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AIDAN: An Attention-guided Dual-path Network for Pediatric Echocardiography Segmentation

Yujin Hu, Bei Xia, Muyi Mao, Zelong Jin, Jie Du, Libao Guo, Alejandro F Frangi, Baiying Lei, Tianfu Wang
2020 IEEE Access  
Thus, the spatial attention sub-module exploits the inter-spatial relationship and focuses on "where" an informative part is.  ...  These researchers are mainly focused on magnetic resonance imaging in adults with less attention given to pediatric cardiac ultrasound analysis [13] .  ... 
doi:10.1109/access.2020.2971383 fatcat:fyppvl37rfeipb4l6fwkjo5wk4

Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning [article]

Hengtao Guo, Sheng Xu, Bradford Wood, Pingkun Yan
2020 arXiv   pre-print
Transrectal ultrasound (US) is the most commonly used imaging modality to guide prostate biopsy and its 3D volume provides even richer context information.  ...  In this paper, we propose a deep contextual learning network (DCL-Net), which can efficiently exploit the image feature relationship between US frames and reconstruct 3D US volumes without any tracking  ...  Acknowledgements This work was partially supported by National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health (NIH) under awards R21EB028001 and R01EB027898  ... 
arXiv:2006.07694v1 fatcat:gpy27ucqkjddplxvzbd7xh7y2a

Automatic ultrasound vessel segmentation with deep spatiotemporal context learning [article]

Baichuan Jiang, Alvin Chen, Shyam Bharat, Mingxin Zheng
2021 arXiv   pre-print
Accurate, real-time segmentation of vessel structures in ultrasound image sequences can aid in the measurement of lumen diameters and assessment of vascular diseases.  ...  We propose to leverage the rich spatiotemporal context available in ultrasound to improve segmentation of small-scale lower-extremity arterial vasculature.  ...  The authors thank Elizabeth Brunelle, Barbara Bannister, and Jochen Kruecker for assistance in data acquisition, annotation, and review.  ... 
arXiv:2111.02461v1 fatcat:njawop2tufhsbbtlvjrklg5uxu

Endocardial Boundary E timation and Tracking in Echocardiographic Images using Deformable Template and Markov Random Fields

Max Mignotte, Jean Meunier, Jean-Claude Tardif
2001 Pattern Analysis and Applications  
We present a new approach to shape-based segmentation and tracking of deformable anatomical structures in medical images, and validate this approach by detecting and tracking the endocardial contour in  ...  In this deformable model-based Bayesian segmentation, the data likelihood model relies on an accurate statistical modelling of the grey level distribution of each class present in the ultrasound image.  ...  Acknowledgments The authors thank INRIA (Institut National de la Recherche en Informatique et Automatique, France) for financial support of this work (postdoctoral grant).  ... 
doi:10.1007/pl00010988 fatcat:3hb3la644vcbfnbn3p3ufhofiu

Using a geometric formulation of annular-like shape priors for constraining variational level-sets

M. Alessandrini, T. Dietenbeck, O. Basset, D. Friboulet, O. Bernard
2010 2010 IEEE International Conference on Image Processing  
The behavior of this approach is illustrated on images from various fields. An evaluation is then performed for the myocardium detection in MRI and ultrasound cardiac images.  ...  In this paper we address the segmentation of images exhibiting annular like shapes which may be approximated by two elliptical contours.  ...  We observe that image gradient, exploited for edges location, is not a reliable indicator in low SNR situations, as in ultrasound images.  ... 
doi:10.1109/icip.2010.5652997 dblp:conf/icip/AlessandriniDBFB10 fatcat:z2x4hlnbvbdlzkdpqi6k6hxrlq

Deep Learning for Cardiac Image Segmentation: A Review

Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert
2020 Frontiers in Cardiovascular Medicine  
Deep learning has become the most widely used approach for cardiac image segmentation in recent years.  ...  (CT), and ultrasound and major anatomical structures of interest (ventricles, atria, and vessels).  ...  ACKNOWLEDGMENTS We would like to thank our colleagues: Karl Hahn, Qingjie Meng, James Batten, and Jonathan Passerat-Palmbach who provided the insight and expertise that greatly assisted the work, and also  ... 
doi:10.3389/fcvm.2020.00025 pmid:32195270 pmcid:PMC7066212 fatcat:iw7xpnltn5cgbn5ullq2ldy3nq

Artificial Intelligence in Quantitative Ultrasound Imaging: A Review [article]

Boran Zhou, Xiaofeng Yang, Tian Liu
2020 arXiv   pre-print
Quantitative ultrasound (QUS) imaging is a reliable, fast and inexpensive technique to extract physically descriptive parameters for assessing pathologies.  ...  In recent years, there has been an increasing interest in artificial intelligence (AI) applications in ultrasound imaging. However, no research has been found that surveyed the AI use in QUS.  ...  Wang et al. developed a 3D deep neural network coupled with attention modules for prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the CNN  ... 
arXiv:2003.11658v1 fatcat:iujuh7gra5ax7od2gxoo6yrbpe

