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Simultaneous Segmentation of Multiple Organs Using Random Walks

Chunhua Dong, Yen-Wei Chen, Lanfen Lin, Hongjie Hu, Chongwu Jin, Huajun Yu, Xian-Hua Han, Tomoko Tateyama
<span title="">2016</span> <i title="Information Processing Society of Japan"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dpojic4iejf5ni3no6ddhgbqaq" style="color: black;">Journal of Information Processing</a> </i> &nbsp;
In this paper, a knowledge-based segmentation framework for multiple organs is proposed based on random walks.  ...  Random walks-based (RW) segmentation methods have been proven to have a potential application in segmenting the medical image with minimal interactive guidance.  ...  In this paper, we proposed a knowledge-based framework for multiple organs segmentation based on random walks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2197/ipsjjip.24.320">doi:10.2197/ipsjjip.24.320</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lk7nir5cmfcrfduhmgpnbf4anq">fatcat:lk7nir5cmfcrfduhmgpnbf4anq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20181102134352/https://www.jstage.jst.go.jp/article/ipsjjip/24/2/24_320/_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/ed/e8/ede802ba174f97f23ed6c054f2d7e36ba528c54f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2197/ipsjjip.24.320"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

An Improved Random Walker with Bayes Model for Volumetric Medical Image Segmentation

Chunhua Dong, Xiangyan Zeng, Lanfen Lin, Hongjie Hu, Xianhua Han, Masoud Naghedolfeizi, Dawit Aberra, Yen-Wei Chen
<span title="">2017</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cswd2rqrire6lgrsm56kv4adue" style="color: black;">Journal of Healthcare Engineering</a> </i> &nbsp;
Hence, we propose a prior knowledge-based Bayes random walk framework to segment the volumetric medical image in a slice-by-slice manner.  ...  Random walk (RW) method has been widely used to segment the organ in the volumetric medical image.  ...  Based on the extended random walker, we applied a knowledge-based segmentation framework for the volumetric medical image in a slice-byslice manner.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2017/6506049">doi:10.1155/2017/6506049</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29201332">pmid:29201332</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5672701/">pmcid:PMC5672701</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jy4kiowswvd4xog7v2j7ocir2q">fatcat:jy4kiowswvd4xog7v2j7ocir2q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190226044128/http://pdfs.semanticscholar.org/791d/f71f255b7bf239d3e574f06ac317384c59ac.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/79/1d/791df71f255b7bf239d3e574f06ac317384c59ac.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2017/6506049"> <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/PMC5672701" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Feature Learning Based Random Walk for Liver Segmentation

Yongchang Zheng, Danni Ai, Pan Zhang, Yefei Gao, Likun Xia, Shunda Du, Xinting Sang, Jian Yang, Zhaohong Deng
<span title="2016-11-15">2016</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
This paper proposes a feature-learning-based random walk method for liver segmentation using CT images.  ...  However, liver segmentation using computed tomography (CT) images remains a challenging task because of the low contrast between the liver and adjacent organs.  ...  In this paper, a feature-learning-based random walk method (FLRW) is presented for liver segmentation using CT images.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0164098">doi:10.1371/journal.pone.0164098</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/27846217">pmid:27846217</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5112808/">pmcid:PMC5112808</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xrtsdf3rgzfn7mpimw573tv5me">fatcat:xrtsdf3rgzfn7mpimw573tv5me</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171012024129/http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0164098&amp;type=printable" 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/00/7c/007c521194a7e22307d95d5d9d7f7091e3dbaf85.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0164098"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5112808" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Weakly supervised segmentation from extreme points [article]

Holger Roth, Ling Zhang, Dong Yang, Fausto Milletari, Ziyue Xu, Xiaosong Wang, Daguang Xu
<span title="2019-10-02">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We use extreme points in each dimension of a 3D medical image to constrain an initial segmentation based on the random walker algorithm.  ...  This segmentation is then used as a weak supervisory signal to train a fully convolutional network that can segment the organ of interest based on the provided user clicks.  ...  They used a patch-based classification CNN to segment brain and lung regions using an initial GrabCut segmentation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.01236v1">arXiv:1910.01236v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/joub4vndynfllbhkqma6fsdhim">fatcat:joub4vndynfllbhkqma6fsdhim</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200930043920/https://arxiv.org/pdf/1910.01236v1.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/bb/da/bbdaeaa0e40daa3b418e35052bf315adbfafd34a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.01236v1" 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>

