LIVIVO - Search results -

Search results

Result 1 - 10 of total 130

Search options

Article: PE-Net: a parallel framework for 3D inferior mesenteric artery segmentation.

Zhang, Kun / Xu, Peixia / Wang, Meirong / Lin, Pengcheng / Crookes, Danny / He, Bosheng / Hua, Liang

2023 Volume 14, Page(s) 1308987

Abstract: The structural morphology of mesenteric artery vessels is of significant importance for the diagnosis and treatment of colorectal cancer. However, developing automated vessel segmentation methods for this purpose remains challenging. Existing convolution- ...

Abstract	The structural morphology of mesenteric artery vessels is of significant importance for the diagnosis and treatment of colorectal cancer. However, developing automated vessel segmentation methods for this purpose remains challenging. Existing convolution-based segmentation methods have limitations in capturing long-range dependencies, while transformer-based models require large datasets, making them less suitable for tasks with limited training samples. Moreover, over-segmentation, mis-segmentation, and vessel discontinuity are common challenges in vessel segmentation tasks. To address these issues, we propose a parallel encoding architecture that combines transformers and convolutions to retain the advantages of both approaches. The model effectively learns position deviations and enhances robustness for small-scale datasets. Additionally, we introduce a vessel edge capture module to improve vessel continuity and topology. Extensive experimental results demonstrate the improved performance of our model, with Dice Similarity Coefficient and Average Hausdorff Distance scores of 81.64% and 7.7428, respectively.
Language	English
Publishing date	2023-12-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2564217-0
ISSN	1664-042X
ISSN	1664-042X
DOI	10.3389/fphys.2023.1308987
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Article ; Online: Central sleep apnea treated by a constant low-dose CO

Luo, Yuan-Ming / Chen, Yong-Yi / Liang, Shan-Feng / Wu, Lu-Guang / Wellman, Andrew / McEvoy, R Doug / Steier, Joerg / Eckert, Danny J / Polkey, Michael I

Journal of applied physiology (Bethesda, Md. : 1985)

2023 Volume 135, Issue 5, Page(s) 977–984

Abstract: ... ...

Abstract	CO
MeSH term(s)	Humans ; Carbon Dioxide ; Sleep Apnea, Central ; Sleep ; Continuous Positive Airway Pressure ; Heart Failure/drug therapy
Chemical Substances	Carbon Dioxide (142M471B3J)
Language	English
Publishing date	2023-09-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	219139-8
ISSN	1522-1601 ; 0021-8987 ; 0161-7567 ; 8750-7587
ISSN (online)	1522-1601
ISSN	0021-8987 ; 0161-7567 ; 8750-7587
DOI	10.1152/japplphysiol.00312.2023
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Uc I Zs.249: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Book ; Online: Efficient approximation of Earth Mover's Distance Based on Nearest Neighbor Search

Meng, Guangyu / Zhou, Ruyu / Liu, Liu / Liang, Peixian / Liu, Fang / Chen, Danny / Niemier, Michael / Hu, X. Sharon

2024

Abstract: Earth Mover's Distance (EMD) is an important similarity measure between two distributions, used in computer vision and many other application domains. However, its exact calculation is computationally and memory intensive, which hinders its scalability ... ...

Abstract	Earth Mover's Distance (EMD) is an important similarity measure between two distributions, used in computer vision and many other application domains. However, its exact calculation is computationally and memory intensive, which hinders its scalability and applicability for large-scale problems. Various approximate EMD algorithms have been proposed to reduce computational costs, but they suffer lower accuracy and may require additional memory usage or manual parameter tuning. In this paper, we present a novel approach, NNS-EMD, to approximate EMD using Nearest Neighbor Search (NNS), in order to achieve high accuracy, low time complexity, and high memory efficiency. The NNS operation reduces the number of data points compared in each NNS iteration and offers opportunities for parallel processing. We further accelerate NNS-EMD via vectorization on GPU, which is especially beneficial for large datasets. We compare NNS-EMD with both the exact EMD and state-of-the-art approximate EMD algorithms on image classification and retrieval tasks. We also apply NNS-EMD to calculate transport mapping and realize color transfer between images. NNS-EMD can be 44x to 135x faster than the exact EMD implementation, and achieves superior accuracy, speedup, and memory efficiency over existing approximate EMD methods.
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence
Subject code	004
Publishing date	2024-01-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: The COVID-19 pandemic and access to health care in people with chronic kidney disease: A systematic review and meta-analysis.

Deng, Danny / Liang, Amy / Chui, Juanita N / Wong, Germaine / Cooper, Tess E

Nephrology (Carlton, Vic.)

