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Model-Based Pancreas Segmentation in Portal Venous Phase Contrast-Enhanced CT Images

Matthias Hammon, Alexander Cavallaro, Marius Erdt, Peter Dankerl, Matthias Kirschner, Klaus Drechsler, Stefan Wesarg, Michael Uder, Rolf Janka
2013 Journal of digital imaging  
This study aims to automatically detect and segment the pancreas in portal venous phase contrast-enhanced computed tomography (CT) images.  ...  The presented novel approach enables automatic pancreas segmentation in portal venous phase contrast-enhanced CT images which are included in almost every clinical routine abdominal CT examination.  ...  Conflict of Interest None Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided  ... 
doi:10.1007/s10278-013-9586-7 pmid:23471751 pmcid:PMC3824921 fatcat:sswmoekzerfzrlxh5wuzic6jya

Front Matter: Volume 10574

Proceedings of SPIE, Elsa D. Angelini, Bennett A. Landman
2018 Medical Imaging 2018: Image Processing  
using a Base 36 numbering system employing both numerals and letters.  ...  Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  [10574-67] 10574 1X Organ localization and identification in thoracic CT volumes using 3D CNNs leveraging spatial anatomic relations [10574-68] 10574 1Y Automatic valve segmentation in cardiac  ... 
doi:10.1117/12.2315755 fatcat:jdfbaent6vhu5dwlrqrqt66vce

A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling [article]

Amal Farag, Le Lu, Holger R. Roth, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
2016 arXiv   pre-print
We present a bottom-up approach for pancreas segmentation in abdominal CT scans that is based on a hierarchy of information propagation by classifying image patches at different resolutions; and cascading  ...  Finally, we implement a multi-atlas label fusion (MALF) approach for pancreas segmentation using the same datasets.  ...  Imaging Data 80 3D abdominal portal-venous contrast enhanced CT scans (∼ 70 seconds after intravenous contrast injection) acquired from 53 male and 27 female subjects are used in our study for evaluation  ... 
arXiv:1505.06236v2 fatcat:bi25x4jby5aavacjgwxyobtdcu

Front Matter: Volume 9784

2016 Medical Imaging 2016: Image Processing  
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  ...  MRI [9784-3] 9784 05 Automatic detection of cardiovascular risk in CT attenuation correction maps in Rb-82 PET/CTs [9784-4] 9784 06 Combining the boundary shift integral and tensor-based morphometry for  ...  ] 9784 3M BOLD delay times using group delay in sickle cell disease [9784-129] POSTER SESSION: IMAGE RESTORATION AND ENHANCEMENT 9784 3N Luminosity and contrast normalization in color retinal images  ... 
doi:10.1117/12.2240619 fatcat:kot6cogf4rf6dcjhkzdrr5gahi

A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling

Amal Farag, Le Lu, Holger R. Roth, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
2017 IEEE Transactions on Image Processing  
We present an automated bottomup approach for pancreas segmentation in abdominal computed tomography (CT) scans.  ...  The proposed method is evaluated on a dataset of 80 manually segmented CT volumes, using six-fold cross-validation.  ...  [5] is not comparable to the others, as it uses three-phase contrast enhanced CT images, as opposed to single phase CT images. • Shimizu et. al [5] use three-phase contrast enhanced CT data, which  ... 
doi:10.1109/tip.2016.2624198 pmid:27831881 fatcat:jbxl6zlwhbh57kydugjlsqxgmq

Classification of Liver Tumors from Computed Tomography Using NRSVM

S. Priyadarsini, Carlos Andrés Tavera Romero, M. Mrunalini, Ganga Rama Koteswara Rao, Sudhakar Sengan
2022 Intelligent Automation and Soft Computing  
A classification system is used for Benign Tumors (BT) and Malignant Tumors (MT) in the abdominal liver. Computed Tomography (CT) images based on enhanced RGS is proposed.  ...  Region Growing Segmentation (RGS) is employed to segment the LR, and Expectation-Maximization (EM) algorithm is used to segment the region of interest.  ...  LTS in CT Scans is deployed using the Modified SegNet technique, a Deep Learning technique for liver CT segmentation and Classification [1] .  ... 
doi:10.32604/iasc.2022.024786 fatcat:vamrt4ldqjhwlfw2dszy6bziqe

Anatomy-aided deep learning for medical image segmentation: a review

Lu Liu, Jelmer Maarten Wolterink, Christoph Brune, Raymond N J Veldhuis
2021 Physics in Medicine and Biology  
We address known and potentially solvable challenges in anatomy-aided deep learning and present a categorized methodology overview on using anatomical information with deep learning from over 70 papers  ...  Deep learning has become widely used for medical image segmentation in recent years. However, despite these advances, there are still problems for which deep learning-based segmentation fails.  ...  JW and CB acknowledge support by the Dutch 4TU HTSF program Precision Medicine.  ... 
doi:10.1088/1361-6560/abfbf4 pmid:33906186 fatcat:radimp5uorcalg3xlfjdesxo64

