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Automated Segmentation and Anatomical Labeling of Abdominal Arteries Based on Multi-organ Segmentation from Contrast-Enhanced CT Data [chapter]

Yuki Suzuki, Toshiyuki Okada, Masatoshi Hori, Futoshi Yokota, Marius George Linguraru, Noriyuki Tomiyama, Yoshinobu Sato
2013 Lecture Notes in Computer Science  
A fully automated method is described for segmentation and anatomical labeling of the abdominal arteries from contrast-enhanced CT data of the upper abdomen.  ...  based on a basic anatomical constraint that arteries supplying blood to an organ consist of tree structures whose root nodes are located in the aorta region and leaf nodes in the organ region.  ...  This work is partly supported by MEXT Grand-in-Aid for Scientific Research on Innovative Areas No. 21103003.  ... 
doi:10.1007/978-3-642-38079-2_9 fatcat:fsbibcwnjfghtm2os74csmk7mi

Multi-atlas pancreas segmentation: Atlas selection based on vessel structure

Ken'ichi Karasawa, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Chengwen Chu, Guoyan Zheng, Daniel Rueckert, Kensaku Mori
2017 Medical Image Analysis  
Also, we investigate two types of applications of the vessel structure information to the atlas selection. Our segmentations were evaluated on 150 abdominal contrast-enhanced CT volumes.  ...  Automated organ segmentation from medical images is an indispensable component for clinical applications such as computer-aided diagnosis (CAD) and computer-assisted surgery (CAS).  ...  wisdom based on image recognition and understanding and its application to computer-assisted diagnosis and surgery", and the Kayamori Foundation of Informational Science Advancement.  ... 
doi:10.1016/j.media.2017.03.006 pmid:28410505 fatcat:4vqifwfef5daxpbasdch5ga36y

Front Matter: Volume 11313

Bennett A. Landman, Ivana Išgum
2020 Medical Imaging 2020: Image Processing  
The papers in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee.  ...  The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon.  ...  using a Siamese ResNet trained on similarity labels from disparity maps of cerebral MRA MIP pairs [11313-94] viii Proc. of SPIE Vol. 11313 1131301-8 Validation and optimization of multi-organ segmentation  ... 
doi:10.1117/12.2570657 fatcat:be32besqknaybh6wibz7unuboa

Multi-Contrast Computed Tomography Healthy Kidney Atlas [article]

Ho Hin Lee, Yucheng Tang, Kaiwen Xu, Shunxing Bao, Agnes B. Fogo, Raymond Harris, Mark P. de Caestecker, Mattias Heinrich, Jeffrey M. Spraggins, Yuankai Huo, Bennett A. Landman
2020 arXiv   pre-print
Hence, we proposed a high-resolution CT retroperitoneal atlas specifically optimized for the kidney across non-contrast CT and early arterial, late arterial, venous and delayed contrast enhanced CT.  ...  Briefly, we introduce a deep learning-based volume of interest extraction method and an automated two-stage hierarchal registration pipeline to register abdominal volumes to a high-resolution CT atlas  ...  The identified datasets used for the analysis described were obtained from the Research Derivative (RD), database of clinical and related data.  ... 
arXiv:2012.12432v2 fatcat:q24uyoiaavbydlznmfxpwaw4dm

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
of histology Better Fiber ODFs From Suboptimal Data With Autoencoder Based Regularization 656 Multi-Label Transduction for Identifying Disease Comorbidity Patterns 659 Training Multi-organ Segmentation  ...  fully convolutional networks for abdominal multi-organ segmentation 450 Autofocus Layer for Semantic Segmentation 451 A Framework for Identifying Diabetic Retinopathy Based on Anti-noise Detection and  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

Front Matter: Volume 12032

Ivana Išgum, Olivier Colliot
2022 Medical Imaging 2022: Image Processing  
Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  .  The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters. These two-number sets start with 00,  ...  image segmentation [12032-30] 0T Effective hyperparameter optimization with proxy data for multi-organ segmentation [12032-31] 0U Spatial label smoothing via aleatoric uncertainty for bleeding region  ... 
doi:10.1117/12.2638192 fatcat:ikfgnjefaba2tpiamxoftyi6sa

Front Matter: Volume 9784

2016 Medical Imaging 2016: Image Processing  
Combining multi-atlas segmentation with brain surface estimation [9784-13] 9784 0F Automated segmentation of upper digestive tract from abdominal contrast-enhanced CT data using hierarchical statistical  ...  modeling of organ interrelations [9784-14] SESSION 4 SEGMENTATION: BRAIN 9784 0G Segmentation and labeling of the ventricular system in normal pressure hydrocephalus using patch-based tissue classification  ...  0U Whole abdominal wall segmentation using augmented active shape models (AASM) with multi-atlas label fusion and level set SESSION 7 MOTION/REGISTRATION 0V Image-based navigation for functional  ... 
doi:10.1117/12.2240619 fatcat:kot6cogf4rf6dcjhkzdrr5gahi

Mesenteric Vasculature-Guided Small Bowel Segmentation on 3-D CT

Weidong Zhang, Jiamin Liu, Jianhua Yao, Adeline Louie, Tan B. Nguyen, Stephen Wank, Wieslaw L. Nowinski, Ronald M. Summers
2013 IEEE Transactions on Medical Imaging  
However, segmenting the small bowel directly on CT scans is very difficult because of the low image contrast on CT scans and high tortuosity of the small bowel and its close proximity to other abdominal  ...  The major mesenteric arteries are first segmented using a vessel tracing method based on multi-linear subspace vessel model and Bayesian inference.  ...  Acknowledgments This research was supported by the intramural research programs of the National Institute of Health Clinical Center and NIDDK.  ... 
doi:10.1109/tmi.2013.2271487 pmid:23807437 pmcid:PMC4224016 fatcat:lp7lvxjz7fbjpcbootmxjxl2hq

