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Locally Deformable Shape Model to Improve 3D Level Set Based Esophagus Segmentation

Sila Kurugol, Necmiye Ozay, Jennifer G. Dy, Gregory C. Sharp, Dana H. Brooks
<span title="">2010</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jsl2pgelqja2piczru3a6nqkg4" style="color: black;">2010 20th International Conference on Pattern Recognition</a> </i> &nbsp;
In this paper we propose a supervised 3D segmentation algorithm to locate the esophagus in thoracic CT scans using a variational framework.  ...  Finally, the esophageal wall is located within a 3D level set framework by optimizing a cost function including terms for appearance, the shape model, smoothness constraints and an air/contrast model.  ...  Acknowledgments Support for the work of SK and DHB provided in part by the NIH/NCRR Center for Integrative Biomedical Computing (CIBC), P41-RR12553-09, JD was also partly supported by NSF IIS-0347532  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icpr.2010.962">doi:10.1109/icpr.2010.962</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/21731883">pmid:21731883</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3127393/">pmcid:PMC3127393</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icpr/KurugolODSB10.html">dblp:conf/icpr/KurugolODSB10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3m6ypwnf5za47gvztkljdvevkq">fatcat:3m6ypwnf5za47gvztkljdvevkq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191105175022/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC3127393&amp;blobtype=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/1e/de/1ede1d5ad3c571596d38c0df8e7f3e06c50959d8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icpr.2010.962"> <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/PMC3127393" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Centerline extraction with principal curve tracing to improve 3D level set esophagus segmentation in CT images

S. Kurugol, E. Bas, D. Erdogmus, J. G. Dy, G. C. Sharp, D. H. Brooks
<span title="">2011</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/i3wdcpisqrhohjypmlikfya2la" style="color: black;">2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society</a> </i> &nbsp;
In this work we propose to initialize our previously introduced model based 3D level set esophagus segmentation method with a principal curve tracing (PCT) algorithm, which we adapted to solve the esophagus  ...  To locate the esophageal wall, the model based 3D level set algorithm including a shape model that represents the variance of esophagus wall around the estimated centerline is utilized.  ...  Recently we presented a model based 3D level set esophagus segmentation algorithm [7] over the entire thoracic range employing a shape model, with a global and a locally deformable component.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/iembs.2011.6090921">doi:10.1109/iembs.2011.6090921</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/22255070">pmid:22255070</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3349355/">pmcid:PMC3349355</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/embc/KurugolBEDSB11.html">dblp:conf/embc/KurugolBEDSB11</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/r5ogze3ygbckjg6srrb7cg3kki">fatcat:r5ogze3ygbckjg6srrb7cg3kki</a> </span>
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Esophagus Segmentation from 3D CT Data Using Skeleton Prior-Based Graph Cut

Damien Grosgeorge, Caroline Petitjean, Bernard Dubray, Su Ruan
<span title="">2013</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xytabl7eh5a5lle2ofnetovd7u" style="color: black;">Computational and Mathematical Methods in Medicine</a> </i> &nbsp;
We propose in this paper an original method to segment in thoracic CT scans the 3D esophagus using a skeleton-shape model to guide the segmentation.  ...  In this paper, we focus on esophagus segmentation, a challenging application since the wall of the esophagus, made of muscle tissue, has very low contrast in CT images.  ...  proposed a two-step segmentation method in cardiac CT: first, the esophagus centerline is extracted using probabilistic spatial and appearance modeling, and then the outer surface of the esophagus is extracted  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2013/547897">doi:10.1155/2013/547897</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24069065">pmid:24069065</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3773378/">pmcid:PMC3773378</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aozrkbwidzhipaijorpjb32yva">fatcat:aozrkbwidzhipaijorpjb32yva</a> </span>
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Channel-attention U-Net: Channel Attention Mechanism for Semantic Segmentation of Esophagus and Esophageal Cancer

Guoheng Huang, Junwen Zhu, Jiajian Li, Zhuowei Wang, Lianglun Cheng, Lizhi Liu, Haojiang Li, Jian Zhou
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
ACKNOWLEDGMENT All experimental datasets and technical support in this paper are from Sun Yat-sen University Cancer Center. Special thanks to Dr. Haojiang Li, Dr. Lizhi Liu and Dr. Jian Zhou.  ...  introduced an original method to segment the 3D esophagus from thoracic CT scans using a skeleton-shape model to guide the segmentation [5] .  ...  As shown in FIGURE. 7, a semiautomatic segmentation method for segmenting 3D esophagus or esophageal cancer in CT images is proposed.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3007719">doi:10.1109/access.2020.3007719</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fqtxo464djbh5akf7rx2idmkg4">fatcat:fqtxo464djbh5akf7rx2idmkg4</a> </span>
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SegTHOR: Segmentation of Thoracic Organs at Risk in CT images [article]