Pancreatic Cancer Survival Prediction: A Survey of the State-of-the-Art

Wilson Bakasa, Serestina Viriri, Huiling Chen
2021 Computational and Mathematical Methods in Medicine  
Because of bewildering and high volumes of data, the recent studies highlight the importance of machine learning (ML) algorithms like support vector machines and convolutional neural networks.  ...  This paper presents the state-of-the-art techniques used in cancer survival prediction, suggesting how these techniques can be implemented in predicting the overall survival of pancreatic ductal adenocarcinoma  ...  [100] implemented CNN to the Taiwanese Centre dataset with contrast-enhanced CT images of 370 patients with pdac and 320 controls.  ... 
doi:10.1155/2021/1188414 pmid:34630626 pmcid:PMC8497168 fatcat:3gwvh2gjlvd6plgpsnapafsjja

Statistical 4D graphs for multi-organ abdominal segmentation from multiphase CT

Marius George Linguraru, John A. Pura, Vivek Pamulapati, Ronald M. Summers
2012 Medical Image Analysis  
Contrast-enhanced CT scans were obtained at two phases: non-contrast and portal venous. Intra-patient data were spatially normalized by non-linear registration.  ...  Then 4D convolution using population training information of contrast-enhanced liver, spleen and kidneys was applied to multiphase data to initialize the 4D graph and adapt to patient-specific data.  ...  Sandberg, Visal Desai and Javed Aman for helping with the data analysis.  ... 
doi:10.1016/j.media.2012.02.001 pmid:22377657 pmcid:PMC3322299 fatcat:q24sslywtzg35nvvugkfmqpcwi

Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review [article]

Juan J. Cerrolaza, Mirella Lopez-Picazo, Ludovic Humbert, Yoshinobu Sato, Daniel Rueckert, Miguel Angel Gonzalez Ballester, Marius George Linguraru
2018 arXiv   pre-print
In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology.  ...  Inter-organ relations are not only spatial, but also functional and physiological.  ...  Acknowledgements This paper was supported in part by the Marie Skodoska-Curie Actions of the UE Framework Program for Research and Innovation, under REA grant agreement 706372.  ... 
arXiv:1812.08577v1 fatcat:xjw2g25pxfftpnduss6d5sggzu

A review of deep learning in medical imaging: Image traits, technology trends, case studies with progress highlights, and future promises [article]

S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers
2020 arXiv   pre-print
Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called  ...  In this survey paper, we first highlight both clinical needs and technical challenges in medical imaging and describe how emerging trends in deep learning are addressing these issues.  ...  Several groups have used data sets such as TCIA CT pancreas to improve pancreas segmentation with Dice coefficients reaching the mid 80 percentile [181] , [182] , [183] , [184] .  ... 
arXiv:2008.09104v1 fatcat:z2gic7or4vgnnfcf4joimjha7i

Brain Image Segmentation in Recent Years: A Narrative Review

Ali Fawzi, Anusha Achuthan, Bahari Belaton
2021 Brain Sciences  
Various segmentation methods ranging from simple intensity-based to high-level segmentation approaches such as machine learning, metaheuristic, deep learning, and hybridization are included in the present  ...  Brain image segmentation is one of the most time-consuming and challenging procedures in a clinical environment. Recently, a drastic increase in the number of brain disorders has been noted.  ...  The present review suggests that deep learning-based and hybrid-based metaheuristic methods are more efficient for the reliable segmentation of brain tumors.  ... 
doi:10.3390/brainsci11081055 fatcat:cdie3nuxzzfevoynik3iqtenli

A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
2017 Medical Image Analysis  
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area.  ...  This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year.  ...  Papers not reporting results on medical image data or only using standard feed-forward neural networks with handcrafted features were excluded.  ... 
doi:10.1016/j.media.2017.07.005 pmid:28778026 fatcat:esbj72ftwvbgzh6jgw367k73j4

Deep Learning for Medical Image Registration: A Comprehensive Review [article]

Subrato Bharati, M. Rubaiyat Hossain Mondal, Prajoy Podder, V. B. Surya Prasath
2022 arXiv   pre-print
This review focuses on monomodal and multimodal registration and associated imaging, for instance, X-ray, CT scan, ultrasound, and MRI.  ...  In recent years, there has been a tremendous surge in the development of deep learning (DL)-based medical image registration models.  ...  Using noisy segmentation and spatial gradients labels, the authors of [36] applied a registration approach to CT-CT registration for abdominal.  ... 
arXiv:2204.11341v1 fatcat:n6yacnk3ffdallbeirsgqpj274

An overview of deep learning in medical imaging [article]

Imran Ul Haq
2022 arXiv   pre-print
difficulties, lessons learned and future of DL in the field of medical science.  ...  Improved and innovative methods for data processing, image analysis and can significantly improve the diagnostic technologies and medicinal services gradually.  ...  For DL-based localization of anatomy region using CT scans, various data augmentation techniques were compared by [218] .  ... 
arXiv:2202.08546v1 fatcat:tg32btcm5vdsnlzeuhdttozj6m
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