Atlas-Based Automated Segmentation of Spleen and Liver Using Adaptive Enhancement Estimation [chapter]

Marius George Linguraru, Jesse K. Sandberg, Zhixi Li, John A. Pura, Ronald M. Summers
2009 Lecture Notes in Computer Science  
The paper presents the automated segmentation of spleen and liver from contrast-enhanced CT images of normal and hepato/splenomegaly populations.  ...  The method used 4 steps: (i) a mean organ model was registered to the patient CT; (ii) the first estimates of the organs were improved by a geodesic active contour; (iii) the contrast enhancements of liver  ...  This work was supported by the Intramural Research Program of the National Institutes of Health, Clinical Center.  ... 
doi:10.1007/978-3-642-04271-3_121 pmid:20448837 pmcid:PMC2864531 fatcat:pqf3jzknjfhalnqv7coadzlapm

Sequential Monte Carlo tracking of the marginal artery by multiple cue fusion and random forest regression

Kevin M. Cherry, Brandon Peplinski, Lauren Kim, Shijun Wang, Le Lu, Weidong Zhang, Jianfei Liu, Zhuoshi Wei, Ronald M. Summers
2015 Medical Image Analysis  
enhanced vessel segments.  ...  employing sequential Monte Carlo tracking (also known as particle filtering tracking) by multiple cue fusion based on intensity, vesselness, organ detection, and minimum spanning tree information for poorly  ...  Acknowledgements This work was supported by the Intramural Research Programs of the NIH Clinical Center and by a Cooperative Research and Development Agreement with iCAD.  ... 
doi:10.1016/j.media.2014.09.006 pmid:25461335 pmcid:PMC4314370 fatcat:zqnbchn6bnb3tjzaiwlfbuuyoy

An application of cascaded 3D fully convolutional networks for medical image segmentation

Holger R. Roth, Hirohisa Oda, Xiangrong Zhou, Natsuki Shimizu, Ying Yang, Yuichiro Hayashi, Masahiro Oda, Michitaka Fujiwara, Kazunari Misawa, Kensaku Mori
2018 Computerized Medical Imaging and Graphics  
In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation  ...  This reduces the number of voxels the second FCN has to classify to ~10% and allows it to focus on more detailed segmentation of the organs and vessels.  ...  Conflict of interest statement: The authors declare that they have no conflict of interest. References  ... 
doi:10.1016/j.compmedimag.2018.03.001 pmid:29573583 fatcat:6ihzswvpurdfval676ptnebv4q

Applying 3D U-Net Architecture to the Task of Multi-Organ Segmentation in Computed Tomography

Pavlo Radiuk
2020 Applied Computer Systems  
We also compared our architecture with recognised state-of-the-art results and demonstrated that 3D U-Net based architectures could achieve competitive performance and efficiency in the multi-organ segmentation  ...  Our hand-crafted architecture was trained and tested on the manually compiled dataset of CT scans.  ...  We accumulated a total of 400 images with high-resolution of abdominal CT scans to provide axial and multi-dimensional segmentation of seven human organs.  ... 
doi:10.2478/acss-2020-0005 fatcat:e77qbkxn4ncw5pahmujzwrttma

Abdominal multi-organ segmentation from CT images using conditional shape–location and unsupervised intensity priors

Toshiyuki Okada, Marius George Linguraru, Masatoshi Hori, Ronald M. Summers, Noriyuki Tomiyama, Yoshinobu Sato
2015 Medical Image Analysis  
This paper addresses the automated segmentation of multiple organs in upper abdominal computed tomography (CT) data.  ...  The proposed framework was evaluated through the segmentation of eight abdominal organs (liver, spleen, left and right kidneys, pancreas, gallbladder, aorta, and inferior vena cava) from 134 CT data from  ...  Institutes of Health, Clinical Center, and a philanthropic gift from the Government of Abu Dhabi to Children's National Medical Center.  ... 
doi:10.1016/j.media.2015.06.009 pmid:26277022 pmcid:PMC4679509 fatcat:lkfuon5fffemffckdgk4v66ghm

Front Matter: Volume 10574

Proceedings of SPIE, Elsa D. Angelini, Bennett A. Landman
2018 Medical Imaging 2018: Image Processing  
Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  .  The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters. These two-number sets start with 00, 01, 02, 03,  ...  ] 10574 2D Aorta and pulmonary artery segmentation using optimal surface graph cuts in non- contrast CT [10574-84] 10574 2E Model based rib-cage unfolding for trauma CT [10574-85] 10574 2F Thoracic  ... 
doi:10.1117/12.2315755 fatcat:jdfbaent6vhu5dwlrqrqt66vce

Front Matter: Volume 10134

2017 Medical Imaging 2017: Computer-Aided Diagnosis  
Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  .  The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters. These two-number sets start with 00, 01, 02, 03, 04,  ...  analysis-based automatic abdominal blood vessel segmentation through contrast enhanced CT [10134-154] 10134 4D Classification of bifurcations regions in IVOCT images using support vector machine and artificial  ... 
doi:10.1117/12.2277119 dblp:conf/micad/X17 fatcat:ika7pheqxngdxejyvkss4dkbv4
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