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A target-oriented segmentation method for specific tissues in MRI images of the brain

MULTIMEDIA TOOLS AND APPLICATIONS 2019年第7期78卷 9083-9099页

作者： Song, Enming Qian, Yuejing Liu, Hong Yan, Meng Song, Huimin Hung, Chih-Cheng Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China Zhejiang Ind & Trade Vocat Coll Wenzhou Zhejiang Peoples R China Cent City Brewers & Distillers Ltd Surrey BC Canada Kennesaw State Univ Ctr Machine Vis & Secur Res Maritetta GA USA

The multi-atlas based segmentation method can achieve the accurate segmentation of specific tissues of the human brain in the magnetic resonance imaging (MRI). The correct image registration and fusion scheme used in this method have an impact on the accuracy of segmentation. Similar to any traditional rigid registration method, we use the same method in our proposed target-oriented registration for the coarse registration between the target image and atlas image. However, to improve the registration accuracy in the area to be segmented, we propose a target-oriented image registration method for the refinement. We employ the distribution probability of the tissue (to be segmented) in the sparse patch-based label fusion process. Our aim is to determine if the proposed registration method can contribute the segmentation accuracy and which label fusion method is a good fit with this target-oriented registration. To evaluate the efficiency of our proposed method, we compare the performance of the majority voting method (MV), the nonlocal patch-based method (Nonlocal-PBM) and the sparse patch-based method (Sparse-PBM). Experimental results show that more accurate segmentation results can be obtained with the proposed registration method in this study. This result can provide more accurate clinical diagnosis information.

关键词： Target segmentation Registration Atlas patch-based segmentation Distribution probability Label fusion

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Neighbourhood approximation using randomized forests

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MEDICAL IMAGE ANALYSIS 2013年第7期17卷 790-804页

作者： Konukoglu, Ender Glocker, Ben Zikic, Dark Criminisi, Antonio Harvard Univ Massachusetts Gen Hosp Sch Med Athinoula A Martinos Ctr Biomed Imaging Boston MA 02129 USA Microsoft Res Cambridge CB1 2FB England

Leveraging available annotated data is an essential component of many modern methods for medical image analysis. In particular, approaches making use of the "neighbourhood" structure between images for this purpose have shown significant potential. Such techniques achieve high accuracy in analysing an image by propagating information from its immediate "neighbours" within an annotated database. Despite their success in certain applications, wide use of these methods is limited due to the challenging task of determining the neighbours for an out-of-sample image. This task is either computationally expensive due to large database sizes and costly distance evaluations, or infeasible due to distance definitions over semantic information, such as ground truth annotations, which is not available for out-of-sample images. This article introduces Neighbourhood Approximation Forests (NAFs), a supervised learning algorithm providing a general and efficient approach for the task of approximate nearest neighbour retrieval for arbitrary distances. Starting from an image training database and a user-defined distance between images, the algorithm learns to use appearance-based features to cluster images approximating the neighbourhood structured induced by the distance. NAF is able to efficiently infer nearest neighbours of an out-of-sample image, even when the original distance is based on semantic information. We perform experimental evaluation in two different scenarios: (i) age prediction from brain MRI and (ii) patch-based segmentation of unregistered, arbitrary field of view CT images. The results demonstrate the performance, computational benefits, and potential of NAF for different image analysis applications. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Approximate nearest neighbours Random decision forests patch-based segmentation Image-based regression Supervised neighbour search

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Multi-atlas segmentation with augmented features for cardiac MR images

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MEDICAL IMAGE ANALYSIS 2015年第1期19卷 98-109页

作者： Bai, Wenjia Shi, Wenzhe Ledig, Christian Rueckert, Daniel Univ London Imperial Coll Sci Technol & Med Dept Comp Biomed Image Anal Grp London SW7 2AZ England