Z. Lambert, C. Petitjean, B. Dubray, S. Ruan
<span title="2019-12-12">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this dataset, the OARs are the heart, the trachea, the aorta and the esophagus, which have varying spatial and appearance characteristics.  ...  The dataset includes 60 3D CT scans, divided into a training set of 40 and a test set of 20 patients, where the OARs have been contoured manually by an experienced radiotherapist.  ...  Acknowledgements This project was co-financed by the European Union with the European regional development fund (ERDF, 18P03390/18E01750/18P02733) and by the Haute-Normandie Régional Council via the M2SINUM  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.05950v1">arXiv:1912.05950v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fvtfhj5cnbbzjoyk7mesc7xwva">fatcat:fvtfhj5cnbbzjoyk7mesc7xwva</a> </span>
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Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm

Minh-Trieu Tran, Soo-Hyung Kim, Hyung-Jeong Yang, Guee-Sang Lee, In-Jae Oh, Sae-Ryung Kang
<span title="2021-07-02">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
However, among the above organs, the esophagus is one of the most difficult organs to segment because of its small size, ambiguous boundary, and very low contrast in CT images.  ...  We also used the simultaneous truth and performance level estimation (STAPLE) algorithm to reach robust results for segmentation. Firstly, our model was trained by k-fold cross-validation.  ...  A skeleton-shape model to guide the segmentation [51] is proposed to segment in thoracic CT scans of the 3D esophagus.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21134556">doi:10.3390/s21134556</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vjtzoctckrfhthhydzqnh6bqfa">fatcat:vjtzoctckrfhthhydzqnh6bqfa</a> </span>
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One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs [article]

Jue Jiang, Harini Veeraraghavan
<span title="2022-01-26">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The registration network was trained in an unsupervised manner using pairs of planning CT (pCT) and CBCT images and produced a progressively deformed sequence of images.  ...  The segmentation network was optimized in a one-shot setting by combining progressively deformed pCT (anatomic context) and pCT delineations (shape context) with CBCT images.  ...  Shape and spatial priors for registration-segmentation Template shape constraints [29] as well as population level anatomical priors learned using a generative model were previously used to regularize  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2201.11000v1">arXiv:2201.11000v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dmc3ojw2mjhjnmy5zdd5n5wx2i">fatcat:dmc3ojw2mjhjnmy5zdd5n5wx2i</a> </span>
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A 3D fully convolutional neural network and a random walker to segment the esophagus in CT [article]

Tobias Fechter, Sonja Adebahr, Dimos Baltas, Ismail Ben Ayed, Christian Desrosiers, Jose Dolz
<span title="2017-04-21">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The outputs of the CNN and ACM are then used in addition to CT Hounsfield values to drive the RW. Evaluation and training was done on 50 CTs with peer reviewed esophagus contours.  ...  Results were assessed regarding spatial overlap and shape similarities.  ...  A detailed mathematical derivation can be found in 18 and 24 . The proposed RW makes use of CT image gradients and a prior model.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1704.06544v1">arXiv:1704.06544v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qyov2kuzwnh6flhrsrkz4ohrmi">fatcat:qyov2kuzwnh6flhrsrkz4ohrmi</a> </span>
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Automatic Segmentation Using a Hybrid Dense Network Integrated With an 3D-Atrous Spatial Pyramid Pooling Module for Computed Tomography (CT) Imaging

Abdul Qayyum, Iftikhar Ahmad, Wajid Mumtaz, Madini O. Alassafi, Rayed Alghamdi, Moona Mazher
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
The automatic segmentation and localization of organs from CT images could be helpful to diagnose the thoracic organs at risk in CT images.  ...  Target organs present a heterogeneous appearance that depends on shape, location, and size from patient to patient [42] and could induce a great challenge in pixel-based image segmentation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3024277">doi:10.1109/access.2020.3024277</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wyphc66xbbhw7jtz2xv7nxuzhu">fatcat:wyphc66xbbhw7jtz2xv7nxuzhu</a> </span>
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Esophagus segmentation in computed tomography images using a U-Net neural network with a semiautomatic labeling method

Xiao Lou, Youzhe Zhu, Kumaradevan Punithakumar, Lawrence H. Le, Baosheng Li
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Esophagus segmentation in computed tomography images is challenging due to the complex shape and low contrast of the esophagus.  ...  Flow chart of the proposed method for segmenting the esophagus in CT images. Xiao Lou et al.: Esophagus segmentation in CT using a U-Net neural network with semiautomatic labeling FIGURE 2.  ...  In a study by Rousson et al. in 2006 [13] , a probabilistic spatial model was employed to extract the centerline of the esophagus, and the outer wall of the esophagus was approximated by elliptical shape  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3035772">doi:10.1109/access.2020.3035772</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6lymi3xz75dkpi2umylqpjyura">fatcat:6lymi3xz75dkpi2umylqpjyura</a> </span>
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Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet) [article]

