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Deep Learning in Medical Ultrasound Image Segmentation: a Review [article]

Ziyang Wang
<span title="2021-03-05">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In addition, common evaluation methods for image segmentation and ultrasound image segmentation datasets are summarized.  ...  In the end, the challenges and potential research directions for medical ultrasound image segmentation are discussed.  ...  [Zhuang et al., 2019] proposed Grouped-Resaunet (GRA U-net) for slice-by-slice nipple segmentation and localization on breast ultrasound.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.07703v3">arXiv:2002.07703v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dosuiqzoh5e6tm4754wmxeifam">fatcat:dosuiqzoh5e6tm4754wmxeifam</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210410070349/https://arxiv.org/pdf/2002.07703v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7f/45/7f45ca3f79ec68fca1b6798394e4410a63fd9783.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.07703v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

An automated computational biomechanics workflow for improving breast cancer diagnosis and treatment

Thiranja Prasad Babarenda Gamage, Duane T. K. Malcolm, Gonzalo Maso Talou, Anna Mîra, Anthony Doyle, Poul M. F. Nielsen, Martyn P. Nash
<span title="2019-08-06">2019</span> <i title="The Royal Society"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/d7whzd2jibasbkf6j6dw5axlae" style="color: black;">Interface Focus</a> </i> &nbsp;
These challenges include interpreting and co-locating information between different medical imaging modalities that are used to identify tumours and predicting where these tumours move to during different  ...  We have developed a novel automated breast image analysis workflow that integrates state-of-the-art image processing and machine learning techniques, personalized three-dimensional biomechanical modelling  ...  The Titan Xp used for this research was donated by the NVIDIA Corporation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1098/rsfs.2019.0034">doi:10.1098/rsfs.2019.0034</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31263540">pmid:31263540</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6597517/">pmcid:PMC6597517</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3vrpb5fdwbc3jbipuytir6c4xy">fatcat:3vrpb5fdwbc3jbipuytir6c4xy</a> </span>
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Recent Advances in Machine Learning Applied to Ultrasound Imaging

Monica Micucci, Antonio Iula
<span title="2022-06-06">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ikdpfme5h5egvnwtvvtjrnntyy" style="color: black;">Electronics</a> </i> &nbsp;
The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation  ...  The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification  ...  septum time-series information [131] Fetus Automatic U-net/DeepLabV3+ IoU Private segmentation segmentation enhanced DeepLabv3+: 0.47 538 4VC images of the thoracic with MultiFrame and U-Net: 0.493 in  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/electronics11111800">doi:10.3390/electronics11111800</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/htw3q5kednhkbndgk7vw3tbvya">fatcat:htw3q5kednhkbndgk7vw3tbvya</a> </span>
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Study on automatic detection and classification of breast nodule using deep convolutional neural network system

Feiqian Wang, Xiaotong Liu, Na Yuan, Buyue Qian, Litao Ruan, Changchang Yin, Ciping Jin
<span title="">2020</span> <i title="AME Publishing Company"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zg4pruoxwzcbtkfe5chpq7zkmi" style="color: black;">Journal of Thoracic Disease</a> </i> &nbsp;
In the training set, we constructed a detection model by a three-dimensionally U-shaped convolutional neural network (3D U-Net) architecture for the purpose of segment the nodules from background breast  ...  In this study, we used Automated Breast Ultrasound (ABUS) machine for the scanning, and deep convolutional neural network (CNN) technology, a kind of Deep Learning (DL) algorithm, for the detection and  ...  U-Net is a modified and extended version of a fully convolutional network, which has been widely used in the tasks of medical image segmentation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.21037/jtd-19-3013">doi:10.21037/jtd-19-3013</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33145042">pmid:33145042</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7578508/">pmcid:PMC7578508</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aoaqpkw7zfbi3chyeyuql7qau4">fatcat:aoaqpkw7zfbi3chyeyuql7qau4</a> </span>
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Discrimination of Breast Cancer Based on Ultrasound Images and Convolutional Neural Network

