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Automated segmentation of Breast in 3-D MR Images Using a Robust atlas

IEEE TRANSACTIONS ON MEDICAL IMAGING 2015年第1期34卷 116-125页

作者： Khalvati, Farzad Gallego-Ortiz, Cristina Balasingham, Sharmila Martel, Anne L. Sunnybrook Res Inst Dept Med Imaging Toronto ON M4N 3M5 Canada

This paper presents a robust atlas-based segmentation (ABS) algorithm for segmentation of the breast boundary in 3-D MR images. The proposed algorithm combines the well-known methodologies of ABS namely probabilistic atlas and atlas selection approaches into a single framework where two configurations are realized. The algorithm uses phase congruency maps to create an atlas which is robust to intensity variations. This allows an atlas derived from images acquired with one MR imaging sequence to be used to segment images acquired with a different MR imaging sequence and eliminates the need for intensity-based registration. Images acquired using a Dixon sequence were used to create an atlas which was used to segment both Dixon images (intra-sequence) and T1-weighted images (inter-sequence). In both cases, highly accurate results were achieved with the median Dice similarity coefficient values of 94% 4% and 87 6.5%, respectively.

关键词： atlas-based segmentation breast magnetic resonance imaging (MRI) image registration image segmentation

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Optimal MAP Parameters Estimation in STAPLE Using Local Intensity Similarity Information

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2015年第5期19卷 1589-1597页

作者： Gorthi, Subrahmanyam Akhondi-Asl, Alireza Warfield, Simon K. Harvard Univ Sch Med Boston Childrens Hosp Computat Radiol Lab Boston MA 02115 USA

In recent years, fusing segmentation results obtained based on multiple template images has become a standard practice in many medical imaging applications. Such multiple-templates based methods are found to provide more reliable and accurate segmentations than the single-template-based methods. In this paper, we present a new approach for learning prior knowledge about the performance parameters of template images using the local intensity similarity information;we also propose a methodology to incorporate that prior knowledge through the estimation of the optimal MAP parameters. The proposed method is evaluated in the context of segmentation of structures in the brain magnetic resonance images by comparing our results with some of the state-of- the-art segmentation methods. These experiments have clearly demonstrated the advantages of learning and incorporating prior knowledge about the performance parameters using the proposed method.

关键词： atlas-based segmentation brain label fusion maximum-a-posteriori (MAP) formulation medical imaging MRI segmentation Simultaneous Truth and Performance Level Estimation (STAPLE)

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Breast segmentation and Density Estimation in Breast MRI: A Fully Automatic Framework

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2015年第1期19卷 349-357页

作者： Gubern-Merida, Albert Kallenberg, Michiel Mann, Ritse M. Marti, Robert Karssemeijer, Nico Univ Girona Dept Comp Architecture & Technol Girona 17071 Spain Radboud Univ Nijmegen Med Ctr Dept Radiol NL-6525 GA Nijmegen Netherlands

Breast density measurement is an important aspect in breast cancer diagnosis as dense tissue has been related to the risk of breast cancer development. The purpose of this study is to develop a method to automatically compute breast density in breast MRI. The framework is a combination of image processing techniques to segment breast and fibroglandular tissue. Intra-and interpatient signal intensity variability is initially corrected. The breast is segmented by automatically detecting body-breast and air-breast surfaces. Subsequently, fibroglandular tissue is segmented in the breast area using expectation-maximization. A dataset of 50 cases with manual segmentations was used for evaluation. Dice similarity coefficient (DSC), total overlap, false negative fraction (FNF), and false positive fraction (FPF) are used to report similarity between automatic and manual segmentations. For breast segmentation, the proposed approach obtained DSC, total overlap, FNF, and FPF values of 0.94, 0.96, 0.04, and 0.07, respectively. For fibroglandular tissue segmentation, we obtained DSC, total overlap, FNF, and FPF values of 0.80, 0.85, 0.15, and 0.22, respectively. The method is relevant for researchers investigating breast density as a risk factor for breast cancer and all the described steps can be also applied in computer aided diagnosis systems.