Computer-aided evaluation of anatomical accuracy of image fusion between X-ray CT and SPECT

Jingfeng Han, Harald Köstler, Christian Bennewitz, Torsten Kuwert, Joachim Hornegger
<span title="">2008</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4jlnkdml2bdgrfvr7tr6ckaowi" style="color: black;">Computerized Medical Imaging and Graphics</a> </i> &nbsp;
A localized maximally stable extremal regions method is proposed to automatically segment SPECT hot spots, while the corresponding CT structures are segmented by the semi-automatic random walk method,  ...  based on a fast multigrid solver.  ...  Random walk segmentation ( 13 ) is chosen to detect CT structures in this application.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.compmedimag.2008.03.002">doi:10.1016/j.compmedimag.2008.03.002</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/18468862">pmid:18468862</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kztmsj4lynddhl6wtukwo4jrye">fatcat:kztmsj4lynddhl6wtukwo4jrye</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170921221309/http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2008/Han08-CEO.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/bf/4dbf684d73a99be92b346535dc5967d6ed8b4461.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.compmedimag.2008.03.002"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images

Wei Ju, Deihui Xiang, Bin Zhang, Lirong Wang, Ivica Kopriva, Xinjian Chen
<span title="">2015</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dhlhr4jqkbcmdbua2ca45o7kru" style="color: black;">IEEE Transactions on Image Processing</a> </i> &nbsp;
The experimental results indicate the proposed method is superior to the graph cut method solely using the PET or CT is more accurate compared with the random walk method, random walk co-segmentation method  ...  Index Terms-Image segmentation, interactive segmentation, graph cut, random walks, prior information, lung tumor, positron emission tomography (PET), computed tomography (CT). ).  ...  We acknowledge the First Affiliated Hospital of Soochow University who supports us the lung cancer dataset.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tip.2015.2488902">doi:10.1109/tip.2015.2488902</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26462198">pmid:26462198</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6cqnvi3ks5fpvfq4m7iseuxz6m">fatcat:6cqnvi3ks5fpvfq4m7iseuxz6m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170829175635/http://fulir.irb.hr/2327/1/Co-segmenation_IEEE_TIP_2015_v2.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/a7/36/a736c0c11546ea7e9312cd724f218dfc99074211.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tip.2015.2488902"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Going to Extremes: Weakly Supervised Medical Image Segmentation

Holger R. Roth, Dong Yang, Ziyue Xu, Xiaosong Wang, Daguang Xu
<span title="2021-06-02">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tjwucdga6zfftlebfsmbvxjiyy" style="color: black;">Machine Learning and Knowledge Extraction</a> </i> &nbsp;
An initial segmentation is generated based on the extreme points using the random walker algorithm.  ...  Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation.  ...  Data Availability Statement: A pre-print is available on arxiv https://arxiv.org/abs/2009.11988, accessed on 28 May 2021. Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/make3020026">doi:10.3390/make3020026</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vpy3rtl63rctjcv3sgtd7mn56u">fatcat:vpy3rtl63rctjcv3sgtd7mn56u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210612014456/https://res.mdpi.com/d_attachment/make/make-03-00026/article_deploy/make-03-00026.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/ea/3b/ea3b994394527fea8b40ad137bc0555b1ca57aa9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/make3020026"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Going to Extremes: Weakly Supervised Medical Image Segmentation [article]