2022 Volume 27, Issue 5, Page(s) 410–420

Abstract: Aim: This systematic review aims to evaluate the effect of the COVID-19 pandemic on access to health care for patients with CKD.: Methods: MEDLINE and EMBASE databases were searched up to July 2021 (PROSPERO CRD42021230831). Data relevant to access ... ...

Abstract	Aim: This systematic review aims to evaluate the effect of the COVID-19 pandemic on access to health care for patients with CKD. Methods: MEDLINE and EMBASE databases were searched up to July 2021 (PROSPERO CRD42021230831). Data relevant to access to health care before and during the COVID-19 pandemic were extracted, including outcomes related to access to general nephrology consultations, telehealth, dialysis services and kidney transplantations. Relative and absolute effects were pooled using a random effects model to account for between-study heterogeneity. Risk of bias was assessed using a modified Quality in Prognostic Studies tool. The certainty of the evidence was rated using the GRADE approach. Results: Twenty-three studies across five WHO regions were identified. Reductions in transplantation surgeries were observed during the COVID-19 pandemic compared with the pre-COVID-19 era (risk ratio = 2.15, 95%CI = 1.51-3.06, I Conclusion: Our findings suggest COVID-19 pandemic may have led to reductions in access to kidney transplantation, dialysis and in-person nephrology care. Meanwhile, whilst the use of telehealth has emerged as a promising alternate mode of health care delivery, its utility during the pandemic warrants further investigation. This study has highlighted major barriers to accessing care in a highly vulnerable chronic disease group.
MeSH term(s)	COVID-19/epidemiology ; Health Services Accessibility ; Humans ; Pandemics ; Renal Dialysis ; Renal Insufficiency, Chronic/diagnosis ; Renal Insufficiency, Chronic/epidemiology ; Renal Insufficiency, Chronic/therapy ; Telemedicine
Language	English
Publishing date	2022-01-06
Publishing country	Australia
Document type	Journal Article ; Meta-Analysis ; Systematic Review
ZDB-ID	1303661-0
ISSN	1440-1797 ; 1320-5358
ISSN (online)	1440-1797
ISSN	1320-5358
DOI	10.1111/nep.14016
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

In stock of ZB MED Cologne/Königswinter

Zs.A 4822: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾

Article ; Online: Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts.

Chen, Jintai / Huang, Shuai / Zhang, Ying / Chang, Qing / Zhang, Yixiao / Li, Dantong / Qiu, Jia / Hu, Lianting / Peng, Xiaoting / Du, Yunmei / Gao, Yunfei / Chen, Danny Z / Bellou, Abdelouahab / Wu, Jian / Liang, Huiying

Nature communications

2024 Volume 15, Issue 1, Page(s) 976

Abstract: Early detection is critical to achieving improved treatment outcomes for child patients with congenital heart diseases (CHDs). Therefore, developing effective CHD detection techniques using low-cost and non-invasive pediatric electrocardiogram are highly ...

Abstract	Early detection is critical to achieving improved treatment outcomes for child patients with congenital heart diseases (CHDs). Therefore, developing effective CHD detection techniques using low-cost and non-invasive pediatric electrocardiogram are highly desirable. We propose a deep learning approach for CHD detection, CHDdECG, which automatically extracts features from pediatric electrocardiogram and wavelet transformation characteristics, and integrates them with key human-concept features. Developed on 65,869 cases, CHDdECG achieved ROC-AUC of 0.915 and specificity of 0.881 on a real-world test set covering 12,000 cases. Additionally, on two external test sets with 7137 and 8121 cases, the overall ROC-AUC were 0.917 and 0.907 while specificities were 0.937 and 0.907. Notably, CHDdECG surpassed cardiologists in CHD detection performance comparison, and feature importance scores suggested greater influence of automatically extracted electrocardiogram features on CHD detection compared with human-concept features, implying that CHDdECG may grasp some knowledge beyond human cognition. Our study directly impacts CHD detection with pediatric electrocardiogram and demonstrates the potential of pediatric electrocardiogram for broader benefits.
MeSH term(s)	Humans ; Child ; Deep Learning ; Heart Defects, Congenital/diagnosis ; Electrocardiography ; Cognition
Language	English
Publishing date	2024-02-01
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-024-44930-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: SR-POSE: A Novel Non-Contact Real-Time Rehabilitation Evaluation Method Using Lightweight Technology.

Zhang, Kun / Zhang, Pengcheng / Tu, Xintao / Liu, Zhicheng / Xu, Peixia / Wu, Chenggang / Crookes, Danny / Hua, Liang

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

2023 Volume 31, Page(s) 4179–4188

Abstract: Rehabilitation movement assessment often requires patients to wear expensive and inconvenient sensors or optical markers. To address this issue, we propose a non-contact and real-time approach using a lightweight pose detection algorithm-Sports ... ...