关键词： atlas-based segmentation breast density segmentation breast MRI breast segmentation image processing quantitative image analysis

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Label Fusion in atlas-based segmentation Using a Selective and Iterative Method for Performance Level Estimation (SIMPLE)

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2010年第12期29卷 2000-2008页

作者： Langerak, Thomas Robin van der Heide, Uulke A. Kotte, Alexis N. T. J. Viergever, Max A. van Vulpen, Marco Pluim, Josien P. W. Univ Med Ctr Utrecht Image Sci Inst NL-3508 GA Utrecht Netherlands Univ Med Ctr Utrecht Dept Radiotherapy NL-3508 GA Utrecht Netherlands

In a multi-atlas based segmentation procedure, propagated atlas segmentations must be combined in a label fusion process. Some current methods deal with this problem by using atlas selection to construct an atlas set either prior to or after registration. Other methods estimate the performance of propagated segmentations and use this performance as a weight in the label fusion process. This paper proposes a selective and iterative method for performance level estimation (SIMPLE), which combines both strategies in an iterative procedure. In subsequent iterations the method refines both the estimated performance and the set of selected atlases. For a dataset of 100 MR images of prostate cancer patients, we show that the results of SIMPLE are significantly better than those of several existing methods, including the STAPLE method and variants of weighted majority voting.

关键词： atlas selection atlas-based segmentation classifier combination SIMPLE STAPLE

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Fully Automated atlas-based Hippocampal Volumetry for Detection of Alzheimer's Disease in a Memory Clinic Setting

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JOURNAL OF ALZHEIMERS DISEASE 2015年第1期44卷 183-193页

作者： Suppa, Per Anker, Ulrich Spies, Lothar Bopp, Irene Rueegger-Frey, Brigitte Klaghofer, Richard Gocke, Carola Hampel, Harald Beck, Sacha Buchert, Ralph Charite Dept Nucl Med Berlin Germany Stadtspital Waid Zurich Switzerland Jung Diagnost GmbH Hamburg Germany Med Prevent Ctr Hamburg Hamburg Germany Univ Paris 06 Paris France

Hippocampal volume is a promising biomarker to enhance the accuracy of the diagnosis of dementia due to Alzheimer's disease (AD). However, whereas hippocampal volume is well studied in patient samples from clinical trials, its value in clinical routine patient care is still rather unclear. The aim of the present study, therefore, was to evaluate fully automated atlas-based hippocampal volumetry for detection of AD in the setting of a secondary care expert memory clinic for outpatients. One-hundred consecutive patients with memory complaints were clinically evaluated and categorized into three diagnostic groups: AD, intermediate AD, and non-AD. A software tool based on open source software (Statistical Parametric Mapping SPM8) was employed for fully automated tissue segmentation and stereotactical normalization of high-resolution three-dimensional T1-weighted magnetic resonance images. Predefined standard masks were used for computation of grey matter volume of the left and right hippocampus which then was scaled to the patient's total grey matter volume. The right hippocampal volume provided an area under the receiver operating characteristic curve of 84% for detection of AD patients in the whole sample. This indicates that fully automated MR-based hippocampal volumetry fulfills the requirements for a relevant core feasible biomarker for detection of AD in everyday patient care in a secondary care memory clinic for outpatients. The software used in the present study has been made freely available as an SPM8 toolbox. It is robust and fast so that it is easily integrated into routine workflow.

关键词： Alzheimer's disease atlas-based segmentation hippocampal volumetry magnetic resonance imaging memory clinic memory impairment

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Multiatlas-based segmentation with preregistration atlas selection

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MEDICAL PHYSICS 2013年第9期40卷 091701-091701页

作者： Langerak, Thomas R. Berendsen, Floris F. Van der Heide, Uulke A. Kotte, Alexis N. T. J. Pluim, Josien P. W. Univ Med Ctr Utrecht Image Sci Inst NL-3508 GA Utrecht Netherlands Univ Med Ctr Utrecht Dept Radiotherapy NL-3508 GA Utrecht Netherlands

