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Automated Segmentation of Mouse Brain Images Using Multi-Atlas Multi-ROI Deformation and Label Fusion

Jingxin Nie, Dinggang Shen
2012 Neuroinformatics  
We propose an automated multi-atlas and multi-ROI based segmentation method for both skullstripping of mouse brain and the ROI-labeling of mouse brain structures from the three dimensional (3D) magnetic  ...  Then, a multi-atlas and multi-ROI based deformable segmentation method is adopted to refine the ROI labeling result by deforming each ROI surface via boundary recognizers (i.e., SVM classifiers) trained  ...  Acknowledgments This work was supported in part by NIH grants EB006733, EB008374, EB009634, AG041721, and CA140413, by National Science Foundation of China under grant No. 61075010, and also by The National  ... 
doi:10.1007/s12021-012-9163-0 pmid:23055043 pmcid:PMC3538942 fatcat:llo6muovm5ay7fobxxsurvnjly

Multi-atlas segmentation with particle-based group-wise image registration

Joohwi Lee, Ilwoo Lyu, Martin Styner, Sebastien Ourselin, Martin A. Styner
2014 Medical Imaging 2014: Image Processing  
In the experiment, we show that our segmentation algorithm provides more accuracy with multi-atlas label fusion and stability against pair-wise image registration.  ...  We show that the use of a particle guided-image registration method can be naturally extended to a novel multi-atlas segmentation method and improves the registration method to explicitly use the provided  ...  Using a pair-wise image registration between an atlas and the target image, multi-atlas label fusion method makes use of more than one atlas to mediate potential bias associated with using a single atlas  ... 
doi:10.1117/12.2043333 pmid:25075158 pmcid:PMC4112129 dblp:conf/miip/LeeLS14 fatcat:5yd565j3wfh3todfycbr72e3xi

Towards an efficient segmentation of small rodents brain: a short critical review

Riccardo De Feo, Federico Giove
2019 Journal of Neuroscience Methods  
The former labels the target volume by registering one or more pre-labeled atlases using a deformable registration method, in which case the result depends on the quality of the reference volumes, the  ...  registration algorithm and the label fusion approach, if more than one atlas is employed.  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding bodies.  ... 
doi:10.1016/j.jneumeth.2019.05.003 pmid:31102669 fatcat:bv6m3zdlebfhbpf4htn4yhad2y

Performing label-fusion-based segmentation using multiple automatically generated templates

M. Mallar Chakravarty, Patrick Steadman, Matthijs C. van Eede, Rebecca D. Calcott, Victoria Gu, Philip Shaw, Armin Raznahan, D. Louis Collins, Jason P. Lerch
2012 Human Brain Mapping  
Multi-atlas-based approaches are used as a remedy and involve matching each subject to a number of manually labeled templates.  ...  The input atlases consist a highresolution mouse brain atlas and an atlas of the human basal ganglia and thalamus derived from serial histological data.  ...  SciNet is funded by the Canada Foundation for Innovation under the auspices of Compute Canada, the Government of Ontario, Ontario Research Fund -Research Excellence and the University of Toronto.  ... 
doi:10.1002/hbm.22092 pmid:22611030 pmcid:PMC4896505 fatcat:vrn5y7zndvhadklclm4j7xkpsy

Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases

Riccardo De Feo, Artem Shatillo, Alejandra Sierra, Juan Miguel Valverde, Olli Gröhn, Federico Giove, Jussi Tohka
2021 NeuroImage  
MU-Net achieved higher segmentation accuracy than state-of-the-art multi-atlas segmentation methods with an inference time of 0.35 seconds and no pre-processing requirements.  ...  We tested MU-Net with an unusually large dataset combining several independent studies consisting of 1,782 mouse brain MRI volumes of both healthy and Huntington animals, and measured average Dice scores  ...  We also extend our thanks to the Academy of Finland , grants (# 275453 to A.S. and # 298007 to O.G. #316258 to J.T.) and to the CHDI 'Cure Huntington's Disease Initiative' foundation, for kindly providing  ... 
doi:10.1016/j.neuroimage.2021.117734 pmid:33454412 fatcat:wvsukoknubgopnlkj57s3e2hze

Multi-Atlas Segmentation of Biomedical Images: A Survey [article]

