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Efficient patient modeling for visuo-haptic VR simulation using a generic patient atlas

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016年 132卷 161-175页

作者： Mastmeyer, Andre Fortmeier, Dirk Handels, Heinz Med Univ Lubeck Inst Med Informat D-23538 Lubeck Germany Med Univ Lubeck Grad Sch Comp Med & Life Sci D-23538 Lubeck Germany

Background and Objective: This work presents a new time-saving virtual patient modeling system by way of example for an existing visuo-haptic training and planning virtual reality (VR) system for percutaneous transhepatic cholangio-drainage (PTCD). Methods: Our modeling process is based on a generic patient atlas to start with. It is defined by organ-specific optimized models, method modules and parameters, i.e. mainly individual segmentation masks, transfer functions to fill the gaps between the masks and intensity image data. In this contribution, we show how generic patient atlases can be generalized to new patient data. The methodology consists of patient-specific, locally-adaptive transfer functions and dedicated modeling methods such as multi-atlas segmentation, vessel filtering and spline-modeling. Results: Our full image volume segmentation algorithm yields median DICE coefficients of 0.98, 0.93, 0.82, 0.74, 0.51 and 0.48 regarding soft-tissue, liver, bone, skin, blood and bile vessels for ten test patients and three selected reference patients. Compared to standard slicewise manual contouring time saving is remarkable. Conclusions: Our segmentation process shows out efficiency and robustness for upper abdominal puncture simulation systems. This marks a significant step toward establishing patient-specific training and hands-on planning systems in a clinical environment. (C) 2016 Elsevier Ireland Ltd. All rights reserved.

关键词： Virtual reality simulation Efficient CT image segmentation atlas-based segmentation Full body segmentation Cloud computing

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atlas-based segmentation of Brain Tumor Images Using a Markov Random Field-based Tumor Growth Model and Non-Rigid Registration

Atlas-Based Segmentation of Brain Tumor Images Using a Marko...

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32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Bauer, Stefan Seiler, Christof Bardyn, Thibaut Buechler, Philippe Reyes, Mauricio Univ Bern Inst Surg Technol & Biomech CH-3012 Bern Switzerland

ISBN: (纸本)9781424441242

We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed with the aim of improving the registration accuracy in case of patients with space-occupying lesions. We perform tests on 2D axial slices of five different patient data sets and show that the approach gives good results for the segmentation of white matter, grey matter, cerebrospinal fluid and the tumor.

关键词： 2D axial slices Markov processes Markov random field-based tumor growth model atlas-based segmentation biomedical MRI brain brain tumor images cerebrospinal fluid grey matter image registration image segmentation medical image processing mesh-free method nonrigid registration pathologic patient image space-occupying lesions tumor mass effect tumor position tumor-induced soft tissue deformation tumours white matter

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Generation and Disease Model Relevance of a Manganese Enhanced Magnetic Resonance Imaging-based NOD/scid-IL-2Rγ c ^null Mouse Brain atlas

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JOURNAL OF NEUROIMMUNE PHARMACOLOGY 2016年第1期11卷 133-141页

作者： Sajja, Balasrinivasa R. Bade, Aditya N. Zhou, Biyun Uberti, Mariano G. Gorantla, Santhi Gendelman, Howard E. Boska, Michael D. Liu, Yutong Univ Nebraska Med Ctr Dept Radiol 981045 Nebraska Med Ctr Omaha NE USA Univ Nebraska Med Ctr Dept Pharmacol & Expt Neurosci 985880 Nebraska Med Ctr Omaha NE USA Huanzhong Univ Sci & Technol Tongji Med Coll Anesthesiol Wuhan Peoples R China

Strain specific mouse brain magnetic resonance imaging (MRI) atlases provide coordinate space linked anatomical registration. This allows longitudinal quantitative analyses of neuroanatomical volumes and imaging metrics for assessing the role played by aging and disease to the central nervous system. As NOD/scid-IL-2R gamma (c) (null) (NSG) mice allow human cell transplantation to study human disease, these animals are used to assess brain morphology. Manganese enhanced MRI (MEMRI) improves contrasts amongst brain components and as such can greatly help identifying a broad number of structures on MRI. To this end, NSG adult mouse brains were imaged in vivo on a 7.0 Tesla MR scanner at an isotropic resolution of 100 mu m. A population averaged brain of 19 mice was generated using an iterative alignment algorithm. MEMRI provided sufficient contrast permitting 41 brain structures to be manually labeled. Volumes of 7 humanized mice brain structures were measured by atlas-based segmentation and compared against non-humanized controls. The humanized NSG mice brain volumes were smaller than controls (p < 0.001). Many brain structures of humanized mice were significantly smaller than controls. We posit that the irradiation and cell grafting involved in the creation of humanized mice were responsible for the morphological differences. Six NSG mice without MnCl2 administration were scanned with high resolution T-2-weighted MRI and segmented to test broad utility of the atlas.