Purpose: Automatic, atlas-based segmentation of medical images benefits from using multiple atlases, mainly in terms of robustness. However, a large disadvantage of using multiple atlases is the large computation time that is involved in registering atlas images to the target image. This paper aims to reduce the computation load of multiatlas-based segmentation by heuristically selecting atlases before registration. Methods: To be able to select atlases, pairwise registrations are performed for all atlas combinations. based on the results of these registrations, atlases are clustered, such that each cluster contains atlas that registers well to each other. This can all be done in a preprocessing step. Then, the representatives of each cluster are registered to the target image. The quality of the result of this registration is estimated for each of the representatives and used to decide which clusters to fully register to the target image. Finally, the segmentations of the registered images are combined into a single segmentation in a label fusion procedure. Results: The authors perform multiatlas segmentation once with postregistration atlas selection and once with the proposed preregistration method, using a set of 182 segmented atlases of prostate cancer patients. The authors performed the full set of 182 leave-one-out experiments and in each experiment compared the result of the atlas-based segmentation procedure to the known segmentation of the atlas that was chosen as a target image. The results show that preregistration atlas selection is slightly less accurate than postregistration atlas selection, but this is not statistically significant. Conclusions: based on the results the authors conclude that the proposed method is able to reduce the number of atlases that have to be registered to the target image with 80% on average, without compromising segmentation accuracy. (C) 2013 American Association of Physicists in Medicine.

关键词： atlas-based segmentation atlas selection registration clustering

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Towards high-throughput mouse embryonic phenotyping: a novel approach to classifying ventricular septal defects

Towards high-throughput mouse embryonic phenotyping: a novel...

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Conference on Medical Imaging - Image Processing

作者： Liang, Xi Xie, Zhongliu Tamura, Masaru Shiroishi, Toshihiko Kitamoto, Asanobu Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan Univ Melbourne Melbourne Vic 3010 Australia Univ London Imperial Coll Sci Technol & Med London SW7 2AZ England Natl Inst Genet Mishima Shizuoka 4118540 Japan RIKEN BioResource Ctr Tsukuba Ibaraki 3050074 Japan

ISBN: (纸本)9781628415032

The goal of the International Mouse Phenotyping Consortium (IMPC, ***) is to study all the over 23,000 genes in the mouse by knocking them out one-by-one for comparative analysis. Large amounts of knockout mouse lines have been raised, leading to a strong demand for high-throughput phenotyping technologies. Traditional means via time-consuming histological examination is clearly unsuitable in this scenario. Biomedical imaging technologies such as CT and MRI therefore have started being used to develop more efficient phenotyping approaches. Existing work however primarily rests on volumetric analytics over anatomical structures to detect anomaly, yet this type of methods generally fail when features are subtle such as ventricular septal defects (VSD) in the heart, and meanwhile phenotypic assessment normally requires expert manual labor. This study proposes, to the best of our knowledge, the first automatic VSD diagnostic system for mouse embryos. Our algorithm starts with the creation of an atlas using wild-type mouse images, followed by registration of knockouts to the atlas to perform atlas-based segmentation on the heart and then ventricles, after which ventricle segmentation is further refined using a region growing technique. VSD classification is completed by checking the existence of an overlap between left and right ventricles. Our approach has been validated on a database of 14 mouse embryo images, and achieved an overall accuracy of 90.9%, with sensitivity of 66.7% and specificity of 100%.

关键词： mouse embryo phenotyping ventricular septal defects atlas-based segmentation region growing

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segmentation of organs at risk in CT volumes of head, thorax, abdomen, and pelvis

Segmentation of organs at risk in CT volumes of head, thorax...

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Conference on Medical Imaging - Image Processing

作者： Han, Miaofei Ma, Jinfeng Li, Yan Li, Meiling Song, Yanli Li, Qiang Shanghai United Imaging Healthcare Co Ltd Shanghai Peoples R China Chinese Acad Sci Shanghai Adv Res Inst Ctr Adv Med Imaging Technol Shanghai 201210 Peoples R China

