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LOGISMOS—Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces: Cartilage Segmentation in the Knee Joint

Yin Yin, Xiangmin Zhang, Rachel Williams, Xiaodong Wu, Donald D Anderson, Milan Sonka
2010 IEEE Transactions on Medical Imaging  
A novel method for simultaneous segmentation of multiple interacting surfaces belonging to multiple interacting objects, called LOGISMOS (layered optimal graph image segmentation of multiple objects and  ...  Although trained on only a relatively small number of nine example images, this system achieved good performance.  ...  Acknowledgments The contributions of C. Van Hofwegen, N. Laird, and N. Muhlenbruch who provided initial knee joint manual tracings, are gratefully acknowledged.  ... 
doi:10.1109/tmi.2010.2058861 pmid:20643602 pmcid:PMC3131162 fatcat:hnwxif523fcldeayigglefjuwu

Computer-Assisted Analysis of Biomedical Images [article]

Leonardo Rundo
2021 arXiv   pre-print
In this regard, frameworks based on advanced Machine Learning and Computational Intelligence can significantly improve traditional Image Processing and Pattern Recognition approaches.  ...  Therefore, the computational analysis of medical and biological images plays a key role in radiology and laboratory applications.  ...  Brain tumor segmentation based on a CA model This section presents a novel semi-automatic MR brain tumor image segmentation approach (GTVCUT) based on a CA model.  ... 
arXiv:2106.04381v1 fatcat:osqiyd3sbja3zgrby7bf4eljfm

Skull-stripping with machine learning deformable organisms

Gautam Prasad, Anand A. Joshi, Albert Feng, Arthur W. Toga, Paul M. Thompson, Demetri Terzopoulos
2014 Journal of Neuroscience Methods  
Our method borrows ideas from artificial life to govern a set of deformable models.  ...  We tested our method on 838 T1-weighted images, evaluating results using distance and overlap error metrics based on expert gold standard segmentations.  ...  Additional support was from the UCLA I2-IDRE Research Informatics and Computational Data Development Grant to PT and GP.  ... 
doi:10.1016/j.jneumeth.2014.07.023 pmid:25124851 pmcid:PMC4169789 fatcat:52jr4sxp7na2hflws4xhoxgcz4

External Force for Deformable Models in Medical Image Segmentation: A Survey

Basavaraj Amarapur, P.K Kulkarni
2011 Signal & Image Processing An International Journal  
The main purpose of survey is to identify and discuss each category with its principle, mathematical model, advantages, disadvantages and applications to medical image analysis.  ...  These models also support highly intuitive interaction mechanisms, which helps medical practitioners to bring their expertise to bear on the model-based image interpretation task.  ...  Suhuai Luo et al [27] proposed a new method to segment a tumour in brain MR images. Method is based on combining of balloon force and GVF models.  ... 
doi:10.5121/sipij.2011.2208 fatcat:nivreffym5hplfptkdi57tbyfa

Analysis of macular OCT images using deformable registration

Min Chen, Andrew Lang, Howard S. Ying, Peter A. Calabresi, Jerry L. Prince, Aaron Carass
2014 Biomedical Optics Express  
Examples of these applications are provided to demonstrate the potential benefits such techniques can have on our understanding of retinal disease.  ...  The approach begins with an initial translation to align the fovea of each subject, followed by a linear rescaling to align the top and bottom retinal boundaries.  ...  Acknowledgments This work was supported by the NIH/NEI under grant R21-EY022150, NIH/NINDS R01-NS082347 and the Intramural Research Program of NINDS.  ... 
doi:10.1364/boe.5.002196 pmid:25071959 pmcid:PMC4102359 fatcat:j3wjamhx4zgdvdyfylvvwi7ys4

Multimodal characterization of the human nucleus accumbens

Samuel CD. Cartmell, Qiyuan Tian, Brandon J. Thio, Christoph Leuze, Li Ye, Nolan R. Williams, Grant Yang, Gabriel Ben-Dor, Karl Deisseroth, Warren M. Grill, Jennifer A. McNab, Casey H. Halpern
2019 NeuroImage  
this region.  ...  These observations motivate a segmentation of the NAc into subregions, which we produce from a diffusion-tractography based analysis of 245 young, unrelated healthy subjects.  ...  The base of the model was set to zero potential to represent distant current return to the IPG in the chest, the internal boundaries within the model were set to continuity, and the outer boundary was  ... 
doi:10.1016/j.neuroimage.2019.05.019 pmid:31077843 pmcid:PMC7341972 fatcat:o73orjsdm5cmldlejnzd6hl3za

High-Resolution Fiber Tract Reconstruction in the Human Brain by Means of Three-Dimensional Polarized Light Imaging

Markus Axer, David Grässel, Melanie Kleiner, Jürgen Dammers, Timo Dickscheid, Julia Reckfort, Tim Hütz, Björn Eiben, Uwe Pietrzyk, Karl Zilles, Katrin Amunts
2011 Frontiers in Neuroinformatics  
To exemplarily highlight the potential of this approach, fiber orientation maps and 3D fiber models were reconstructed in selected regions of the brain (e.g., Corpus callosum, Internal capsule, Pons).  ...  It is based on the birefringence of the myelin sheaths surrounding axons, and enables the high-resolution analysis of myelinated axons constituting the fiber tracts. 3D-PLI provides the mapping of spatial  ...  Our work was partly supported by the Initiative and Network Fund of the Helmholtz Association within the Helmholtz Alliance on Systems Biology ("Human Brain Model").  ... 
doi:10.3389/fninf.2011.00034 pmid:22232597 pmcid:PMC3248698 fatcat:ilxr5uofsvgcnllhus3gzjlepq

