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The Liver Tumor Segmentation Benchmark (LiTS)
[article]
2019
arXiv
pre-print
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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 ...
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International ...
constraints [99] , and fuzzy c-means clustering on manual selected slices with segmentation refinement using hidden markov measure field models [100] . ...
arXiv:1901.04056v1
fatcat:25ekt2znl5adnd5laap4ez6a4y
Artificial Intelligence in Quantitative Ultrasound Imaging: A Review
[article]
2020
arXiv
pre-print
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Therefore, it is in great need to develop automatic method to improve the imaging quality and aid in measurements in QUS. ...
Despite its safety and efficacy, QUS suffers from several major drawbacks: poor imaging quality, inter- and intra-observer variability which hampers the reproducibility of measurements. ...
Hu et al. proposed to combine a dilated fully convolutional network (DFCN) with a phase-based active contour model for automatic tumor segmentation in BUS images [140] . ...
arXiv:2003.11658v1
fatcat:iujuh7gra5ax7od2gxoo6yrbpe
Metastatic liver tumour segmentation from discriminant Grassmannian manifolds
2015
Physics in Medicine and Biology
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2015 pre-print arXiv:1509.00083v1 fatcat:qdkiyq3c4ndurcqo5tmvbckzoy
The early detection, diagnosis and monitoring of liver cancer progression can be achieved with the precise delineation of metastatic tumours. ...
First, the framework learns within-class and between-class similarity distributions from a training set of images to discover the optimal manifold discrimination between normal and pathological tissue ...
Nadine Abi-Jaoudeh from the National Institutes of Health for providing liver CT images. Research funding was supported in part by the Canada Research ...
doi:10.1088/0031-9155/60/16/6459
pmid:26247117
fatcat:zjdhad2ssjeg7hmgjqjca5qp4q
Liver tumor segmentation in CT volumes using an adversarial densely connected network
2019
BMC Bioinformatics
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one patient to another, automatic liver tumor segmentation is still a difficult task. ...
However, because of the noise existed in the scan sequence and the similar pixel intensity of liver tumors with their surrounding tissues, besides, the size, position and shape of tumors also vary from ...
[1] proposed a unified level set method (LSM) for liver tumor segmentation. They used local pixel intensity clustering combined with hidden Markov random field to construct a unified LSM. ...
doi:10.1186/s12859-019-3069-x
pmid:31787071
pmcid:PMC6886252
fatcat:2pfk7enaerdpbo65vgrsbosutq
Evaluation of semiautomated quantification of cranial ultrasound images in newborns as a predictor of Neonatal Behavioral Assessment Scale
2011
2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
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IN HIDDEN MARKOV RANDOM FIELDS Gyorgy Kovacs, Andras Hajdu, University of Debrecen, Hungary TH-PS2b.6: A CONTENT-BASED 3D SHAPE RETRIEVAL SYSTEM FOR ABDOMINAL ...............697 AORTIC ANEURYSM RUPTURE ...
Alison Noble, University of Oxford, United Kingdom TH-PS2a.7: CONVEX SPATIO-TEMPORAL SEGMENTATION OF THE ENDOCARDIUM ...........626 IN ULTRASOUND DATA USING DISTRIBUTION AND SHAPE PRIORS Mattias Hansson ...
doi:10.1109/isbi.2011.5872350
dblp:conf/isbi/Bonet-CarneTFGA11
fatcat:hz26tx4dmzbbnefruo7eh5jrbm
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
2015
IEEE Transactions on Medical Imaging
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approach
Yes
Doyle
Hidden Markov fields and variational EM in a generative model
Yes
Festa
Random forest classifier using neighborhood and local context features
Yes
Guo
Semi-automatic segmentation ...
DOYLE, VASSEUR, DOJAT & FORBES (2013): FULLY AUTOMATIC BRAIN TUMOR SEGMENTATION FROM MULTIPLE MR SEQUENCES USING HIDDEN MARKOV FIELDS AND VARIATIONAL EM Algorithm and Data: We propose an adaptive scheme ...
doi:10.1109/tmi.2014.2377694
pmid:25494501
pmcid:PMC4833122
fatcat:csrnfqc4i5eilh7wk5howvpr4u
Deep Learning Initialized and Gradient Enhanced Level-set Based Segmentation for Liver Tumor from CT Images
2020
IEEE Access
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In this paper, we propose and validate a novel level-set method integrating an enhanced edge indicator and an automatically derived initial curve for CT based liver tumor segmentation. ...
Liver and liver tumor segmentation provides vital biomarkers for surgical planning and hepatic diagnosis. ...