ISBN: (纸本)9781628415032

Accurate segmentation of organs at risk (OARs) is a key step in treatment planning system (TPS) of image guided radiation therapy. We are developing three classes of methods to segment 17 organs at risk throughout the whole body, including brain, brain stem, eyes, mandible, temporomandibular joints, parotid glands, spinal cord, lungs, trachea, heart, livers, kidneys, spleen, prostate, rectum, femoral heads, and skin. The three classes of segmentation methods include (1) threshold-based methods for organs of large contrast with adjacent structures such as lungs, trachea, and skin;(2) context-driven Generalized Hough Transform-based methods combined with graph cut algorithm for robust localization and segmentation of liver, kidneys and spleen;and (3) atlas and registration-based methods for segmentation of heart and all organs in CT volumes of head and pelvis. The segmentation accuracy for the seventeen organs was subjectively evaluated by two medical experts in three levels of score: 0, poor (unusable in clinical practice);1, acceptable (minor revision needed);and 2, good (nearly no revision needed). A database was collected from Ruijin Hospital, Huashan Hospital, and Xuhui Central Hospital in Shanghai, China, including 127 head scans, 203 thoracic scans, 154 abdominal scans, and 73 pelvic scans. The percentages of "good" segmentation results were 97.6%, 92.9%, 81.1%, 87.4%, 85.0%, 78.7%, 94.1%, 91.1%, 81.3%, 86.7%, 82.5%, 86.4%, 79.9%, 72.6%, 68.5%, 93.2%, 96.9% for brain, brain stem, eyes, mandible, temporomandibular joints, parotid glands, spinal cord, lungs, trachea, heart, livers, kidneys, spleen, prostate, rectum, femoral heads, and skin, respectively. Various organs at risk can be reliably segmented from CT scans by use of the three classes of segmentation methods.

关键词： organ segmentation treatment planning system generalized Hough transform atlas-based segmentation

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atlas-based segmentation of 3D cerebral structures with competitive level sets and fuzzy control

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MEDICAL IMAGE ANALYSIS 2009年第3期13卷 456-470页

作者： Ciofolo, Cybele Barillot, Christian INRIA INSERM IRISA CNRSUnit Project VisAGeSU746 F-35042 Rennes France

We propose a novel approach for the simultaneous segmentation of multiple structures with competitive level sets driven by fuzzy control. To this end, several contours evolve simultaneously toward previously defined anatomical targets. A fuzzy decision system combines the a priori knowledge provided by an anatomical atlas with the intensity distribution of the image and the relative position of the contours. This combination automatically determines the directional term of the evolution equation of each level set. This leads to a local expansion or contraction of the contours, in order to match the boundaries of their respective targets. Two applications are presented: the segmentation of the brain hemispheres and the cerebellum, and the segmentation of deep internal structures. Experimental results on real magnetic resonance (MR) images are presented, quantitatively assessed and discussed. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Level sets Fuzzy control atlas-based segmentation Brain imaging Neurological diseases

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Weighted Shape-based Averaging With Neighborhood Prior Model for Multiple atlas Fusion-based Medical Image segmentation

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IEEE SIGNAL PROCESSING LETTERS 2013年第11期20卷 1036-1039页

作者： Gorthi, Subrahmanyam Cuadra, Meritxell Bach Tercier, Pierre-Alain Allal, Abdelkarim S. Thiran, Jean-Philippe Ecole Polytech Fed Lausanne Signal Proc Lab LTS5 Lausanne Switzerland CHU Vaudois Dept Radiol Lausanne Switzerland UNIL Lausanne Switzerland Ctr Biomed Imaging CIBM CHUV Unit Lausanne Switzerland Hop Fribourgeois Serv Radiooncol Fribourg Switzerland Univ Hosp Ctr CHUV Dept Radiol Lausanne Switzerland Univ Lausanne UNIL Lausanne Switzerland

In medical imaging, merging automated segmentations obtained from multiple atlases has become a standard practice for improving the accuracy. In this letter, we propose two new fusion methods: "Global Weighted Shape-based Averaging" (GWSBA) and "Local Weighted Shape-based Averaging" (LWSBA). These methods extend the well known Shape-based Averaging (SBA) by additionally incorporating the similarity information between the reference (i.e., atlas) images and the target image to be segmented. We also propose a new spatially-varying similarity-weighted neighborhood prior model, and an edge-preserving smoothness term that can be used with many of the existing fusion methods. We first present our new Markov Random Field (MRF) based fusion framework that models the above mentioned information. The proposed methods are evaluated in the context of segmentation of lymph nodes in the head and neck 3D CT images, and they resulted in more accurate segmentations compared to the existing SBA.

关键词： atlas-based segmentation label fusion medical imaging MRF SBA

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