Medical Imaging Synthesis using Deep Learning and its Clinical Applications: A Review [article]

Tonghe Wang, Yang Lei, Yabo Fu, Walter J. Curran, Tian Liu, Xiaofeng Yang
2020 arXiv   pre-print
performances with related clinical applications on representative studies.  ...  This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application.  ...  The 377 synthetic MR image sets with tumor labels were then incorporated into segmentation model training.  ... 
arXiv:2004.10322v1 fatcat:bkhct7wzjnfrrd4kwa4rqw6rbe

Image Segmentation with a Unified Graphical Model

Lei Zhang, Qiang Ji
2010 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Experimental results on the Weizmann horse data set, on the VOC2006 cow data set, and on the MSRC2 multiclass data set demonstrate that our approach achieves favorable results compared to state-of-the-art  ...  Specifically, we first propose to employ Conditional Random Field (CRF) to model the spatial relationships among image superpixel regions and their measurements.  ...  The authors also want to thank the anonymous reviewers and the editors who gave them valuable comments on this work.  ... 
doi:10.1109/tpami.2009.145 pmid:20558874 fatcat:d3v5j5h3zzbk7eusno2xpa4gri

Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI [article]

Endre Grøvik, Darvin Yi, Michael Iv, Elisabeth Tong, Daniel L. Rubin, Greg Zaharchuk
2019 arXiv   pre-print
Detecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of multi-sequence 3D imaging.  ...  This study demonstrates automated detection and segmentation of brain metastases on multi-sequence MRI using a deep learning approach based on a fully convolution neural network (CNN).  ...  Işın A, Direkoğlu C, Şah M: Review of MRI-based Brain double dose contrast-enhanced magnetic resonance Tumor Image Segmentation Using Deep Learning imaging for clear delineation of gross  ... 
arXiv:1903.07988v1 fatcat:igpmay2pzve3do4ctt26sn3ys4

Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering

A. Ortiz, J.M. Gorriz, J. Ramirez, D. Salas-Gonzalez
2014 Information Sciences  
The primary brain image segmentation goal is to partition a given brain image into different regions representing anatomical structures.  ...  Magnetic resonance image (MRI) segmentation is especially interesting because the accurate representation of white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders  ...  Similar to the way that a human expert has to learn to recognise different regions on the MR image, segmentation algorithms are based on prior knowledge.  ... 
doi:10.1016/j.ins.2013.10.002 fatcat:4sgzyuipubfztd3mjzakubgylu

Automatic MRI Database Exploration and Applications

Alexandre Guimond, Gérard Subsol, Jean-Philippe Thirion
1997 International journal of pattern recognition and artificial intelligence  
We present as examples of applications the building of an average volume of interest and preliminary results of classi cation according to morphology. keywords image database, exploration, volume of interest  ...  The design of representative models of the human body is of great interest to medical doctors.  ...  On light of those results, comparisons bases on mutual information techniques are expected to give good classi cations.  ... 
doi:10.1142/s0218001497000627 fatcat:gdn22nx5lffrxmbnmbq53osua4

Hippocampal gray matter reduction associates with memory deficits in adolescents with history of prematurity

Mónica Giménez, Carme Junqué, Ana Narberhaus, Xavier Caldú, Pilar Salgado-Pineda, Núria Bargalló, Dolors Segarra, Francesc Botet
2004 NeuroImage  
Using optimized voxel-based morphometry (VBM), we compared the relationship between hippocampal and thalamic gray matter loss and memory impairment in 22 adolescents with history of prematurity (HP) and  ...  Using stereological methods, we also observed a reduction in hippocampal volume, with left posterior predominance.  ...  Caldú , a research grant from the University of Barcelona to A. Narberhaus, and the grant AP2002-0737 (Ministerio de Educación, Cultura y Deporte) to M.  ... 
doi:10.1016/j.neuroimage.2004.07.029 pmid:15528087 fatcat:aw5r2uue2jaxbnevu5c6qo5gjm

Modeling & Analysis

2003 NeuroImage  
Thus, it was inclusive than exclusively fully on "double-blind, placebo-controlled" experiments.  ...  Most of the automatic brain image segmentation algorithms are using the MNI templates, which have been generated from MR images of young healthy individuals (mean age 23.4+-4.1 years).  ...  Conclusion We propose a processing chain to analyse structural brain changes in time series of MR images.  ... 
doi:10.1016/s1053-8119(05)70006-9 fatcat:zff2suxcofbxvetfrwfwcxi3zm

Intersection Based Motion Correction of Multislice MRI for 3-D in Utero Fetal Brain Image Formation

K. Kim, P.A. Habas, F. Rousseau, O.A. Glenn, A.J. Barkovich, C. Studholme
2010 IEEE Transactions on Medical Imaging  
The method is tested on simulated data with known motions and is applied to retrospectively reconstruct 3-D images from a range of clinically acquired imaging studies.  ...  In recent years, postprocessing of fast multislice magnetic resonance imaging (MRI) to correct fetal motion has provided the first true 3-D MR images of the developing human brain in utero.  ...  ACKNOWLEDGMENT The authors would also like to thank Dr. R. Henry for advice on the imaging protocols and assistance with the transfer of imaging data.  ... 
doi:10.1109/tmi.2009.2030679 pmid:19744911 pmcid:PMC3328314 fatcat:3q6ynjl5kvhlfc2656ofsa2qmu
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