Juan Eugenio Iglesias, Mert Rory Sabuncu
2015 arXiv   pre-print
Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004), Klein, Mensh, Ghosh, Tourville and Hirsch (2005), and Heckemann  ...  , Hajnal, Aljabar, Rueckert and Hammers (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications.  ...  Sabuncu is supported by NIH NIBIB 1K25EB013649-01 and a BrightFocus Alzheimer's disease pilot research grant (ahaf-a2012333).  ... 
arXiv:1412.3421v2 fatcat:6cgyslltmbfsxc72wimg6c7ofq

Multi-atlas segmentation of biomedical images: A survey

Juan Eugenio Iglesias, Mert R. Sabuncu
2015 Medical Image Analysis  
Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004) , Klein, Mensh, Ghosh, Tourville and Hirsch (2005) , and Heckemann  ...  , Hajnal, Aljabar, Rueckert and Hammers (2006) , is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications.  ...  Sabuncu is supported by NIH NIBIB 1K25EB013649-01 and a Bright-Focus Alzheimer's disease pilot research grant (AHAF-A2012333).  ... 
doi:10.1016/ pmid:26201875 pmcid:PMC4532640 fatcat:ovo6f7ekvnazfmer2fohshx3c4

Fully-Automated μMRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome

Nick M. Powell, Marc Modat, M. Jorge Cardoso, Da Ma, Holly E. Holmes, Yichao Yu, James O'Callaghan, Jon O. Cleary, Ben Sinclair, Frances K. Wiseman, Victor L. J. Tybulewicz, Elizabeth M. C. Fisher (+3 others)
2016 PLoS ONE  
We show the application of freely available open-source software developed for clinical MRI analysis to mouse brain data: NiftySeg for segmentation and NiftyReg for registration, and discuss atlases and  ...  We describe a fully automated pipeline for the morphometric phenotyping of mouse brains from μMRI data, and show its application to the Tc1 mouse model of Down syndrome, to identify new morphological phenotypes  ...  To create brain masks, we adopted a multi-atlas technique, employing Similarity and Truth Estimation for Propagated Segmentations (STEPS) label fusion [40] , implemented in NiftySeg, using NiftyReg for  ... 
doi:10.1371/journal.pone.0162974 pmid:27658297 pmcid:PMC5033246 fatcat:wjr7ooarezgo7b4i4jaxekixje

Front Matter: Volume 8669

Proceedings of SPIE, Sebastien Ourselin, David R. Haynor
2013 Medical Imaging 2013: Image Processing  
Handels, Univ. of Lübeck (Germany) POSTER SESSION: ATLASES 8669 1O Combined pixel classification and atlas-based segmentation of the ventricular system in brain CT Images [8669-59] P. C. Vos, I.  ...  (Japan) POSTER SESSION: LABEL FUSION 8669 2O iSTAPLE: improved label fusion for segmentation by combining STAPLE with image intensity [8669-97] X. Liu, A. Montillo, E. T. Tan, J. F.  ... 
doi:10.1117/12.2021914 fatcat:toykpoh74fgzvhbahmojzrszw4

Convolutional Neural Networks Enable Robust Automatic Segmentation of the Rat Hippocampus in MRI After Traumatic Brain Injury

Riccardo De Feo, Elina Hämäläinen, Eppu Manninen, Riikka Immonen, Juan Miguel Valverde, Xavier Ekolle Ndode-Ekane, Olli Gröhn, Asla Pitkänen, Jussi Tohka
2022 Frontiers in Neurology  
Automatic segmentations using MU-Net-R and multi-atlas registration were of excellent quality, achieving cross-validated Dice scores above 0.90 despite the presence of brain lesions, atrophy, and ventricular  ...  The performance of MU-Net-R was quantitatively compared with methods based on single and multi-atlas registration using MR images from two large preclinical cohorts.  ...  Multi-Atlas Segmentation To label the hippocampus in each target volume by combining the individually-registered atlases we applied two different label fusion strategies: STEPS multi-atlas segmentation  ... 
doi:10.3389/fneur.2022.820267 pmid:35250823 pmcid:PMC8891699 fatcat:mj7wbschsrcg5e3kduz6oefyfa