Sahar Yousefi, Hessam Sokooti, Mohamed S. Elmahdy, Irene M. Lips, Mohammad T. Manzuri Shalmani, Roel T. Zinkstok, Frank J.W.M. Dankers, Marius Staring
<span title="2021-03-24">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Manual or automatic delineation of the esophageal tumor in CT images is known to be very challenging.  ...  In this paper we aim to investigate if and to what extent a simplified clinical workflow based on CT alone, allows one to automatically segment the esophageal tumor with sufficient quality.  ...  Kurugol et al. presented a 3D level set model for segmenting the esophagus over the entire thoracic range employing a shape model, with a global and a locally deformable component [19] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2012.03242v3">arXiv:2012.03242v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jxke2c4kxrb23hzy5va77kf5ge">fatcat:jxke2c4kxrb23hzy5va77kf5ge</a> </span>
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Esophageal Tumor Segmentation in CT Images using a Dilated Dense Attention Unet (DDAUnet)

Sahar Yousefi, Hessam Sokooti, Mohamed S. Elmahdy, Irene M. Lips, Mohammad T. Manzuri Shalmani, Roel T. Zinkstok, Frank J.W.M. Dankers, Marius Staring
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l) Kurugol et al. presented a 3D level set model for segmenting the esophagus over the entire thoracic range employing a shape model, with a global and a locally  ...  Also, they proposed another method based on spatially-constrained shape interpolation in order to segment the esophagus in 2D CT images [15] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3096270">doi:10.1109/access.2021.3096270</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6e63h7hbvrcerazpqmnybtagcu">fatcat:6e63h7hbvrcerazpqmnybtagcu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210719193413/https://ieeexplore.ieee.org/ielx7/6287639/6514899/09481104.pdf?tp=&amp;arnumber=9481104&amp;isnumber=6514899&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/e2/8d/e28d341cf8b424d9b94f4860ef51932dd4467068.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3096270"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Three-Dimensional Reconstruction of Thoracic Structures: Based on Chinese Visible Human

Yi Wu, Na Luo, Liwen Tan, Binji Fang, Ying Li, Bing Xie, Kaijun Liu, Chun Chu, Min Li, Shaoxiang Zhang
<span title="">2013</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xytabl7eh5a5lle2ofnetovd7u" style="color: black;">Computational and Mathematical Methods in Medicine</a> </i> &nbsp;
The contour data set of segmented thoracic structures was imported to Amira software and 3D thorax models were reconstructed via surface rendering and volume rendering.  ...  We managed to establish three-dimensional digitized visible model of human thoracic structures and to provide morphological data for imaging diagnosis and thoracic and cardiovascular surgery.  ...  Acknowledgment The authors greatly thank the National Science Foundation of China (nos. 81100480 and 61190122) (http://www .nsfc.gov.cn/) for its financial support to this research work.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2013/795650">doi:10.1155/2013/795650</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24369489">pmid:24369489</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3857995/">pmcid:PMC3857995</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wkklxmijmbecbimoqubcmodxmq">fatcat:wkklxmijmbecbimoqubcmodxmq</a> </span>
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Generalizable Cone Beam CT Esophagus Segmentation Using Physics-Based Data Augmentation [article]

Sadegh R Alam, Tianfang Li, Pengpeng Zhang, Si-Yuan Zhang, Saad Nadeem
<span title="2021-01-30">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We developed a semantic physics-based data augmentation method for segmenting esophagus in both planning CT (pCT) and cone-beam CT (CBCT) using 3D convolutional neural networks. 191 cases with their pCT  ...  Automated segmentation of esophagus is critical in image guided/adaptive radiotherapy of lung cancer to minimize radiation-induced toxicities such as acute esophagitis.  ...  This problem is more prominent when segmenting a long tubular soft-tissue organ such as esophagus in noisy CBCT images where 3D spatial information is critical to avoid discontinuities and to deal with  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.15713v2">arXiv:2006.15713v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iszgmpwgpjerzpnxuwb3q2623i">fatcat:iszgmpwgpjerzpnxuwb3q2623i</a> </span>
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Progressive and Multi-path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images [chapter]

Adam P. Harrison, Ziyue Xu, Kevin George, Le Lu, Ronald M. Summers, Daniel J. Mollura
<span title="">2017</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Pathological lung segmentation (PLS) is an important, yet challenging, medical image application due to the wide variability of pathological lung appearance and shape.  ...  Along those lines, we present a bottom-up deep-learning based approach that is expressive enough to handle variations in appearance, while remaining unaffected by any variations in shape.  ...  We also prefer to use a sample estimate of the population balance, since we train on an entire training set, and not just on individual images.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-66179-7_71">doi:10.1007/978-3-319-66179-7_71</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4oavcvbxmjg5zd4lmljxijxcy4">fatcat:4oavcvbxmjg5zd4lmljxijxcy4</a> </span>
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