Multi-atlas segmentation infers the target image segmentation by combining prior anatomical knowledge encoded in multiple atlases. It has been quite successfully applied to medical image segmentation in the recent years, resulting in highly accurate and robust segmentation for many anatomical structures. However, to guide the label fusion process, most existing multi-atlas segmentation methods only utilise the intensity information within a small patch during the label fusion process and may neglect other useful information such as gradient and contextual information (the appearance of surrounding regions). This paper proposes to combine the intensity, gradient and contextual information into an augmented feature vector and incorporate it into multi-atlas segmentation. Also, it explores the alternative to the K nearest neighbour (KNN) classifier in performing multi-atlas label fusion, by using the support vector machine (SVM) for label fusion instead. Experimental results on a short-axis cardiac MR data set of 83 subjects have demonstrated that the accuracy of multi-atlas segmentation can be significantly improved by using the augmented feature vector. The mean Dice metric of the proposed segmentation framework is 0.81 for the left ventricular myocardium on this data set, compared to 0.79 given by the conventional multi-atlas patch-based segmentation (Coupe et al., 2011;Rousseau et al., 2011). A major contribution of this paper is that it demonstrates that the performance of non-local patch-based segmentation can be improved by using augmented features. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Multi-atlas segmentation patch-based segmentation Cardiac image segmentation Augmented features

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A Fast Level-Set Curve-Flow Formulation by Logarithmic Step-Size Control for Brain Ultrasound Image segmentation

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IEEE ACCESS 2023年 11卷 131897-131914页

作者： Chel, Haradhan Bora, Prabin Kumar Ramchiary, Kandarpa Kumar Cent Inst Technol Kokrajhar Dept Elect & Commun Kokrajhar 783370 Assam India Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati 781039 Assam India City Clin & Res Ctr Dept Radiol Kokrajhar 783370 Assam India

segmentation of different regions in intra-operative brain ultrasound (iBUS) images is often required for assisting the neuro-surgeon. Traditional level-set and active contour-based semi-automatic image segmentation approaches suffer from low accuracy and slow convergence. This paper presents a novel semi-automatic level-set approach for segmenting hyper-echoic (HE), hypo-echoic, and anechoic regions with minimal user intervention. Three HE regions longitudinal fissure, choroid plexus, and tumor and two anechoic regions, namely ventricle and resection cavity are segmented using a patch-based level-set approach. This method is a combination of three procedures: a) unidirectional level-set curve flow (ULSCF), b) bidirectional level-set curve flow (BLSCF) using a logarithmic patch size control, and c) cubic B-spline-based contour smoothing. The zero level-set curve is derived using a patch-based intensity thresholding method of the desired region. The imperfection on the blocky edges produced during the patch-based ULSCF, are minimized using the BLSCF step that uses a local region splitting approach. Slope and curvature discontinuities of the resulting boundaries after BLSCF are eliminated using cubic B-spline based contour smoothing. The proposed method outperforms other state-of-the-art level-set and active contour methods, and the desired result is obtained within reasonable time required for online monitoring during surgery.

关键词： Ultrasound guided surgery ultrasound image segmentation patch-based segmentation boundary correction contour smoothing

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Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第4期56卷 2923-2969页

作者： Jyothi, Parvathy Singh, A. Robert Jyothi Engn Coll Dept Comp Sci & Engn Trichur Kerala India SRM Inst Sci & Technol Sch Comp Dept Computat Intelligence Chennai Tamil Nadu India

Brain is an amazing organ that controls all activities of a human. Any abnormality in the shape of anatomical regions of the brain needs to be detected as early as possible to reduce the mortality rate. It is also beneficial for treatment planning and therapy. The most crucial task is to isolate abnormal areas from normal tissue regions. To identify abnormalities in the earlier stage, various medical imaging modalities were used by medical practitioners as part of the diagnosis. Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool used for analyzing the internal structures owing to its capability to provide images with high resolution and better contrast for soft tissues. This survey focuses on studies done in brain MRI. Manual segmentation of abnormal tissues is a time-consuming task, and the performance depends on the expert's efficiency. Hence automating tumor segmentation plays a vital role in medical imaging applications. This study aims to provide a comprehensive survey on recent works developed in brain tumor segmentation. In this paper, a systematic literature review is presented to the reader to understand three policies, namely classical scheme, machine learning strategy, and deep learning methodology meant for tumor segmentation. Our primary goal is to include classical methods like atlas-based strategy and statistical-based models employed for segmenting tumors from brain MRI. Few studies that utilized machine learning approaches for the segmentation and classification of brain structures are also discussed. After that, the study provides an overview of deep learning-based segmentation models for quantitative analysis of brain MRI. Deep learning plays a vital role in the automatic segmentation of brain tissues. Presently deep learning technique outshines traditional statistical methods and machine learning approaches. An effort is made to enclose the literature on patch-based and semantic-based tissue segmentation presented by researchers w