Holger R Roth, Dong Yang, Ziyue Xu, Xiaosong Wang, Daguang Xu
<span title="2020-09-25">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation.  ...  An initial segmentation is generated based on the extreme points utilizing the random walker algorithm.  ...  Can et al. (2018) proposes to use scribbles with random walks (Grady, 2006) and FCN predictions to achieve semi-automated segmentation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2009.11988v1">arXiv:2009.11988v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/msm35eefarharcaldcmjhr3wzm">fatcat:msm35eefarharcaldcmjhr3wzm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201010201024/https://arxiv.org/pdf/2009.11988v1.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/23/71/2371e504d1a0fb0516137a2aafdeae0444014890.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2009.11988v1" 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>

Applications of Knowledge Graphs in Drug Discovery [article]

Charles Tapley Hoyt
<span title="2019-11-05">2019</span> <i title="figshare"> Figshare </i> &nbsp;
of key proteins in a disease and their subsequent targeting for modulation.  ...  I gave this presentation to the Computational Drug Discovery Group at the University of Leiden on 2019-11-05.Abstract:Drug discovery campaigns are often successful because of a coherence between the identification  ...  Generate γ random walks starting at each v ∈ V 2.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.6084/m9.figshare.10257650">doi:10.6084/m9.figshare.10257650</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vg6lfuzobrdsvc7uqzfupzgl2u">fatcat:vg6lfuzobrdsvc7uqzfupzgl2u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200212095656/https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/18520349/ApplicationsofKnowledgeGraphsinDrugDiscovery.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/48/4a/484a5802edfa21eb36b821ab24e9377b7d0ef843.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.6084/m9.figshare.10257650"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> figshare.com </button> </a>

Semi-Automatic Segmentation of Autosomal Dominant Polycystic Kidneys using Random Forests [article]

Kanishka Sharma, Loic Peter, Christian Rupprecht, Anna Caroli, Lichao Wang, Andrea Remuzzi, Maximilian Baust, Nassir Navab
<span title="2015-10-23">2015</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
As a consequence, fully automatic segmentation of such kidneys is very challenging. We present a segmentation method with minimal user interaction based on a random forest classifier.  ...  This paper presents a method for 3D segmentation of kidneys from patients with autosomal dominant polycystic kidney disease (ADPKD) and severe renal insufficiency, using computed tomography (CT) data.  ...  [9] proposed a semi-automatic approach based on 3D random walks for the segmentation of polycystic kidneys on T2 weighted fat saturated MR acquisitions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1510.06915v1">arXiv:1510.06915v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yk6urja6mzfyfd335iyd432omy">fatcat:yk6urja6mzfyfd335iyd432omy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200913163123/https://arxiv.org/pdf/1510.06915v1.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/1c/26/1c26768a5ff22d79f3138ef58569e8bd0e771d03.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1510.06915v1" 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>

Liver tumors segmentation from CTA images using voxels classification and affinity constraint propagation

Moti Freiman, Ofir Cooper, Dani Lischinski, Leo Joskowicz
<span title="2010-06-24">2010</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ohr6juw5xrcrzgnsati6eq3edq" style="color: black;">International Journal of Computer Assisted Radiology and Surgery</a> </i> &nbsp;
Objective We present a method and a validation study for the nearly automatic segmentation of liver tumors in CTA scans.  ...  The result is a continuous segmentation map that is thresholded to obtain a binary segmentation.  ...  [14] propose a random-walk-based 3D liver tumors segmentation based on a single user-defined seed.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11548-010-0497-5">doi:10.1007/s11548-010-0497-5</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/20574799">pmid:20574799</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ws4awcj3xfgabcawjs4zh36dae">fatcat:ws4awcj3xfgabcawjs4zh36dae</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20140901144039/http://www.cs.huji.ac.il/~caslab/material/papers/freiman/ijcars10.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/76/3e/763e9dc079276db78758c9b551a3e3316e46a334.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11548-010-0497-5"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

The Liver Tumor Segmentation Benchmark (LiTS) [article]

