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Hippocampal Segmentation from Longitudinal Infant Brain MR Images via Classification-guided Boundary Regression Y

Yeqin Shao, Jaeil Kim, Yaozong Gao, Qian Wang, Weili Lin, Dinggang Shen
<span title="">2019</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;
Specifically, we first employ a classification-guided regression forest to predict the 3D displacements from individual image voxels to the potential hippocampal boundaries.  ...  Hippocampal segmentation from infant brain MR images is indispensable for studying early brain development.  ...  the deformable model for final hippocampal segmentation in the infant brain MR images.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2904143">doi:10.1109/access.2019.2904143</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d57rup3ejfeflkachkgya324te">fatcat:d57rup3ejfeflkachkgya324te</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429113633/https://ieeexplore.ieee.org/ielx7/6287639/8600701/08664424.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/e8/1ee89df788908835433a7f0eaf68c89e47600a70.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2904143"> <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>

Segmenting hippocampal subfields from 3T MRI with multi-modality images

Zhengwang Wu, Yaozong Gao, Feng Shi, Guangkai Ma, Valerie Jewells, Dinggang Shen
<span title="">2018</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kpkfymbkufcnzjfc5ydyokby4y" style="color: black;">Medical Image Analysis</a> </i> &nbsp;
In this paper, we propose an automatic learning-based hippocampal subfields segmentation method using 3T multi-modality MR images, including structural MRI (T1, T2) and resting state fMRI (rs-fMRI).  ...  In the training stage, these extracted features are adopted to train a structured random forest classifier, which is further iteratively refined in an auto-context model by adopting the context features  ...  In subsection 2.4, structured random forest classifier is introduced, which is used as our segmentation classifier.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.media.2017.09.006">doi:10.1016/j.media.2017.09.006</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28961451">pmid:28961451</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5709221/">pmcid:PMC5709221</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hlogfs4m4bezbc7kmk24apbpz4">fatcat:hlogfs4m4bezbc7kmk24apbpz4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200428093247/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5709221&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/27/e5/27e59795a78de91f3a50800690993fdfa11aa544.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.media.2017.09.006"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5709221" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Diagnostic Performance of MRI Volumetry in Epilepsy Patients With Hippocampal Sclerosis Supported Through a Random Forest Automatic Classification Algorithm

Juan Pablo Princich, Patricio Andres Donnelly-Kehoe, Alvaro Deleglise, Mariana Nahir Vallejo-Azar, Guido Orlando Pascariello, Pablo Seoane, Jose Gabriel Veron Do Santos, Santiago Collavini, Alejandro Hugo Nasimbera, Silvia Kochen
<span title="2021-02-22">2021</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4kchoxo3jrfubkck3y7nfncina" style="color: black;">Frontiers in Neurology</a> </i> &nbsp;
We further validated the results by a state-of-the-art machine learning classification algorithm (Random Forest) computing accuracy and feature relevance to distinguish between patients and controls.  ...  Several methods offer free volumetry services for MR data that adequately quantify volume differences in the hippocampus and its subregions.  ...  (B) Right hippocampal 3D models for the same subject, constructed using all subfields from both methods in 3D Slicer 2 ; boxplots represent mean hippocampal volumes for left and right hippocampi in HC  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fneur.2021.613967">doi:10.3389/fneur.2021.613967</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33692740">pmid:33692740</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7937810/">pmcid:PMC7937810</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/m6aipgn3krab3ieq7tfwc75tla">fatcat:m6aipgn3krab3ieq7tfwc75tla</a> </span>
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Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm

Rosalia Maglietta, Nicola Amoroso, Marina Boccardi, Stefania Bruno, Andrea Chincarini, Giovanni B. Frisoni, Paolo Inglese, Alberto Redolfi, Sabina Tangaro, Andrea Tateo, Roberto Bellotti
<span title="2015-07-09">2015</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/at6h2du27zgp5hrmgtzl2rteea" style="color: black;">Pattern Analysis and Applications</a> </i> &nbsp;
In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented.  ...  RUSBoost is an algorithm specifically designed for imbalanced classification, suitable for large data sets because it uses random undersampling of the majority class.  ...  Random forest Random Forest uses multiple binary decision trees.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10044-015-0492-0">doi:10.1007/s10044-015-0492-0</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/27110218">pmid:27110218</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4828512/">pmcid:PMC4828512</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7mx52epbbfh3flqxg7oz53ukwm">fatcat:7mx52epbbfh3flqxg7oz53ukwm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201107105926/https://archive-ouverte.unige.ch/files/downloads/0/0/1/1/1/5/3/5/unige_111535_attachment01.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/c9/3b/c93b396a4a7853286a073db3bea8c855a0b32a3f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10044-015-0492-0"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4828512" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Automatic Hippocampal Subfield Segmentation from 3T Multi-modality Images [chapter]