关键词： Deep learning Machine learning Tissue segmentation Convolutional neural network patch-based segmentation Semantic segmentation Dice score coefficient

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segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling

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NEUROIMAGE 2013年第1期76卷 11-23页

作者： Tong, Tong Wolz, Robin Coupe, Pierrick Hajnal, Joseph V. Rueckert, Daniel Univ London Imperial Coll Sci Technol & Med Dept Comp Biomed Image Anal Grp London SW7 2AZ England CNRS UMR 5800 LaBRI F-33405 Talence France Kings Coll London St Thomas Hosp Ctr Dev Brain Div Imaging Sci & Biomed Engn London SE1 7EH England

We propose a novel method for the automatic segmentation of brain MRI images by using discriminative dictionary learning and sparse coding techniques. In the proposed method, dictionaries and classifiers are learned simultaneously from a set of brain atlases, which can then be used for the reconstruction and segmentation of an unseen target image. The proposed segmentation strategy is based on image reconstruction, which is in contrast to most existing atlas-based labeling approaches that rely on comparing image similarities between atlases and target images. In addition, we propose a Fixed Discriminative Dictionary Learning for segmentation (F-DDLS) strategy, which can learn dictionaries offline and perform segmentations online, enabling a significant speed-up in the segmentation stage. The proposed method has been evaluated for the hippocampus segmentation of 80 healthy ICBM subjects and 202 ADNI images. The robustness of the proposed method, especially of our F-DDLS strategy, was validated by training and testing on different subject groups in the ADNI database. The influence of different parameters was studied and the performance of the proposed method was also compared with that of the nonlocal patch-based approach. The proposed method achieved a median Dice coefficient of 0.879 on 202 ADNI images and 0.890 on 80 ICBM subjects, which is competitive compared with state-of-the-art methods. (c) 2013 Elsevier Inc. All rights reserved.

关键词： Structural MR images patch-based segmentation Discriminative dictionary learning Sparse representation

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A novel patch-based procedure for estimating brain age across adulthood

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NEUROIMAGE 2019年 197卷 618-624页

作者： Beheshti, Iman Gravel, Pierre Potvin, Olivier Dieumegarde, Louis Duchesne, Simon Ctr Rech CERVO F-35682601 Canardiere Quebec City PQ G1J 2G3 Canada Univ Laval Fac Med Dept Radiol & Med Nucl 1050Ave Med Quebec City PQ G1V 0A6 Canada

Aging is associated with structural alterations in many regions of the brain. Monitoring these changes contributes to increasing our understanding of the brain's morphological alterations across its lifespan, and could allow the identification of departures from canonical trajectories. Here, we introduce a novel and unique patch-based grading procedure for estimating a synthetic estimate of cortical aging in cognitively intact individuals. The cortical age metric is computed based on image similarity between an unknown (test) cortical label and known (training) cortical labels using machine learning algorithms. The proposed method was trained on a dataset of 100 cognitively intact individuals aged 19-61 years, within the 31 bilateral cortical labels of the Desikan-KillianyTourville parcellation, then tested on an independent test set of 78 cognitively intact individuals spanning a similar age range. The proposed patch-based framework yielded a R-2 = 0.94, as well as a mean absolute error of 1.66 years, which compared favorably to the literature. These experimental results demonstrate that the proposed patch-based grading framework is a reliable and robust method to estimate brain age from image data, even with a limited training size.