Patrick Bilic, Patrick Ferdinand Christ, Eugene Vorontsov, Grzegorz Chlebus, Hao Chen, Qi Dou, Chi-Wing Fu, Xiao Han, Pheng-Ann Heng, Jürgen Hesser, Samuel Kadoury, Tomasz Konopczyǹski (+44 others)
<span title="2019-01-13">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI).  ...  Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense  ...  Other feature based classification combine AdaBoost with random walk algorithms [50] . Multi-label liver and liver tumor classification methods are mentioned in 2.2.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.04056v1">arXiv:1901.04056v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/25ekt2znl5adnd5laap4ez6a4y">fatcat:25ekt2znl5adnd5laap4ez6a4y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200929070732/https://arxiv.org/pdf/1901.04056v1.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/06/55/0655dcaa39cf41a3609974840f91300d73b4aed1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.04056v1" 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>

Segmentation of medical images using adaptive region growing

Regina Pohle, Klaus D. Toennies, Milan Sonka, Kenneth M. Hanson
<span title="2001-07-03">2001</span> <i title="SPIE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ha25cznnjncxtjoykhsg6fz5ly" style="color: black;">Medical Imaging 2001: Image Processing</a> </i> &nbsp;
Interaction increases flexibility of segmentation but it leads to undesirable behavior of an algorithm if knowledge being requested is inappropriate.  ...  These locations are selected sequentially in a random walk starting at the seed point, and the homogeneity criterion is updated continuously.  ...  This description method is to be used for segmentation of 2D and 3D structures in CT and MR data. Our knowledge of the homogeneity model is based on knowledge about the image formation process.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.431013">doi:10.1117/12.431013</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/miip/PohleT01.html">dblp:conf/miip/PohleT01</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rj3zwsb62fhm7hdez2rl32fiqe">fatcat:rj3zwsb62fhm7hdez2rl32fiqe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20040904032555/http://wwwisg.cs.uni-magdeburg.de:80/bv/pub/pdf/mi_4322_153.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/c6/80/c680439186775b5a1a77d18dd55de83183895d5e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.431013"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Affinity-based constraint optimization for nearly-automatic vessel segmentation

O. Cooper, M. Freiman, L. Joskowicz, D. Lischinski, Benoit M. Dawant, David R. Haynor
<span title="2010-03-04">2010</span> <i title="SPIE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ha25cznnjncxtjoykhsg6fz5ly" style="color: black;">Medical Imaging 2010: Image Processing</a> </i> &nbsp;
The desired segmentation is modeled as a function that minimizes a quadratic affinity-based functional.  ...  The advantages of our method are that it requires fewer initialization seeds, is robust, and yields better results than existing graph-based interactive segmentation methods.  ...  Acknowledgments This research was funded in part by a grant from the Israeli Ministry of Trade and Industry, MAGNETON Grant 38652.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.841245">doi:10.1117/12.841245</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/miip/CooperFJL10.html">dblp:conf/miip/CooperFJL10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lizbbewv5bc7jdg27aqwqnf2hm">fatcat:lizbbewv5bc7jdg27aqwqnf2hm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20140901144241/http://www.cs.huji.ac.il/~caslab/material/papers/c096-affinity-spie10.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/c7/3b/c73bab50aa3d11ab191a53cc868add5c2d2ac2f5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.841245"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A New Approach for Model-Based Adaptive Region Growing in Medical Image Analysis [chapter]

Regina Pohle, Klaus D. Toennies
<span title="">2001</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Interaction increases flexibility of segmentation but it leads to undesirable behaviour of an algorithm if knowledge being requested is inappropriate.  ...  It produces results that are only little sensitive to the seed point location and it allows a segmentation of individual structures.  ...  Conclusions We developed a new process of automatically finding the homogeneity criterion in region growing based on a simple model of region homogeneity.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/3-540-44692-3_30">doi:10.1007/3-540-44692-3_30</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/z3ght7nm6vhirjbwp5z3v4wlhq">fatcat:z3ght7nm6vhirjbwp5z3v4wlhq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808140224/http://wwwisg.cs.uni-magdeburg.de/bv/pub/pdf/caip2001.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/fd/95/fd9552dd28ffc5500ef200a8653cdadd62807205.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/3-540-44692-3_30"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>
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