Rui Du, Yanwei Chen, Tao Li, Liang Shi, Zhengdong Fei, Yuefeng Li, Xiaodong Li
<span title="2022-03-19">2022</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wewutbvhmjgxpaawukserpbooe" style="color: black;">Journal of Oncology</a> </i> &nbsp;
The aim of our study was to establish an artificial intelligence tool for the diagnosis of breast disease base on ultrasound (US) images.  ...  Totally 1181 US images from 487 patients of our hospital and 694 publicly accessible images were employed for modeling, including 558 benign images, 370 malignant images, and 253 normal tissue images.  ...  Acknowledgments The authors acknowledge financial support from the Jiangsu government (Maternal and Child Health Research Project of Jiangsu Province No. F201822).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/7733583">doi:10.1155/2022/7733583</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35345516">pmid:35345516</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8957444/">pmcid:PMC8957444</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u5q2v4iq25arjbm4zrl6obcfau">fatcat:u5q2v4iq25arjbm4zrl6obcfau</a> </span>
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A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses

Matteo Interlenghi, Christian Salvatore, Veronica Magni, Gabriele Caldara, Elia Schiavon, Andrea Cozzi, Simone Schiaffino, Luca Alessandro Carbonaro, Isabella Castiglioni, Francesco Sardanelli
<span title="2022-01-13">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/abr6zdbfebblxlzh436eqtiagi" style="color: black;">Diagnostics</a> </i> &nbsp;
to 18%, always keeping a sensitivity over 94%, when externally tested on 236 images from two image sets: (1) 123 lesions (51 malignant and 72 benign) obtained from two ultrasound systems used for training  ...  From a retrospective 2015–2019 series of ultrasound-guided core needle biopsies performed by four board-certified breast radiologists using six ultrasound systems from three vendors, we collected 821 images  ...  The segmentation of suspicious masses on all 821 images was performed manually by a board-certified radiologist with 34 years of experience in breast imaging, using the TRACE4 segmentation tool.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/diagnostics12010187">doi:10.3390/diagnostics12010187</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35054354">pmid:35054354</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8774734/">pmcid:PMC8774734</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sj7chyalgjhv3imtyfn3sdxkta">fatcat:sj7chyalgjhv3imtyfn3sdxkta</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220114092640/https://mdpi-res.com/d_attachment/diagnostics/diagnostics-12-00187/article_deploy/diagnostics-12-00187.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/02/7f/027fd077a435fa27adc6ae12dd334e60ec32f917.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/diagnostics12010187"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774734" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Breast compression parameters among women imaged with full field digital mammography and breast tomosynthesis in BreastScreen Norway

N. Moshina, Solveig Hofvind, Gunvor Waade, Åsne Holen, Berit Hanestad, Sofie Sebuødegård, K. Pedersen, Elizabeth A. Krupinski
<span title="2018-07-06">2018</span> <i title="SPIE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zeeniayy2vaynggzx7d5wv2s4i" style="color: black;">14th International Workshop on Breast Imaging (IWBI 2018)</a> </i> &nbsp;
The u-net classifies the image but also provides lesion segmentation. Free receiver operating characteristic (FROC) analysis was used to evaluate the model, on an image and on an exam level.  ...  Data was randomly split on an exam level into training (50%), validation (10%) and testing (40%) of deep neural network with u-net architecture.  ...  The geometric information along the breast boundary was used to categorize the nipples into obvious and subtle types. A top hat transform was used to identify the location of obvious nipples.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.2317918">doi:10.1117/12.2317918</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/iwbi/WadeHHSMPH18.html">dblp:conf/iwbi/WadeHHSMPH18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gyksxd5b2jf4jpntucqs5zjc5i">fatcat:gyksxd5b2jf4jpntucqs5zjc5i</a> </span>
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Early Breast Cancer Detection Utilizing Artificial Neural Network