Abstract	Rehabilitation movement assessment often requires patients to wear expensive and inconvenient sensors or optical markers. To address this issue, we propose a non-contact and real-time approach using a lightweight pose detection algorithm-Sports Rehabilitation-Pose (SR-Pose), and a depth camera for accurate assessment of rehabilitation movement. Our approach utilizes an E-Shufflenet network to extract underlying features of the target, a RLE-Decoder module to directly regress the coordinate values of 16 key points, and a Weight Fusion Unit (WFU) module to output optimal human posture detection results. By combining the detected human pose information with depth information, we accurately calculate the angle between each joint in three-dimensional space. Furthermore, we apply the DTW algorithm to solve the distance measurement and matching problem of video sequences with different lengths in rehabilitation evaluation tasks. Experimental results show that our method can detect human joint nodes with an average detection speed of 14.32ms and an average detection accuracy for pose of 91.2%, demonstrating its computational efficiency and effectiveness for practical application. Our proposed approach provides a low-cost and user-friendly alternative to traditional sensor-based methods, making it a promising solution for rehabilitation movement assessment.
MeSH term(s)	Humans ; Algorithms ; Movement ; Posture ; Sports ; Technology
Language	English
Publishing date	2023-10-26
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1166307-8
ISSN	1558-0210 ; 1063-6528 ; 1534-4320
ISSN (online)	1558-0210
ISSN	1063-6528 ; 1534-4320
DOI	10.1109/TNSRE.2023.3324960
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 4726: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images.

Zhang, Kun / Lin, Pengcheng / Pan, Jing / Xu, Peixia / Qiu, Xuechen / Crookes, Danny / Hua, Liang / Wang, Lin

Computational intelligence and neuroscience

2023 Volume 2023, Page(s) 3018320

Abstract: Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual-energyX-ray, quantitative CT, etc., which are high ... ...

Abstract	Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual-energyX-ray, quantitative CT, etc., which are high costs in terms of equipment and human time. Therefore, a more efficient and economical method is urgently needed for diagnosing osteoporosis. With the development of deep learning, automatic diagnosis models for various diseases have been proposed. However, the establishment of these models generally requires images with only lesion areas, and annotating the lesion areas is time-consuming. To address this challenge, we propose a joint learning framework for osteoporosis diagnosis that combines localization, segmentation, and classification to enhance diagnostic accuracy. Our method includes a boundary heat map regression branch for thinning segmentation and a gated convolution module for adjusting context features in the classification module. We also integrate segmentation and classification features and propose a feature fusion module to adjust the weight of different levels of vertebrae. We trained our model on a self-built dataset and achieved an overall accuracy rate of 93.3% for the three label categories (normal, osteopenia, and osteoporosis) in the testing datasets. The area under the curve for the normal category is 0.973; for the osteopenia category, it is 0.965; and for the osteoporosis category, it is 0.985. Our method provides a promising alternative for the diagnosis of osteoporosis at present.
MeSH term(s)	Humans ; Osteoporosis/diagnostic imaging ; Bone Diseases, Metabolic ; Tomography, X-Ray Computed
Language	English
Publishing date	2023-03-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	2388208-6
ISSN	1687-5273 ; 1687-5273
ISSN (online)	1687-5273
ISSN	1687-5273
DOI	10.1155/2023/3018320
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: DeepmdQCT: A multitask network with domain invariant features and comprehensive attention mechanism for quantitative computer tomography diagnosis of osteoporosis.

Zhang, Kun / Lin, Peng-Cheng / Pan, Jing / Shao, Rui / Xu, Pei-Xia / Cao, Rui / Wu, Cheng-Gang / Crookes, Danny / Hua, Liang / Wang, Lin

Computers in biology and medicine

2024 Volume 170, Page(s) 107916

Abstract: In the medical field, the application of machine learning technology in the automatic diagnosis and monitoring of osteoporosis often faces challenges related to domain adaptation in drug therapy research. The existing neural networks used for the ... ...

Abstract	In the medical field, the application of machine learning technology in the automatic diagnosis and monitoring of osteoporosis often faces challenges related to domain adaptation in drug therapy research. The existing neural networks used for the diagnosis of osteoporosis may experience a decrease in model performance when applied to new data domains due to changes in radiation dose and equipment. To address this issue, in this study, we propose a new method for multi domain diagnostic and quantitative computed tomography (QCT) images, called DeepmdQCT. This method adopts a domain invariant feature strategy and integrates a comprehensive attention mechanism to guide the fusion of global and local features, effectively improving the diagnostic performance of multi domain CT images. We conducted experimental evaluations on a self-created OQCT dataset, and the results showed that for dose domain images, the average accuracy reached 91%, while for device domain images, the accuracy reached 90.5%. our method successfully estimated bone density values, with a fit of 0.95 to the gold standard. Our method not only achieved high accuracy in CT images in the dose and equipment fields, but also successfully estimated key bone density values, which is crucial for evaluating the effectiveness of osteoporosis drug treatment. In addition, we validated the effectiveness of our architecture in feature extraction using three publicly available datasets. We also encourage the application of the DeepmdQCT method to a wider range of medical image analysis fields to improve the performance of multi-domain images.
MeSH term(s)	Humans ; Osteoporosis/diagnostic imaging ; Bone Density ; Tomography, X-Ray Computed ; Computers ; Machine Learning ; Image Processing, Computer-Assisted
Language	English
Publishing date	2024-01-08
Publishing country	United States
Document type	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2023.107916
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 653: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Book ; Online: Input Augmentation with SAM