关键词： patch-based segmentation Grading Anatomical MRI Brain age

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NABS: non-local automatic brain hemisphere segmentation

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MAGNETIC RESONANCE IMAGING 2015年第4期33卷 474-484页

作者： Romero, Jose E. Manjon, Jose V. Tohka, Jussi Coupe, Pierrick Robles, Montserrat Univ Politecn Valencia Inst Aplicac Tecnol Informac & Comunicac Avanzada E-46022 Valencia Spain Univ Carlos III Madrid Dept Bioengn & Aerosp Engn E-28903 Getafe Spain CNRS PICTURA Res Grp Lab Bordelais Rech Informat Unite Mixte RechUMR 5800 F-33405 Talence France

In this paper, we propose an automatic method to segment the five main brain sub-regions (i.e. left/right hemispheres, left/right cerebellum and brainstem) from magnetic resonance images. The proposed method uses a library of pre-labeled brain images in a stereotactic space in combination with a non-local label fusion scheme for segmentation. The main novelty of the proposed method is the use of a multi-label block-wise label fusion strategy specifically designed to deal with the classification of main brain sub-volumes that process only specific parts of the brain images significantly reducing the computational burden. The proposed method has been quantitatively evaluated against manual segmentations. The evaluation showed that the proposed method was faster while producing more accurate segmentations than a current state-of-the-art method. We also present evidences suggesting that the proposed method was more robust against brain pathologies than the compared method. Finally, we demonstrate the clinical value of our method compared to the state-of-the-art approach in terms of the asymmetry quantification in Alzheimer's disease. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Brain segmentation Asymmetry Brain volume analysis patch-based segmentation MRI

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BEaST: Brain extraction based on nonlocal segmentation technique

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NEUROIMAGE 2012年第3期59卷 2362-2373页

作者： Eskildsen, Simon F. Coupe, Pierrick Fonov, Vladimir Manjon, Jose V. Leung, Kelvin K. Guizard, Nicolas Wassef, Shafik N. Ostergaard, Lasse Riis Collins, D. Louis McGill Univ Montreal Neurol Inst McConnell Brain Imaging Ctr Montreal PQ Canada Aalborg Univ Dept Hlth Sci & Technol Aalborg Denmark Univ Politecn Valencia Inst Aplicac Tecnol Informac & Comunicac Avanzada Valencia 46022 Spain UCL Inst Neurol DRC London WC1N 3BG England

Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases. In testing, a mean Dice similarity coefficient of 0.9834 +/- 0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781 +/- 0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Brain extraction Skull stripping patch-based segmentation Multi-resolution MRI BET

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Label Fusion based Brain MR Image segmentation via a Latent Selective Model 9

Label Fusion Based Brain MR Image Segmentation via a Latent ...

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9th International Conference on Graphic and Image Processing (ICGIP)

作者： Liu, Gang Guo, Xiantang Zhu, Kai Liao, Hengxu Shanghai Univ Elect Power Sch Automat Engn Shanghai 200090 Peoples R China State Grid Corp China Jinhua Power Supply Co Hangzhou 321000 Zhejiang Peoples R China

ISBN: (数字)9781510617421

ISBN: (纸本)9781510617421

Multi-atlas segmentation is an effective approach and increasingly popular for automatically labeling objects of interest in medical images. Recently, segmentation methods based on generative models and patch-based techniques have become the two principal branches of label fusion. However, these generative models and patch-based techniques are only loosely related, and the requirement for higher accuracy, faster segmentation, and robustness is always a great challenge. In this paper, we propose novel algorithm that combines the two branches using global weighted fusion strategy based on a patch latent selective model to perform segmentation of specific anatomical structures for human brain magnetic resonance (MR) images. In establishing this probabilistic model of label fusion between the target patch and patch dictionary, we explored the Kronecker delta function in the label prior, which is more suitable than other models, and designed a latent selective model as a membership prior to determine from which training patch the intensity and label of the target patch are generated at each spatial location. Because the image background is an equally important factor for segmentation, it is analyzed in label fusion procedure and we regard it as an isolated label to keep the same privilege between the background and the regions of interest. During label fusion with the global weighted fusion scheme, we use Bayesian inference and expectation maximization algorithm to estimate the labels of the target scan to produce the segmentation map. Experimental results indicate that the proposed algorithm is more accurate and robust than the other segmentation methods.

关键词： Multi-atlas segmentation latent selective model patch-based segmentation global weighted fusion image background analysis bayesian inference expectation maximization algorithm

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