Zhengwang Wu, Yaozong Gao, Feng Shi, Valerie Jewells, Dinggang Shen
<span title="">2016</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;
In this paper, we propose an automatic learning-based hippocampal subfields segmentation framework using multi-modality 3TMR images, including T1 MRI and resting-state fMRI (rs-fMRI).  ...  Next, corresponding appearance and relationship features from both 3T T1 MRI and rs-fMRI are extracted to train a structured random forest as a multilabel classifier to conduct the segmentation.  ...  the random forest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-47157-0_28">doi:10.1007/978-3-319-47157-0_28</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/28603791">pmid:28603791</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5464731/">pmcid:PMC5464731</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/44at452qqngcxeh75gxzgrbe5m">fatcat:44at452qqngcxeh75gxzgrbe5m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200206130544/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5464731&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/bc/f4/bcf4b2a0b9fa610198eed7f7941989a146c53ee2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-47157-0_28"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5464731" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Diagnosis of Alzheimer's Disease via Multi-Modality 3D Convolutional Neural Network

Yechong Huang, Jiahang Xu, Yuncheng Zhou, Tong Tong, Xiahai Zhuang
<span title="2019-05-31">2019</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wrk3kouosrhcxiprcbguskdipu" style="color: black;">Frontiers in Neuroscience</a> </i> &nbsp;
In this paper, we propose a convolutional neural network (CNN) to integrate all the multi-modality information included in both T1-MR and FDG-PET images of the hippocampal area, for the diagnosis of AD  ...  Finally, the results have demonstrated that (1) segmentation is not a prerequisite when using a CNN for the classification, (2) the combination of two modality imaging data generates better results.  ...  ACKNOWLEDGMENTS Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnins.2019.00509">doi:10.3389/fnins.2019.00509</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31213967">pmid:31213967</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6555226/">pmcid:PMC6555226</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oahckqwqtzanvhiudlx7bnqr2u">fatcat:oahckqwqtzanvhiudlx7bnqr2u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200208124744/https://fjfsdata01prod.blob.core.windows.net/articles/files/448373/pubmed-zip/.versions/1/.package-entries/fnins-13-00509/fnins-13-00509.pdf?sv=2015-12-11&amp;sr=b&amp;sig=Xt9Y0%2FydlZ6%2F%2FyjlAaLplewgNHYpmUbn1uH6d8ORB8E%3D&amp;se=2020-02-08T12%3A48%3A13Z&amp;sp=r&amp;rscd=attachment%3B%20filename%2A%3DUTF-8%27%27fnins-13-00509.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/cf/b2/cfb2948bc7f1633d01ccb270f218b5ff68fec561.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnins.2019.00509"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> frontiersin.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6555226" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Front Matter: Volume 10133

Proceedings of SPIE, Martin A. Styner, Elsa D. Angelini
<span title="2017-04-04">2017</span> <i title="SPIE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ha25cznnjncxtjoykhsg6fz5ly" style="color: black;">Medical Imaging 2017: Image Processing</a> </i> &nbsp;
random forest [10133-68]10133 2RSemi-automatic 3D lung nodule segmentation in CT using dynamic programming 10133 0P White matter fiber-based analysis of T1w/T2w ratio map [10133-25] SESSION 6 QUANTITATIVE  ...  fitting [10133-89] 10133 2G A multi-object statistical atlas adaptive for deformable registration errors in anomalous medical image segmentation [10133-90] 10133 2H Automatic MR prostate segmentation  ...  SESSION 4 SEGMENTATION: BRAIN Session Chairs  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.2270368">doi:10.1117/12.2270368</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/miip/X17.html">dblp:conf/miip/X17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/resfpzholvbtbalfkbg3pj64gu">fatcat:resfpzholvbtbalfkbg3pj64gu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180720190018/https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10133/1013301/Front-Matter-Volume-10133/10.1117/12.2270368.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/75/72/75724d176267865b54af4e9818ea0e18918f700b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.2270368"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Hippocampus Segmentation for Preterm and Aging Brains Using 3D Densely Connected Fully Convolutional Networks

Debin Zeng, Qiongling Li, Baoqiang Ma, Shuyu 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;
Efficient and accurate segmentation of hippocampi from preterm and aging brain MR images is one of the most fundamental steps in understanding hippocampal growth and development or diagnosing and monitoring  ...  To deal with these problems, we propose an efficient, open-source algorithm, 3D densely connected fully convolutional network (3D-DCFCN) for the infant and aging hippocampal segmentation.  ...  for preterm and aging brain from 3D MR images.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2993504">doi:10.1109/access.2020.2993504</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qatel7buajazjfon6p3phfpmmy">fatcat:qatel7buajazjfon6p3phfpmmy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108195951/https://ieeexplore.ieee.org/ielx7/6287639/8948470/09090157.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/e8/87/e8871bac493ccfa707e101aebe7f247f83cffc10.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2993504"> <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>