Collection of informatics proposals from 2007

Arno Klein
2016 Research Ideas and Outcomes  
This is a collection of brief proposals for informatics and image processing projects from 2007. Some are intended for the Mindboggle brain image software project  ...  We will adopt the general approach and slightly modify the nomenclature of a successful Bayesian framework for face identification (Moghaddam et al. 1998) .  ...  In that work, the authors formulate a probabilistic similarity measure which is based on the probability that the differences between two images are within the variations (e.g., expressions) of the  ... 
doi:10.3897/rio.2.e8813 fatcat:rbiw67qbqrcrrgw7ntusipum3e

Automated Edge-Aware Refinement of Anatomical Atlases in 3D and Its Application to the Developing Mouse Brain [article]

David M Young, Siavash Fazel Darbandi, Grace Schwartz, Zachary Bonzell, Deniz Yuruk, Mai Nojima, Laurent Gole, John Rubenstein, Weimiao Yu, Stephan J Sanders
2020 biorxiv/medrxiv   pre-print
Applying these techniques to the Allen Developing Mouse Brain Atlas (ADMBA), we demonstrate improvements, both qualitatively and quantitatively.  ...  Increasing utilization of 3D microscopy from intact tissues necessitates corresponding 3D atlases and anatomical labels.  ...  We greatly appreciate Eric Lam from the KAVLI-PBBR Fabrication and Design Center at UCSF for assistance with the initial design of the custom 3D printed microscopy brain mount.  ... 
doi:10.1101/2020.04.01.017665 fatcat:o66s5hocezcmfdw2debywyqthi

Fully automated dual-resolution serial optical coherence tomography aimed at diffusion MRI validation in whole mouse brains

Joël Lefebvre, Patrick Delafontaine-Martel, Philippe Pouliot, Hélène Girouard, Maxime Descoteaux
2018 Neurophotonics  
The capability of this system to perform multimodal imaging studies is demonstrated by labeling the ROIs using a mouse brain atlas and by categorizing the ROIs based on their associated dMRI measures.  ...  Using this imaging system, 3 whole mouse brains are imaged, and 250 high-resolution 40 × three-dimensional ROIs are acquired.  ...  We also thank Jean-Christophe Houde (SCIL) for this support in dMRI processing tools and pipeline using singularity.  ... 
doi:10.1117/1.nph.5.4.045004 pmid:30681668 pmcid:PMC6215086 fatcat:xrfqdx4z35byjfv2lmakixitzq

Constructing and optimizing 3D atlases from 2D data with application to the developing mouse brain

David M Young, Siavash Fazel Darbandi, Grace Schwartz, Zachary Bonzell, Deniz Yuruk, Mai Nojima, Laurent C Gole, John LR Rubenstein, Weimiao Yu, Stephan J Sanders
2021 eLife  
We generated imaging data from fifteen whole mouse brains to validate atlas performance and observed qualitative and quantitative improvement (37% greater alignment between atlas and anatomical boundaries  ...  Applying these methods to the eight developmental stages in the Allen Developing Mouse Brain Atlas (ADMBA) led to more comprehensive and accurate atlases.  ...  Note to editors and reviewers: The refined ADMBA atlas files and wild-type brain images are too large to be included as supplements.  ... 
doi:10.7554/elife.61408 pmid:33570495 pmcid:PMC7994002 fatcat:bwzrphk2fjddjhjspp7ran4gw4

Primatologist: A modular segmentation pipeline for macaque brain morphometry

Yaël Balbastre, Denis Rivière, Nicolas Souedet, Clara Fischer, Anne-Sophie Hérard, Susannah Williams, Michel E. Vandenberghe, Julien Flament, Romina Aron-Badin, Philippe Hantraye, Jean-François Mangin, Thierry Delzescaux
2017 NeuroImage  
In patients, magnetic resonance imaging (MRI) combined with automated segmentation methods has offered the unique opportunity to assess in vivo brain morphological changes.  ...  Meanwhile, specific challenges caused by brain size and high field contrasts make existing algorithms hard to use routinely in NHPs.  ...  -of a representative collection of segmented images towards the target image space and the fusion of their labels.  ... 
doi:10.1016/j.neuroimage.2017.09.007 pmid:28899745 fatcat:qyccho26avgyjhbywyc6ejghz4
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