Zakia Sultana, Md. Ashikur Rahman Khan, Nusrat Jahan
<span title="2021-03-18">2021</span> <i title="World Scientific and Engineering Academy and Society (WSEAS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rp2v5illivap5oxivlfoyrptfu" style="color: black;">WSEAS Transactions on Biology and Biomedicine</a> </i> &nbsp;
Recurrent Neural Network (RNN) are used for classifying breast cancer tumor.  ...  And compare the results of these networks to find the best neural network for detecting breast cancer. The networks are tested on Wisconsin breast cancer (WBC) database.  ...  The use of deep learning approaches was proposed for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.37394/23208.2021.18.4">doi:10.37394/23208.2021.18.4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sanygdnd5vfiddcyyzyjucjqo4">fatcat:sanygdnd5vfiddcyyzyjucjqo4</a> </span>
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A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

Parita Oza, Paawan Sharma, Samir Patel, Alessandro Bruno
<span title="2021-09-18">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/au3ye363lzbdroopx7rzfyv63m" style="color: black;">Journal of Imaging</a> </i> &nbsp;
Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer.  ...  One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image.  ...  [121] developed a model to detect breast arterial calcifications using U-Net with dense connectivity.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/jimaging7090190">doi:10.3390/jimaging7090190</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34564116">pmid:34564116</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8466003/">pmcid:PMC8466003</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2r2va44qe5hzhmc6pfysuzphlu">fatcat:2r2va44qe5hzhmc6pfysuzphlu</a> </span>
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Descriptive analysis of computational methods for automating mammograms with practical applications [article]

Aparna Bhale, Manish Joshi
<span title="2020-10-06">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Practical applications of mammograms are not limited to breast cancer revealing, identification ,but include task based lens design, image compression, image classification, content based image retrieval  ...  The discussion focuses on research aiming at a variety of applications and automations of mammograms.  ...  [60] recommend to use a U-Net method to automatically identify and fragment lesions in mammogram images.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.03378v1">arXiv:2010.03378v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5mokkfho6ffvbai7rcdpytszj4">fatcat:5mokkfho6ffvbai7rcdpytszj4</a> </span>
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CARS 2021: Computer Assisted Radiology and Surgery Proceedings of the 35th International Congress and Exhibition Munich, Germany, June 21–25, 2021

<span title="">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ohr6juw5xrcrzgnsati6eq3edq" style="color: black;">International Journal of Computer Assisted Radiology and Surgery</a> </i> &nbsp;
While, Dense U-net, the modified U-net with dense connections, showed a nearly similar dice coefficient score to 3D U-net, it still had some false positives like 2D U-net and 2D SegNet.  ...  For the first step of approximating depth maps, three deep neural networks with different architectures, namely ''V-Net'', ''U-Net'', and ''modified U-Net'' (with a ResNet encoder) were constructed and  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11548-021-02375-4">doi:10.1007/s11548-021-02375-4</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34085172">pmid:34085172</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6d564hsv2fbybkhw4wvc7uuxcy">fatcat:6d564hsv2fbybkhw4wvc7uuxcy</a> </span>
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Three-dimensional stochastic numerical breast phantoms for enabling virtual imaging trials of ultrasound computed tomography

Fu Li, Umberto Villa, Seonyeong Park, Mark A. Anastasio
<span title="2021-09-14">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tmpncrq4lfd25oyfnzmop7odyq" style="color: black;">IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control</a> </i> &nbsp;
Ultrasound computed tomography (USCT) is an emerging imaging modality for breast imaging that can produce quantitative images that depict the acoustic properties of tissues.  ...  Examples of breast phantoms produced by use of the proposed methods and a collection of 52 sets of simulated USCT measurement data have been made open source for use in image reconstruction development  ...  ACKNOWLEDGMENT This work was supported in part by NIH awards R01EB028652 and R01NS102213 and NSF award DMS1614305.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tuffc.2021.3112544">doi:10.1109/tuffc.2021.3112544</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34520354">pmid:34520354</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8790767/">pmcid:PMC8790767</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4nrwfmubdvcahkmhrwfl463eaq">fatcat:4nrwfmubdvcahkmhrwfl463eaq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210921110324/https://ieeexplore.ieee.org/ielx7/58/7307696/09537158.pdf?tp=&amp;arnumber=9537158&amp;isnumber=7307696&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/22/c0/22c00a4b6f10e0e8cb6d737f4fcdc72137592a39.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tuffc.2021.3112544"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8790767" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Deep learning in mammography and breast histology, an overview and future trends