Diagnosis of Alzheimer's Disease via Multi-modality 3D Convolutional Neural Network [article]

Yechong Huang, Jiahang Xu, Yuncheng Zhou, Tong Tong, Xiahai Zhuang, the Alzheimer's Disease Neuroimaging Initiative
<span title="2019-02-26">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Keywords: Alzheimer's Disease, Multi-modality, Image Classification, CNN, Deep Learning, Hippocampal  ...  In this paper, we propose a novel convolutional neural network (CNN) to fuse the multi-modality information including T1-MRI and FDG-PDT images around the hippocampal area for the diagnosis of AD.  ...  Both studies used random forest to implement the classification because random forest has been demonstrated as a feasible and reliable machine learning method for classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1902.09904v1">arXiv:1902.09904v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pzzhu2mdfzhgbldhqwtofaz3sq">fatcat:pzzhu2mdfzhgbldhqwtofaz3sq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200910155832/https://arxiv.org/ftp/arxiv/papers/1902/1902.09904.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/53/27/532776ccae1392a804a622e34d12c4daae27b443.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1902.09904v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,125 Individuals from the ADNI and OASIS Databases [article]

Jianfeng Wu, Qunxi Dong, Jie Gui, Jie Zhang, Yi Su, Kewei Chen, Paul M Thompson, Richard J Caselli, Eric M. Reiman, Jieping Ye, Yalin Wang
<span title="2020-10-17">2020</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Then we apply these individual representations and a binary random forest classifier to predict brain Aβ positivity for each person.  ...  Our prior studies show that MRI-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD.  ...  In panel (1) , hippocampal structures are segmented from registered brain MR images with FIRST from the FMRIB Software Library (FSL) (Paquette et al., 2017; Patenaude et al., 2011) .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.10.16.343137">doi:10.1101/2020.10.16.343137</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jjupkgjtnbadfgpxjvflv7acfi">fatcat:jjupkgjtnbadfgpxjvflv7acfi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201210020637/https://www.biorxiv.org/content/biorxiv/early/2020/10/17/2020.10.16.343137.1.full.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/71/55/7155ca679711290d39edd533a8c3b8d5c5a8eb6d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.10.16.343137"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Feature Selection Based on Machine Learning in MRIs for Hippocampal Segmentation

Sabina Tangaro, Nicola Amoroso, Massimo Brescia, Stefano Cavuoti, Andrea Chincarini, Rosangela Errico, Paolo Inglese, Giuseppe Longo, Rosalia Maglietta, Andrea Tateo, Giuseppe Riccio, Roberto Bellotti
<span title="">2015</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;
, respectively, (ii) sequential forward selection and (iii) sequential backward elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested  ...  Magnetic resonance imaging (MRI) scans can show these variations and therefore can be used as a supportive feature for a number of neurodegenerative diseases.  ...  Acknowledgments The authors would like to thank the anonymous referee for extremely valuable comments and suggestions. Nicola Amoroso, Rosangela Errico, Paolo Inglese, and Andrea Tateo  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2015/814104">doi:10.1155/2015/814104</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26089977">pmid:26089977</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4450305/">pmcid:PMC4450305</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lxoqyreapvehrgi4vwvy2g4aqy">fatcat:lxoqyreapvehrgi4vwvy2g4aqy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190501143704/http://downloads.hindawi.com/journals/cmmm/2015/814104.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/4d/8a/4d8a82557b30e30ed0546adb685e335db55f77cf.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2015/814104"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4450305" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation

Hancan Zhu, Zhenyu Tang, Hewei Cheng, Yihong Wu, Yong Fan
<span title="2019-11-14">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tnqhc2x2aneavcd3gx5h7mswhm" style="color: black;">Scientific Reports</a> </i> &nbsp;
Automatic and reliable segmentation of the hippocampus from magnetic resonance (MR) brain images is extremely important in a variety of neuroimage studies.  ...  The registered atlases are then used as training data to build linear regression models for segmenting the images based on the image features, referred to as random local binary pattern (RLBP), extracted  ...  Acknowledgements This work was supported in part by the National Key Basic Research and Development Program (No.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41598-019-53387-9">doi:10.1038/s41598-019-53387-9</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31727982">pmid:31727982</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6856174/">pmcid:PMC6856174</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/natiwdxzqng2pdtsfus52qi35y">fatcat:natiwdxzqng2pdtsfus52qi35y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191116025641/https://www.nature.com/articles/s41598-019-53387-9.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/64/9c/649c364f76712b3173e294f13d41c5880be35968.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41598-019-53387-9"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6856174" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation

Qiang Zheng, Yihong Wu, Yong Fan
<span title="2018-10-10">2018</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/w7oms7cvvjfs7ht4yodsyhuzku" style="color: black;">Frontiers in Neuroinformatics</a> </i> &nbsp;
We build random forests classification models for each image voxel to be segmented based on its corresponding image patches of atlas images that have been registered to the image to be segmented.  ...  The voxelwise random forests classification models are then applied to the image to be segmented to obtain a probabilistic segmentation map.  ...  An example image slice, its probabilistic segmentation map obtained by the random forest classification model, and its balanced label information are shown in Figure 1 .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fninf.2018.00069">doi:10.3389/fninf.2018.00069</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30364123">pmid:30364123</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6191508/">pmcid:PMC6191508</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/35euewmvfvdyldxjhgij76aeze">fatcat:35euewmvfvdyldxjhgij76aeze</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200210050034/https://fjfsdata01prod.blob.core.windows.net/articles/files/401287/pubmed-zip/.versions/1/.package-entries/fninf-12-00069/fninf-12-00069.pdf?sv=2015-12-11&amp;sr=b&amp;sig=2DzBx%2BNVIVSAEf%2F2JT%2BqprjOD7nBjOijVjOu5D5HKjM%3D&amp;se=2020-02-10T05%3A01%3A03Z&amp;sp=r&amp;rscd=attachment%3B%20filename%2A%3DUTF-8%27%27fninf-12-00069.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/34/23/3423a761fca8be81ff85da00d95f8f4005286fbc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fninf.2018.00069"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> frontiersin.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6191508" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Measurement of hippocampal atrophy using 4D graph-cut segmentation: Application to ADNI

Robin Wolz, Rolf A. Heckemann, Paul Aljabar, Joseph V. Hajnal, Alexander Hammers, Jyrki Lötjönen, Daniel Rueckert
<span title="">2010</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sa477uo7lveh7hchpikpixop5u" style="color: black;">NeuroImage</a> </i> &nbsp;
We propose a new method of measuring atrophy of brain structures by simultaneously segmenting longitudinal magnetic resonance (MR) images.  ...  Solving the min-cut/max-flow problem on this graph yields the segmentation for all timepoints in a single step.  ...  In this work we extend this algorithm to the simultaneous segmentation of a series of MR images acquired from the same subject.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2010.04.006">doi:10.1016/j.neuroimage.2010.04.006</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/20382238">pmid:20382238</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dewk65nukrewbnsrdr45jf4o4i">fatcat:dewk65nukrewbnsrdr45jf4o4i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20120802181757/http://www.doc.ic.ac.uk/~pa100/pubs/wolzNeuroImage2010b.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/80/52/80520e973f7f222f8ec0c0906de67ac96e38e925.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2010.04.006"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

Dilated Dense U-Net for Infant Hippocampus Subfield Segmentation

Hancan Zhu, Feng Shi, Li Wang, Sheng-Che Hung, Meng-Hsiang Chen, Shuai Wang, Weili Lin, Dinggang Shen
<span title="2019-04-24">2019</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/w7oms7cvvjfs7ht4yodsyhuzku" style="color: black;">Frontiers in Neuroinformatics</a> </i> &nbsp;
Accurate and automatic segmentation of infant hippocampal subfields from magnetic resonance (MR) images is an important step for studying memory related infant neurological diseases.  ...  In this paper, we propose a new fully convolutional network (FCN) for infant hippocampal subfield segmentation by embedding the dilated dense network in the U-net, namely DUnet.  ...  These extracted features were then fed into a random forest classifier for voxel-wise classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fninf.2019.00030">doi:10.3389/fninf.2019.00030</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31068797">pmid:31068797</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6491864/">pmcid:PMC6491864</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e7cnqfplbjhj3itzbkcwii7fvy">fatcat:e7cnqfplbjhj3itzbkcwii7fvy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200214180651/https://fjfsdata01prod.blob.core.windows.net/articles/files/453140/pubmed-zip/.versions/1/.package-entries/fninf-13-00030/fninf-13-00030.pdf?sv=2015-12-11&amp;sr=b&amp;sig=7QV1tXyebiZfZYIAjO8oRB5YdLug58jN6pOIJ3M%2B87U%3D&amp;se=2020-02-14T18%3A07%3A16Z&amp;sp=r&amp;rscd=attachment%3B%20filename%2A%3DUTF-8%27%27fninf-13-00030.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/3d/c6/3dc6fcf98b269d4c5d50ad5e658aef12c81977da.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fninf.2019.00030"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> frontiersin.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6491864" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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