic chip design for painless blood collection and hematocrit measurement. The fluid channel in the microfluidic chip was fabricated using a laser-cutting method. An optimal dimension of the negative pressure chamber was designed to control the maximum sample volume obtained for further hematocrit examination. The estimated blood sample of 20 µl could be obtained based on the negative pressure driven by finger press actuation on the negative pressure chamber of the microfluidic chip. The repeatability of blood volume collection evaluated by a laboratory technician was 21.126±0.001 µl with a coefficient of variation (CV) of 5.32%. The sample volume used in this centrifugal microfluidic chip was around three times lower than that used in the standard method. Pack red cell preparation for hematocrit examination in the microfluidic chip could be achieved using a centrifugation speed around 2.4 times lower than that used in the standard method. The hematocrit examination by our centrifugal microfluidic chip and the standard method was 41.75±0.77 (1.84 %CV), and 41.5±0.71 (1.7 %CV), respectively. The results showed that the hematocrit measurements in our centrifugal microfluidic chip were compatible with those measured by the standard method.

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A New Computer-Aided Solution for the Automatic Detection of Metal Stent Struts in Follow-up Evaluation in OCT Images

A New Computer-Aided Solution for the Automatic Detection of...

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Computers in Cardiology (CinC)

作者： Zofia Schneider Elżbieta Pociask Klaudia Proniewska Radek Kolecki Department of Biocybernetics and Biomedical Engineering BioMedical Imaging Science Club AGH University of Science and Technology Kraków Poland Center for Digital Medicine and Robotics Jagiellonian University Medical College Kraków Poland Department of Bioinformatics and Telemedicine Faculty of Medicine Jagiellonian University Medical College Kraków Poland University Hospital in Kraków Poland Jagiellonian University Medical College Kraków Poland

Stenting is commonly used in the treatment of coronary artery disease. To optimize the results of the procedure, it is crucial to evaluate the stent immediately after its implantation, and then to later monitor how it becomes covered by the neointima. One modality used for this is intravascular optical coherence tomography (IVOCT). While the identification and assessment of stent struts in IVOCT images is routinely done in clinical practice, manual analysis remains laborious and time-consuming. To address this, automated algorithms for stent segmentation have recently been developed. However, these are mainly used for stents without thick tissue coverage. This study proposes a new computer-aided method to automate the detection of both covered and uncovered metal stent struts in OCT images. In general, the algorithm involves segmenting potential stent strut shadows, analyzing the distribution of pixel intensities in detected areas, and then classifying objects. The algorithm has been validated on 606 cross-sections chosen randomly from 34 cases containing pullbacks: at baseline and at 3-36-month follow-up. The presented algorithm achieves sensitivity of 88% and precision of 90% including in-stent restenosis cases.

关键词： Image segmentation Sensitivity Optical coherence tomography Metals Morphology Manuals Classification algorithms

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Autoencoder-XGBoost Classifier (AeXGB) for Predicting Severity Level of Parkinson's Disease from Spontaneous Speech

Autoencoder-XGBoost Classifier (AeXGB) for Predicting Severi...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Thiri Wai Yu-Shan Liao Ting-Yun Liao Chin-Hsien Lin Chi-Sheng Hung Li-Chen Fu Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Neurology National Taiwan University Hospital Bei-Hu Branch Graduate Institute of Biomedical Engineering College of Medical Science and Technology Taipei Medical University Taipei Taiwan Institute of Molecular Medicine College of Medicine National Taiwan University Taipei Taiwan Department of Internal Medicine National Taiwan University Hospital and National Taiwan University College of Medicine Taipei Taiwan NTU Center for Artificial Intelligence (AI) and Advanced Robotics

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Parkinson's disease (PD) is the cause of the gradual decline of nerve cells that control movement disorder disease, which is most common among the elderly in the US after Alzheimer's disease. There are several studies on detecting Parkinson's disease from speech using machine learning techniques; however, most of them focus on classifying healthy patients (HC) against Parkinson's disease (PD). This paper focuses on developing a screening system that could detect healthy (HC) vs. mild Parkinson's (MP) vs. severe Parkinson's (SP) from spontaneous speech. Four acoustic feature sets were compared for the screening system. Our proposed AeXGB was also compared with six different classifying approaches. The result has shown that extracting the phonation features and using AeXGB could achieve an accuracy of 92% for classifying HC vs. MP vs. SP, which outperforms the traditional machine learning approach for three class classifications of Parkinson's severity level from spontaneous speech.

关键词： Deep learning Accuracy Parkinson's disease Autoencoders Neurons Speech enhancement Biomarkers Feature extraction Multilingual Older adults

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PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation

arXiv

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arXiv 2022年

作者： Wang, Guotai Luo, Xiangde Gu, Ran Yang, Shuojue Qu, Yijie Zhai, Shuwei Zhao, Qianfei Li, Kang Zhang, Shaoting School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Shanghai Artificial Intelligence Laboratory Shanghai China Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore United States West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China

Background and Objective: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models. Existing toolkits mainly focus on fully supervised segmentation and require full and accurate pixel-level annotations that are time-consuming and difficult to acquire for segmentation tasks, which makes learning from imperfect labels highly desired for reducing the annotation cost. We aim to develop a new deep learning toolkit to support annotation-efficient learning for medical image segmentation. Methods: Our proposed toolkit named PyMIC is a modular deep learning library for medical image segmentation tasks. In addition to basic components that support development of high-performance models for fully supervised segmentation, it contains several advanced components tailored for learning from imperfect annotations, such as loading annotated and unannounced images, loss functions for unannotated, partially or inaccurately annotated images, and training procedures for co-learning between multiple networks, etc. PyMIC supports development of semi-supervised, weakly supervised and noise-robust learning methods for medical image segmentation. Results: We present several illustrative medical image segmentation tasks based on PyMIC: (1) Achieving competitive performance on fully supervised learning;(2) Semi-supervised cardiac structure segmentation with only 10% training images annotated;(3) Weakly supervised segmentation using scribble annotations;and (4) Learning from noisy labels for chest radiograph segmentation. Conclusions: The PyMIC toolkit is easy to use and facilitates efficient development of medical image segmentation models with imperfect annotations. It is modular and flexible, which enables researchers to develop high-performance models with low annotation cost. The source code is available at:https://***/HiLab-git/PyMIC. © 2022, CC BY.

关键词： Deep learning

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