B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention

Hamideh Kerdegari, Nhat Tran Huy Phung, Angela McBride, Luigi Pisani, Hao Van Nguyen, Thuy Bich Duong, Reza Razavi, Louise Thwaites, Sophie Yacoub, Alberto Gomez, VITAL Consortium
2021 Applied Sciences  
The presence of B-line artefacts, the main artefact reflecting lung abnormalities in dengue patients, is often assessed using lung ultrasound (LUS) imaging.  ...  Inspired by human visual attention that enables us to process videos efficiently by paying attention to where and when it is required, we propose a spatiotemporal attention mechanism for B-line detection  ...  Recently, automatic image analysis using deep learning (DL) methods have shown promise for various tasks such as the classification, reconstruction, and segmentation of tissues in ultrasound images [6  ... 
doi:10.3390/app112411697 fatcat:u2ov6u2hzfhkjg6gscj2d354ma

Learning Disentangled Representations in the Imaging Domain [article]

Xiao Liu, Pedro Sanchez, Spyridon Thermos, Alison Q. O'Neil, Sotirios A. Tsaftaris
2022 arXiv   pre-print
We survey applications in medical imaging emphasising choices made in exemplar key works, and then discuss links to computer vision applications.  ...  In this tutorial paper, we motivate the need for disentangled representations, revisit key concepts, and describe practical building blocks and criteria for learning such representations.  ...  We thank the participants of the DREAM tutorials for feedback.  ... 
arXiv:2108.12043v5 fatcat:cbpmp6pbajhjvjzovulswuj2wy

Deep learning for cardiac image segmentation: A review [article]

Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert
2019 arXiv   pre-print
Deep learning has become the most widely used approach for cardiac image segmentation in recent years.  ...  (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels).  ...  Anatomical shape priors have been utilized to increase the robustness of deep learningbased segmentation methods to challenging 3D ultrasound images.  ... 
arXiv:1911.03723v1 fatcat:cwsq5hiaebgkza5ktmtyw553je

An Efficient Discrete Wavelet Transform Based Partial Hadamard Feature Extraction and Hybrid Neural Network Based Monarch Butterfly Optimization for Liver Tumor Classification

Deepak S Uplaonkar, PDA College of Engineering, Kalaburagi 585101, Karnataka, India, Virupaksh appa, Nagabhushan Patil, Department of Computer Science and Engineering, Sharnbasva University, Kalaburagi 585101, Karnataka, India,, Department of Electrical and Electronics Engineering, PDA College of Engineering, Kalaburagi 585101, Karnataka, India
2021 Engineered Science  
Liver tumour classification from ultrasound images is a challenging task since it is based on the structure and orientation of the liver tumour cells.  ...  Then the preprocessed images are segmented using the adaptively regularized kernel-based fuzzy C-means clustering algorithm and level enhanced segmentation which enhances the segmentation process and the  ...  Basavaraj Amarapur for their continuous guidance and support.  ... 
doi:10.30919/es8d594 fatcat:pmb5pzjw65hxpplsn3f7dkooqm

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Sara Moccia, Elena De Momi, Sara El Hadji, Leonardo S. Mattos
2018 Computer Methods and Programs in Biomedicine  
Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes  ...  For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed.  ...  [121] 2004 --X-ray context of image segmentation and then in the specific context of vessel segmentation.  ... 
doi:10.1016/j.cmpb.2018.02.001 pmid:29544791 fatcat:cchvmvuy5zgzzetv5hwc67nnbe

A restoration framework for ultrasonic tissue characterization

M. Alessandrini, S. Maggio, J. Poree, L. De Marchi, N. Speciale, E. Franceschini, O. Bernard, O. Basset
2011 IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control  
In medical ultrasound, deconvolution is commonly used to increase diagnostic reliability of ultrasound images by improving their contrast and resolution.  ...  The algorithm overcomes limitations associated with standard techniques by using a nonstandard prior model for the tissue response.  ...  The problem of deconvolution of medical ultrasound images has received some attention in recent years; see [21] for a review.  ... 
doi:10.1109/tuffc.2011.2092 pmid:22083768 fatcat:ivyqbhu7pjbmrfaakv2h52kboq

Recent advances and clinical applications of deep learning in medical image analysis [article]

Xuxin Chen, Ximin Wang, Ke Zhang, Roy Zhang, Kar-Ming Fung, Theresa C. Thai, Kathleen Moore, Robert S. Mannel, Hong Liu, Bin Zheng, Yuchen Qiu
2021 arXiv   pre-print
scenarios, including classification, segmentation, detection, and image registration.  ...  Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging  ...  In another study, Schlemper et al. (2019) incorporated attention modules to a variant network of VGG (Baumgartner et al., 2017) and U-Net (Ronneberger et al., 2015) for 2D fetal ultrasound image plane  ... 
arXiv:2105.13381v2 fatcat:2k342a6rhjaavpoa2qoqxhg5rq

3-D active appearance models: segmentation of cardiac MR and ultrasound images

S.C. Mitchell, J.G. Bosch, B.P.F. Lelieveldt, R.J. van der Geest, J.H.C. Reiber, M. Sonka
2002 IEEE Transactions on Medical Imaging  
A model-based method for three-dimensional image segmentation was developed and its performance assessed in segmentation of volumetric cardiac magnetic resonance (MR) images and echocardiographic temporal  ...  This ensures a spatially and/or temporally consistent segmentation of three-dimensional cardiac images.  ...  ACKNOWLEDGMENT Ultrasound data and echocardiographic independent standard were provided by F. Nijland, M.D., and O. Kamp M.D., Ph.D., Free University Hospital, Amsterdam.  ... 
doi:10.1109/tmi.2002.804425 pmid:12564884 fatcat:bt6s3bp23varngj4lv26bk6efe
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