Zhang, Yizhe / Zhou, Tao / Wang, Shuo / Liang, Peixian / Chen, Danny Z.

Boosting Medical Image Segmentation with Segmentation Foundation Model

2023

Abstract: The Segment Anything Model (SAM) is a recently developed large model for general-purpose segmentation for computer vision tasks. SAM was trained using 11 million images with over 1 billion masks and can produce segmentation results for a wide range of ... ...

Abstract	The Segment Anything Model (SAM) is a recently developed large model for general-purpose segmentation for computer vision tasks. SAM was trained using 11 million images with over 1 billion masks and can produce segmentation results for a wide range of objects in natural scene images. SAM can be viewed as a general perception model for segmentation (partitioning images into semantically meaningful regions). Thus, how to utilize such a large foundation model for medical image segmentation is an emerging research target. This paper shows that although SAM does not immediately give high-quality segmentation for medical image data, its generated masks, features, and stability scores are useful for building and training better medical image segmentation models. In particular, we demonstrate how to use SAM to augment image input for commonly-used medical image segmentation models (e.g., U-Net). Experiments on three segmentation tasks show the effectiveness of our proposed SAMAug method. The code is available at \url{https://github.com/yizhezhang2000/SAMAug}. Comment: GitHub: https://github.com/yizhezhang2000/SAMAug. Comments and questions are welcome
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Subject code	004 ; 006
Publishing date	2023-04-22
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: CCF-GNN: A Unified Model Aggregating Appearance, Microenvironment, and Topology for Pathology Image Classification.

Wang, Hongxiao / Huang, Gang / Zhao, Zhuo / Cheng, Liang / Juncker-Jensen, Anna / Nagy, Mate Levente / Lu, Xin / Zhang, Xiangliang / Chen, Danny Z

IEEE transactions on medical imaging

2023 Volume 42, Issue 11, Page(s) 3179–3193

Abstract: Pathology images contain rich information of cell appearance, microenvironment, and topology features for cancer analysis and diagnosis. Among such features, topology becomes increasingly important in analysis for cancer immunotherapy. By analyzing ... ...

Abstract	Pathology images contain rich information of cell appearance, microenvironment, and topology features for cancer analysis and diagnosis. Among such features, topology becomes increasingly important in analysis for cancer immunotherapy. By analyzing geometric and hierarchically structured cell distribution topology, oncologists can identify densely-packed and cancer-relevant cell communities (CCs) for making decisions. Compared to commonly-used pixel-level Convolution Neural Network (CNN) features and cell-instance-level Graph Neural Network (GNN) features, CC topology features are at a higher level of granularity and geometry. However, topological features have not been well exploited by recent deep learning (DL) methods for pathology image classification due to lack of effective topological descriptors for cell distribution and gathering patterns. In this paper, inspired by clinical practice, we analyze and classify pathology images by comprehensively learning cell appearance, microenvironment, and topology in a fine-to-coarse manner. To describe and exploit topology, we design Cell Community Forest (CCF), a novel graph that represents the hierarchical formulation process of big-sparse CCs from small-dense CCs. Using CCF as a new geometric topological descriptor of tumor cells in pathology images, we propose CCF-GNN, a GNN model that successively aggregates heterogeneous features (e.g., appearance, microenvironment) from cell-instance-level, cell-community-level, into image-level for pathology image classification. Extensive cross-validation experiments show that our method significantly outperforms alternative methods on H&E-stained and immunofluorescence images for disease grading tasks with multiple cancer types. Our proposed CCF-GNN establishes a new topological data analysis (TDA) based method, which facilitates integrating multi-level heterogeneous features of point clouds (e.g., for cells) into a unified DL framework.
MeSH term(s)	Humans ; Decision Making ; Forests ; Neural Networks, Computer ; Neoplasms/diagnostic imaging ; Tumor Microenvironment
Language	English
Publishing date	2023-10-27
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	622531-7
ISSN	1558-254X ; 0278-0062
ISSN (online)	1558-254X
ISSN	0278-0062
DOI	10.1109/TMI.2023.3249343
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 2546: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

To top

More links

Kategorien

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito