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Conference on Medical Imaging - Image Processing

作者： Ding, Xiaowei Terzopoulos, Demetri Diaz-Zamudio, Mariana Berman, Daniel S. Slomka, Piotr J. Dey, Damini Cedars Sinai Med Ctr Dept Biomed Sci Biomed Imaging Res Inst Los Angeles CA 90048 USA Univ Calif Los Angeles Henry Samueil Sch Engn & Appl Dept Comp Sci Los Angeles CA 90024 USA Cedars Sinai Med Ctr Dept Imaging & Med Los Angeles CA 90048 USA Cedars Sinai Med Ctr Dept Nucl Med Los Angeles CA 90048 USA Cedars Sinai Med Ctr David Geffen Sch Med Dept Med Los Angeles CA 90048 USA

ISBN: (纸本)9780819498274

Epicardial fat volume (EFV) is now regarded as a significant imaging biomarker for cardiovascular risk stratification. Manual or semi-automated quantification of EFV includes tedious and careful contour drawing of pericardium on fine image features. We aimed to develop and validate a fully-automated, accurate algorithm for EVF quantification from non-contrast CT using active contours and multiple atlases registration. This is a knowledge-based model that can segment both the heart and pericardium accurately by initializing the location and shape of the heart in large scale from multiple co-registered atlases and locking itself onto the pericardium actively. The deformation process is driven by pericardium detection, extracting only the white contours representing the pericardium in the CT images. Following this step, we can calculate fat volume within this region (epicardial fat) using standard fat attenuation range. We validate our algorithm on CT datasets from 15 patients who underwent routine assessment of coronary calcium. Epicardial fat volume quantified by the algorithm (69.15 +/- 8.25 cm(3)) and the expert (69.46 +/- 8.80 cm(3)) showed excellent correlation (r = 0.96, p < 0.0001) with no significant differences by comparison of individual data points (p = 0.9). The algorithm achieved a Dice overlap of 0.93 (range 0.88 - 0.95). The total time was less than 60 sec on a standard windows computer. Our results show that fast accurate automated knowledge-based quantification of epicardial fat volume from non-contrast CT is feasible. To our knowledge, this is also the first fully automated algorithms reported for this task.

关键词： epicardial fat atlas-based segmentation registration active contours line detection non-contrast CT

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Optimal MAP Parameters Estimation in STAPLE - Learning from Performance Parameters versus Image Similarity Information

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5th International Workshop on Machine Learning in Medical Imaging (MLMI)

作者： Gorthi, Subrahmanyam Akhondi-Asl, Alireza Thiran, Jean-Philippe Warfield, Simon K. Childrens Hosp Computat Radiol Lab Boston MA 02115 USA Harvard Univ Sch Med Boston MA 02115 USA Ecole Polytech Fed Lausanne Signal Proc Lab LTS5 CH-1015 Lausanne Switzerland Univ Hosp Ctr CHUV Dept Radiol Lausanne Switzerland Univ Lausanne UNIL Lausanne Switzerland

ISBN: (纸本)9783319105819;9783319105802

In many medical imaging applications, merging segmentations obtained from multiple reference images (i.e., templates) has become a standard practice for improving the accuracy as well as reliability. Simultaneous Truth And Performance Level Estimation (STAPLE) is a widely used fusion algorithm that simultaneously estimates both performance parameters for each template, and the output segmentation;a more accurate estimation of performance parameters consequently results in more accurate output segmentations. In this paper, we propose a new approach for learning prior knowledge about the performance parameters of each template, and for incorporating it into the Maximum-a-Posteriori (MAP) formulation of the STAPLE, so that more accurate output segmentations can be obtained. More specifically, we propose a new approach to learn, for each structure to be segmented, the relationships between the performance parameters (viz. sensitivity and specificity) and the intensity similarities;we also propose a methodology for transferring this prior knowledge about the performance parameters into the STAPLE algorithm through optimal setting of the MAP parameters. The proposed approach is evaluated for the segmentation of structures in the brain MR images. These experiments have clearly demonstrated the advantages of incorporating such prior knowledge.

关键词： Medical Imaging segmentation atlas-based segmentation Label Fusion STAPLE MAP Formulation MRI Brain Lateral Ventricles

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Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours

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MEDICAL PHYSICS 2014年第5期41卷 051910页

作者： Fritscher, Karl D. Peroni, Marta Zaffino, Paolo Spadea, Maria Francesca Schubert, Rainer Sharp, Gregory Massachusetts Gen Hosp Dept Radiat Oncol Boston MA 02114 USA Paul Scherrer Inst CH-5232 Villigen Switzerland Magna Graecia Univ Catanzaro Dept Expt & Clin Med I-88100 Catanzaro Italy Private Univ Hlth Sci Med Informat & Technol Inst Biomed Image Anal A-6060 Hall In Tirol Austria Massachusetts Gen Hosp Dept Radiat Oncol Boston MA 02114 USA

Purpose: Accurate delineation of organs at risk (OARs) is a precondition for intensity modulated radiation therapy. However, manual delineation of OARs is time consuming and prone to high inter-observer variability. Because of image artifacts and low image contrast between different structures, however, the number of available approaches for autosegmentation of structures in the head-neck area is still rather low. In this project, a new approach for automated segmentation of head-neck CT images that combine the robustness of multiatlas-based segmentation with the flexibility of geodesic active contours and the prior knowledge provided by statistical appearance models is presented. Methods: The presented approach is using an atlas-based segmentation approach in combination with label fusion in order to initialize a segmentation pipeline that is based on using statistical appearance models and geodesic active contours. An anatomically correct approximation of the segmentation result provided by atlas-based segmentation acts as a starting point for an iterative refinement of this approximation. The final segmentation result is based on using model to image registration and geodesic active contours, which are mutually influencing each other. Results: 18 CT images in combination with manually segmented labels of parotid glands and brainstem were used in a leave-one-out cross validation scheme in order to evaluate the presented approach. For this purpose, 50 different statistical appearance models have been created and used for segmentation. Dice coefficient (DC), mean absolute distance and max. Hausdorff distance between the autosegmentation results and expert segmentations were calculated. An average Dice coefficient of DC = 0.81 (right parotid gland), DC = 0.84 (left parotid gland), and DC = 0.86 (brainstem) could be achieved. Conclusions: The presented framework provides accurate segmentation results for three important structures in the head neck area. Compared to a

关键词： radiation therapy planning atlas-based segmentation geodesic active contours InShape models

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Development and evaluation of an articulated registration algorithm for human skeleton registration

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PHYSICS IN MEDICINE AND BIOLOGY 2014年第6期59卷 1485-1499页

作者： Yip, Stephen Perk, Timothy Jeraj, Robert Univ Wisconsin Dept Phys Madison WI 53706 USA Univ Wisconsin Dept Med Phys Madison WI 53706 USA

Accurate registration over multiple scans is necessary to assess treatment response of bone diseases (e. g. metastatic bone lesions). This study aimed to develop and evaluate an articulated registration algorithm for the whole-body skeleton registration in human patients. In articulated registration, whole-body skeletons are registered by auto-segmenting into individual bones using atlas-based segmentation, and then rigidly aligning them. Sixteen patients (weight = 80-117 kg, height = 168-191 cm) with advanced prostate cancer underwent the pre- and mid-treatment PET/CT scans over a course of cancer therapy. Skeletons were extracted from the CT images by thresholding (HU>150). Skeletons were registered using the articulated, rigid, and deformable registration algorithms to account for position and postural variability between scans. The inter-observers agreement in the atlas creation, the agreement between the manually and atlas-based segmented bones, and the registration performances of all three registration algorithms were all assessed using the Dice similarity index-DSIobserved, DSIatlas, and DSIregister. Hausdorff distance (d(Hausdorff)) of the registered skeletons was also used for registration evaluation. Nearly negligible inter-observers variability was found in the bone atlases creation as the DSIobserver was 96 +/- 2%. atlas-based and manual segmented bones were in excellent agreement with DSIatlas of 90 +/- 3%. Articulated (DSIregsiter = 75 +/- 2%, d(Hausdorff) = 0.37 +/- 0.08 cm) and deformable registration algorithms (DSIregister = 77 +/- 3%, d(Hausdorff) = 0.34 +/- 0.08 cm) considerably outperformed the rigid registration algorithm (DSIregsiter = 59 +/- 9%, d(Hausdorff) = 0.69 +/- 0.20 cm) in the skeleton registration as the rigid registration algorithm failed to capture the skeleton flexibility in the joints. Despite superior skeleton registration performance, deformable registration algorithm failed to preserve the local rigidity of bones as over 60%

关键词： skeleton rigid registration articulated registration atlas-based segmentation bone-by-bone registration

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Probabilistic atlas and geometric variability estimation to drive tissue segmentation

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STATISTICS IN MEDICINE 2014年第20期33卷 3576-3599页

作者： Xu, Hao Thirion, Bertrand Allassonniere, Stephanie Ecole Polytech CMAP F-91128 Palaiseau France INRIA Saclay Ile De France Parietal Team Palaiseau France CEA DSV I2BM F-91191 Gif Sur Yvette France