关键词： In vivo mouse brain atlas MEMRI Mouse brain morphology atlas-based segmentation NSG mice

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Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2016年第5期11卷 817-826页

作者： Wang, Jinke Cheng, Yuanzhi Guo, Changyong Wang, Yadong Tamura, Shinichi Harbin Inst Technol Sch Comp Sci & Technol Harbin 150006 Peoples R China Harbin Univ Sci & Technol Dept Software Engn Rongcheng Peoples R China Osaka Univ Ctr Adv Med Engn & Informat Suita Osaka Japan

Purpose Propose a fully automatic 3D segmentation framework to segment liver on challenging cases that contain the low contrast of adjacent organs and the presence of pathologies from abdominal CT images. Methods First, all of the atlases are weighted in the selected training datasets by calculating the similarities between the atlases and the test image to dynamically generate a subject-specific probabilistic atlas for the test image. The most likely liver region of the test image is further determined based on the generated atlas. A rough segmentation is obtained by a maximum a posteriori classification of probability map, and the final liver segmentation is produced by a shape-intensity prior level set in the most likely liver region. Our method is evaluated and demonstrated on 25 test CT datasets from our partner site, and its results are compared with two state-of-the-art liver segmentation methods. Moreover, our performance results on 10 MICCAI test datasets are submitted to the organizers for comparison with the other automatic algorithms. Results Using the 25 test CT datasets, average symmetric surface distance is 1.09 +/- 0.34 mm (range 0.62-2.12 mm), root mean square symmetric surface distance error is 1.72 +/- 0.46 mm (range 0.97-3.01 mm), and maximum symmetric surface distance error is 18.04 +/- 3.51 mm (range 12.73-26.67 mm) by our method. Our method on 10 MICCAI test data sets ranks 10th in all the 47 automatic algorithms on the site as of July 2015. Quantitative results, as well as qualitative comparisons of segmentations, indicate that our method is a promising tool to improve the efficiency of both techniques. Conclusion The applicability of the proposed method to some challenging clinical problems and the segmentation of the liver are demonstrated with good results on both quantitative and qualitative experimentations. This study suggests that the proposed framework can be good enough to replace the time-consuming and tedious slice-by-slice manual

关键词： Active shape model Statistical shape model Expectation maximization atlas-based segmentation Level set segmentation

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A fast approach for hippocampal segmentation from T1-MRI for predicting progression in Alzheimer's disease from elderly controls

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JOURNAL OF NEUROSCIENCE METHODS 2016年第0期270卷 61-75页

作者： Platero, Carlos Carmen Tobar, M. Tech Univ Madrid Hlth Sci Technol Grp Ronda Valencia 3 Madrid 28012 Spain

Background: We provide and evaluate an open-source software solution for automatically measuring hippocampal volume and hippocampal surface roughness based on T1-weighted MRI, which allows for discriminating between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls (NC) using only one scan. New method: This solution is based on a fast multiple-atlas segmentation technique, which combines a patch-based labeling method with an atlas-warping using non-rigid registrations. Results: The classifications are comparable to the best classifications in a large clinical dataset. For AD vs control, we obtain a high degree of accuracy, approximately 90%. For MCI vs control, we obtain accuracies ranging from 70% to 78%. The average time for the hippocampal segmentation from a T1-MRI is less than 17 min. Comparison with existing method: In this study, we investigate a combination of our method with annotations using the Harmonized Hippocampal Protocol (HarP). We compare its capabilities with the FreeSurfer method and verify its impact on segmentation and diagnostic group separation capabilities. Our approach is developed and validated using 134 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with annotations from HarP. Then, this method, tuned with the best parameters, is applied to 162 subjects from a private image database. Conclusions: Our approach with HarP annotations has a high level of accuracy for segmentation of the hippocampus and is robust to multi-site data. The bio-markers extracted from our proposed method have discriminative power based on a scalar feature, showing robustness in generalization and avoid over fitting. The computational time in our hippocampal segmentation algorithm has decreased considerably compared to other available analysis. (C) 2016 Elsevier B.V. All rights reserved.

关键词： atlas-based segmentation Alzheimer's disease Patch-based label fusion Hippocampal segmentation Magnetic resonance imaging

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Fully automatic analysis of the knee articular cartilage T_1ρ relaxation time using voxel-based relaxometry

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JOURNAL OF MAGNETIC RESONANCE IMAGING 2016年第4期43卷 970-980页

作者： Pedoia, Valentina Li, Xiaojuan Su, Favian Calixto, Nathaniel Majumdar, Sharmila UC San Francisco Dept Radiol & Biomed Imaging Musculoskeletal Quantitat Imaging Res Grp San Francisco CA USA