Häme et al. presented a semi-automatic tumor segmentation scheme based on non-parametric intensity distribution estimation and a hidden Markov field model [21] . ...
doi:10.1109/access.2020.2988647
fatcat:n6hdm2mptje7bbx3aphlmuss2e
Quantification of ultrasonic texture intra-heterogeneity via volumetric stochastic modeling for tissue characterization
2015
Medical Image Analysis
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In the case of ultrasound, the Nakagami distribution is a general model of the ultrasonic backscattering envelope under various scattering conditions and densities where it can be employed for characterizing ...
Experimental results on real 3D ultrasonic pre-clinical and clinical datasets suggest that describing tumor intra-heterogeneity via this descriptor may facilitate improved prediction of therapy response ...
Acknowledgments This work was support by the Engineering and Physical Sciences Research Council and Wellcome Trust Grant WT 088877/Z/09/Z. ...
doi:10.1016/j.media.2014.12.004
pmid:25595523
pmcid:PMC4339203
fatcat:b7jhseyhardihpa3mz4q5mjrde
Ultrasound image segmentation: a survey
2006
IEEE Transactions on Medical Imaging
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First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. ...
This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical -mode ultrasound images. ...
ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their thoughtful suggestions on how best to present this review, and the authors (too many to name in person) who kindly provided ...
doi:10.1109/tmi.2006.877092
pmid:16894993
fatcat:wtt6igy2ozhjtbnsizi6qtosn4
Biomedical Image Segmentation: A Survey
2021
SN Computer Science
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Medical Image Segmentation is the process of segmenting and detecting boundaries of anatomical structures in various types of 2D and 3D-medical images. ...
Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ...
Markov Random Field (MRF) Models MRFs are undirected graphical models that have been used widely in image segmentation and image restoration, since they can preserve edges by parameter estimation. ...
doi:10.1007/s42979-021-00704-7
fatcat:ukiglrr5orfplcea7gy4jzqqca
Machine learning and radiology
2012
Medical Image Analysis
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We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease ...
Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports ...
For example, Zhang et al. utilized a hidden Markov random field model and the expectation-maximization algorithm to solve the segmentation problem of brain MR images . ...
doi:10.1016/j.media.2012.02.005
pmid:22465077
pmcid:PMC3372692
fatcat:4ynexgzdhrev7dfqapmjpxexuu
Interactive Volumetry Of Liver Ablation Zones
2015
Scientific Reports
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Within this study, the RF ablation zones from twelve post-interventional CT acquisitions have been segmented semi-automatically to support the visual inspection. ...
The results show that the proposed tool provides lesion segmentation with sufficient accuracy much faster than manual segmentation. ...
To achieve this, they introduced a semi-automatic liver tumor segmentation approach with a hidden Markov measure field model and a non-parametric distribution estimation. ...
doi:10.1038/srep15373
pmid:26482818
pmcid:PMC4612735
fatcat:63bihgy4gzderg3txj3ozwqpka
Kidney segmentation in renal magnetic resonance imaging – current status and prospects
2021
IEEE Access
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Parametric or non-parametric geometrical models are used in this category of segmentation techniques. ...
combinations of discrete Gaussians (LCDG) intensity estimation model, a second-order pair-wise Potts-Markov-Gibbs random field (MGRF) spatial interaction model, and a weighted probabilistic shape prior ...
Her research interest lie in the fields of image processing and deep learning. ...
doi:10.1109/access.2021.3078430
fatcat:lmzeqqaf4jcahgzk2p5t45qroi
Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies
2013
International Journal of Biomedical Imaging
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A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules ...
, and diagnosis of the nodules as benign or malignant. ...
[166, 167] used the Bhattacharya distance-based classification with a GGO intensity distribution modeled by the non-parametric KDE. Tao et al. ...
doi:10.1155/2013/942353
pmid:23431282
pmcid:PMC3570946
fatcat:4kcqhbezknh6tps6wv2zgl77xy
Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art
2022
Journal of Imaging
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Some difficulties with manual segmentation have necessitated the use of deep learning models to assist clinicians in effectively recognizing and segmenting tumors. ...
Simultaneously, researchers in the field of medical image segmentation employ DL approaches to solve problems such as tumor segmentation, cell segmentation, and organ segmentation. ...
Among the different bias field correction strategies are the non-parametric non-uniform normalization (N3) approach [105] . ...
doi:10.3390/jimaging8030055
pmid:35324610
pmcid:PMC8954467
fatcat:7dhh3zwk5zcmpe3ijzbgpmo4ze
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