Computerized anatomical atlases play an important role in medical image analysis. While an atlas usually refers to a standard or mean image also called template, which presumably represents well a given population, it is not enough to characterize the observed population in detail. A template image should be learned jointly with the geometric variability of the shapes represented in the observations. These two quantities will in the sequel form the atlas of the corresponding population. The geometric variability is modeled as deformations of the template image so that it fits the observations. In this paper, we provide a detailed analysis of a new generative statistical model based on dense deformable templates that represents several tissue types observed in medical images. Our atlas contains both an estimation of probability maps of each tissue (called class) and the deformation metric. We use a stochastic algorithm for the estimation of the probabilistic atlas given a dataset. This atlas is then used for atlas-based segmentation method to segment the new images. Experiments are shown on brain T1 MRI datasets. Copyright (C) 2014 John Wiley & Sons, Ltd.

关键词： probabilistic atlas geometric variability neuro-segmentation coupled with registration atlas-based segmentation stochastic algorithm statistical estimation

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Integrated segmentation of brain tumor images for radiotherapy and neurosurgery

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2013年第1期23卷 59-63页

作者： Bauer, Stefan Lu, Huanxiang May, Christian P. Nolte, Lutz-P. Buechler, Philippe Reyes, Mauricio Univ Bern Inst Surg Technol & Biomech CH-3012 Bern Switzerland RMS Fdn Bettlach Switzerland

segmentation of brain tumor images is an important task in diagnosis and treatment planning for cancer patients. To achieve this goal with standard clinical acquisition protocols, conventionally, either classification algorithms are applied on multimodal MR images or atlas-based segmentation is used on a high-resolution monomodal MR image. These two approaches have been commonly regarded separately. We propose to integrate all the available imaging information into one framework to be able to use the information gained from the tissue classification of the multimodal images to perform a more precise segmentation on the high-resolution monomodal image by atlas-based segmentation. For this, we combine a state of the art regularized classification method with an enhanced version of an atlas-registration approach including multiscale tumor-growth modeling. This contribution offers the possibility to simultaneously segment subcortical structures in the patient by warping the respective atlas labels, which is important for neurosurgical planning and radiotherapy planning. (c) 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 5963, 2013

关键词： brain tumor glioma atlas-based segmentation tumor-growth modeling multimodal image analysis

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atlas-based segmentation of deep brain structures using non-rigid registration

Atlas-based segmentation of deep brain structures using non-...

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Medical Imaging 2008 Conference

作者： Khan, Muhammad Faisal Mewes, Klaus Gross, Robert E. Skrinjar, Oskar Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Emory Univ Sch Med Dept Neurol Atlanta GA 30322 USA Emory Univ Sch Med Dept Neurol Surg Atlanta GA 30322 USA Georgia Inst Technol Dept Biomed Engn Atlanta GA 30332 USA

ISBN: (纸本)9780819471024

Deep brain structures are frequently used as targets in neurosurgical procedures. However, the boundaries of these structures are often not visible in clinically used MR and CT images. Techniques based on anatomical atlases and indirect targeting are used to infer the location of these targets intraoperatively. Initial errors of such approaches may be up to a few millimeters, which is not negligible. E.g. subthalamic nucleus is approximately 4x6 mm in the axial plane and the diameter of globus pallidus internus is approximately 8 mm, both of which are used as targets in deep brain stimulation surgery. To increase the initial localization accuracy of deep brain structures we have developed an atlas-based segmentation method that can be used for the surgery planning. The atlas is a high resolution MR head scan of a healthy volunteer with nine deep brain structures manually segmented. The quality of the atlas image allowed for the segmentation of the deep, brain structures, which is not possible from the clinical MR head scans of patients. The subject image is non-rigidly registered to the atlas image using thin plate splines to represent the transformation and normalized mutual information as a similarity measure. The obtained transformation is used to map the segmented structures from the atlas to the subject image. We tested the approach on five subjects. The quality of the atlas-based segmentation was evaluated by visual inspection of the third and lateral ventricles, putamena, and caudate nuclei, which are visible in the subject MR images. The agreement of these structures for the five tested subjects was approximately 1 to 2 mm.

关键词： image registration non-rigid registration atlas-based segmentation deep brain structures deep brain stimulation surgery DBS

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How to identify rectal sub-regions likely involved in rectal bleeding in prostate cancer radiotherapy?

How to identify rectal sub-regions likely involved in rectal...