Azam Hamidinekoo, Erika Denton, Andrik Rampun, Kate Honnor, Reyer Zwiggelaar
<span title="">2018</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kpkfymbkufcnzjfc5ydyokby4y" style="color: black;">Medical Image Analysis</a> </i> &nbsp;
CAD systems developed for mammography and breast histopathology images is presented.  ...  Considering the importance of breast cancer worldwide and the promising results reported by deep learning based methods in breast imaging, an overview of the recent state-of-the-art deep learning based  ...  Other imaging modalities for breast imaging, such as MRI and Ultrasound could be exploited in the development of a linking map.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.media.2018.03.006">doi:10.1016/j.media.2018.03.006</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29679847">pmid:29679847</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nkrmtohwfvdtfpo3rbdvvotu2a">fatcat:nkrmtohwfvdtfpo3rbdvvotu2a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200307182610/http://pure.aber.ac.uk/ws/files/25842704/1_s2.0_S1361841518300902_main.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e6/ec/e6ec528c7fbb34f928e2cdd08558ac3cc65c82f6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.media.2018.03.006"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Comparison Four Different Probability Sampling Methods based on Differential Evolution Algorithm

Qingbo Lu, Xueliang Zhang, Shuhua Wen, Guosheng Lan
<span title="2012-11-01">2012</span> <i title="Engineering and Technology Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hfcgk5yv5beurknenrhk7lzef4" style="color: black;">Journal of Advances in Information Technology</a> </i> &nbsp;
In this paper, segmentation problems in medical imaging modalities especially for lung CT as well as for thyroid ultrasound (US) are discussed along with their comparative results are shown using automatic  ...  Accurate segmentation of medical images is a key step in the use of computer-aided diagnosis (CAD) systems to improve the sensitivity and specificity of lesion detection.  ...  Shyang-Rong Shih Thyroid Segmentation and Volume Estimation in Ultrasound Images, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 57, NO. 6, JUNE 2010.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4304/jait.3.4.206-214">doi:10.4304/jait.3.4.206-214</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lt35ulkqwfbbbikn6xtlfhcoty">fatcat:lt35ulkqwfbbbikn6xtlfhcoty</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20160821210747/http://www.academypublisher.com/jait/vol03/no04/jait0304.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6a/64/6a64d284ea927275e696fca0d4e9957a9fa89309.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4304/jait.3.4.206-214"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

A combined local and global motion estimation and compensation method for cardiac CT

Qiulin Tang, Beshan Chiang, Akinola Akinyemi, Alexander Zamyatin, Bibo Shi, Satoru Nakanishi, Bruce R. Whiting, Christoph Hoeschen
<span title="2014-03-19">2014</span> <i title="SPIE"> Medical Imaging 2014: Physics of Medical Imaging </i> &nbsp;
In this paper, we proposed a fully automatic method for detecting the nipple location in 3D ultrasound breast images acquired from Automated Breast Ultrasound Systems.  ...  The method was applied on a dataset of 100 patients with 294 different 3D ultrasound views from Siemens and U-systems acquisition systems.  ...  This is a manual process and can be time consuming in cases where several sections using different stains are required.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.2043492">doi:10.1117/12.2043492</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fyzpc5m6jbh7fjohqpdmtzkhte">fatcat:fyzpc5m6jbh7fjohqpdmtzkhte</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20140308053954/http://spie.org:80/Documents/ConferencesExhibitions/MI14-Abstracts.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.2043492"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>
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