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9th International Seminar on Medical Information Processing and Analysis

作者： Drean, G. Acosta, O. Ospina, J. D. Voisin, C. Rigaud, B. Simon, A. Haigron, P. de Crevoisier, R. INSERM U1099 Rennes France

ISBN: (纸本)9780819498090

Nowadays, the definition of patient-specific constraints in prostate cancer radiotherapy planning are solely based on dose-volume histogram (DVH) parameters. Nevertheless those DVH models lack of spatial accuracy since they do not use the complete 3D information of the dose distribution. The goal of the study was to propose an automatic work flow to de ne patient-specific rectal sub-regions (RSR) involved in rectal bleeding (RB) in case of prostate cancer radiotherapy. A multi-atlas database spanning the large rectal shape variability was built from a population of 116 individuals. Non-rigid registration followed by voxel-wise statistical analysis on those templates allowed finding RSR likely correlated with RB (from a learning cohort of 63 patients). To de ne patient-specific RSR, weighted atlas-based segmentation with a vote was then applied to 30 test patients. Results show the potentiality of the method to be used for patient-specific planning of intensity modulated radiotherapy (IMRT).

关键词： Prostate Cancer Radiotherapy Toxicity Rectal bleeding Predictive model Specific patient Inverse planning Registration atlas-based segmentation

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Learning-Boosted Label Fusion for Multi-atlas Auto-segmentation

Learning-Boosted Label Fusion for Multi-atlas Auto-Segmentat...

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4th International Workshop on Machine Learning in Medical Imaging (MLMI) in Conjunction with the 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Han, Xiao Elekta Inc St Louis MO 63146 USA

ISBN: (纸本)9783319022673;9783319022666

Structure segmentation of patient CT images is an essential step for radiotherapy planning but very tedious if done manually. atlas-based auto-segmentation (ABAS) methods have shown great promise for getting accurate segmentation results especially when multiple atlases are used. In this work, we aim to further improve the performance of ABAS by integrating it with learning-based segmentation techniques. In particular, the Random Forests (RF) supervised learning algorithm is applied to construct voxel-wise structure classifiers using both local and contextual image features. Training of the RF classifiers is specially tailored towards structure border regions where errors in ABAS segmentation typically occur. The trained classifiers are applied to re-estimate structure labels at "ambiguous" voxels where labels from different atlases do not fully agree. The classification result is combined with traditional label fusion to achieve improved accuracy. Experimental results on H&N images and ribcage segmentation show clear advantage of the proposed method, which offers consistent and significant improvements over the baseline method.

关键词： atlas-based segmentation machine learning label fusion random forests radiotherapy planning CT image

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Automatic neonatal brain tissue segmentation with MRI

Automatic neonatal brain tissue segmentation with MRI

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Conference on Medical Imaging - Image Processing

作者： Srhoj-Egekher, Vedran Benders, Manon J. N. L. Viergever, Max A. Isgum, Ivana Univ Zagreb Fac Elect Engn & Comp Zagreb 10000 Croatia

ISBN: (纸本)9780819494436

Volumetric measurements of neonatal brain tissue classes have been suggested as an indicator of long-term neurodevelopmental performance. To obtain these measurements, accurate brain tissue segmentation is needed. We propose a novel method for automatic segmentation of cortical grey matter (CoGM), unmyelinated white matter (UWM), myelinated white matter (MWM), basal ganglia and thalami, brainstem, cerebellum, ventricles, and cerebrospinal fluid in the extracerebral space (CSF) in MRI scans of the brain in preterm infants. For this project, seven preterm born infants, scanned at term equivalent age were used. Axial T1- and T2-weighted scans were acquired with 3T MRI scanner. The automatic segmentation was performed in three subsequent stages where each tissue was labeled. First, a multi-atlas-based segmentation (MAS) was employed to obtain localized, subject specific spatially varying priors for each tissue. Next, based on these priors, two-class classification with k-nearest neighbor (kNN) classifier was performed to obtain the segmentation of each tissue type separately. Last, to refine the final result, and to achieve the segmentation along the tissue boundaries, a multiclass naive Bayes classifier was employed. The results were evaluated against the manually set reference standard and quantified in terms of Dice coefficient (DC) and modified Hausdorff distance (MHD), defined as 95th-percentile of the Hausdorff distance. On average, the method achieved the following DCs: 0.87 for CoGM, 0.91 for UWM, 0.60 for MWM, 0.93 for basal ganglia and thalami, 0.87 for brainstem, 0.94 for cerebellum, 0.86 for ventricles, 0.82 for CSF. The obtained average MHDs were 0.48 mm, 0.44 mm, 3.09 mm, 0.39 mm, 0.62 mm, 0.35 mm, 1.75 mm, 1.13 mm, for each tissue, respectively. The proposed methods achieved high segmentation accuracy for all tissues, except for MWM, and it provides a tool for quantification of brain tissue volumes in axial MRI scans of preterm born infants.

关键词： Neonatal brain MRI Brain segmentation atlas-based segmentation Supervised classification

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