PurposeTo develop and compare with the classical region of interest (ROI)-based approach a fully automatic, local, and unbiased way of studying the knee T-1 relaxation time by creating an atlas and using voxel-based relaxometry (VBR) in osteoarthritis (OA) and anterior cruciate ligament (ACL) subjects. Materials and MethodsIn this study 110 subjects from two cohorts: 1) Mild OA 40 patients with mild-OA Kellgren-Lawrence (KL) 2 and 15 controls KL 1;2) ACL cohort (a model for early OA): 40 ACL-injured patients imaged prior to ACL reconstruction and 1-year postsurgery and 15 controls are analyzed. All the subjects were acquired at 3T with a protocol that includes: 3D-FSE (CUBE) and 3D-T-1. A nonrigid registration technique was applied to align all the images on a single template. This allows for performing VBR to assess local statistical differences of T-1 values using z-score analysis. VBR results were compared with those obtained with classical ROI-based technique. ResultsROI-based results from atlas-based segmentation were consistent with classical ROI-based method (coefficient of variation [CV]=3.83%). Voxel-based group analysis revealed local patterns that were overlooked by the ROI-based approach;eg, VBR showed posterior lateral femur and posterior lateral tibia significant T-1 elevations in ACL-injured patients (sample mean z-score=9.7 and 10.3). Those elevations were overlooked by the classical ROI-based approach (sample mean z-score=1.87 and -1.73) ConclusionVBR is a feasible and accurate tool for the local evaluation of the biochemical composition of knee articular cartilage. VBR is capable of detecting specific local patterns on T-1 maps in OA and ACL subjects. J. Magn. Reson. Imaging 2016;43:970-980

关键词： T-1 knee cartilage voxel based relaxometry atlas-based segmentation ACL osteoarthritis

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Evaluation of Multi atlas-based Approaches for the segmentation of the Thyroid Gland in IMRT Head and Neck CT Images

Evaluation of Multi Atlas-based Approaches for the Segmentat...

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Conference on Medical Imaging 2011 - Image Processing

作者： Chen, Antong Niermann, Kenneth J. Deeley, Matthew A. Dawant, Benoit M. Vanderbilt Univ Dept Elect Engn & Comp Sci 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Ingram Canc Ctr Dept Radiat Oncol Nashville TN 37232 USA

ISBN: (纸本)9780819485045

Segmenting the thyroid gland in head and neck CT images for IMRT treatment planning is of great importance. In this work, we evaluate and compare multi-atlas methods to segment this structure. The various methods we evaluate range from using a single average atlas representative of the population to selecting one atlas based on three similarity measures. We also compare ways to combine segmentation results obtained with several atlases, i.e., vote rule, and STAPLE, which is a commonly used method to combine multiple segmentations. We show that the best results are obtained when several atlases are combined. We also show that with our data sets, STAPLE does not lead to the best results.

关键词： atlas-based segmentation nonrigid registration head and neck IMRT thyroid gland

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SUB-CORTICAL SHAPE MORPHOLOGY AND VOXEL-based FEATURES FOR ALZHEIMER'S DISEASE CLASSIFICATION

SUB-CORTICAL SHAPE MORPHOLOGY AND VOXEL-BASED FEATURES FOR A...

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International Symposium on Biomedical Imaging

作者： Shashank Tripathi Seyed Hossein Nozadi Mahsa Shakeri Samuel Kadoury MedICAL Laboratory Polytechnique Montreal

ISBN: (纸本)9781509011735

Neurodegenerative pathologies, such as Alzheimer's disease, are linked with morphological alterations and tissue variations in subcortical structures which can be assessed from medical imaging and biological data. In this work, we present an unsupervised framework for the classification of Alzheimer's disease (AD) patients, stratifying patients into four diagnostic groups, namely: AD, early Mild Cognitive Impairment (MCI), late MCI and normal controls by combining shape and voxel-based features from 12 sub-cortical areas. An automated anatomical labeling using an atlas-based segmentation approach is proposed to extract multiple regions of interest known to be linked with AD progression. We take advantage of gray-matter voxel-based intensity variations and structural alterations extracted with a spherical harmonics framework to learn the discriminative features between multiple diagnostic classes. The proposed method is validated on 600 patients from the ADNI database by training binary SVM classifiers of dimensionality reduced features, using both linear and RBF kernels. Results show near state-of-the-art approaches in classification accuracy (>88%), especially for the more challenging discrimination tasks: AD vs. LMCI (76.81%), NC vs. EMCI (75.46%) and EMCI vs. LMCI (70.95%). By combining multimodality features, this pipeline demonstrates the potential by exploiting complementary features to improve cognitive assessment.

关键词： Alzheimer's Disease Morphology shape analysis atlas-based segmentation Classification Mild cognitive impairment

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Generation and evaluation of an ultra-high-field atlas with applications in DBS planning

Generation and evaluation of an ultra-high-field atlas with ...

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Conference on Medical Imaging - Image Processing

作者： Wang, Brian T. Poirier, Stefan Guo, Ting Parrent, Andrew G. Peters, Terry M. Khan, Ali R. Western Univ 1151 Richmond St London ON N6A 3K7 Canada Robarts Res Inst 1151 Richmond St London ON N6A 5B7 Canada Hosp Sick Children 555 Univ Ave Toronto ON M5G 1X8 Canada Western Univ Dept Clin Neurol Serv 339 Windermere Rd London ON N6A 5A5 Canada Western Univ Dept Med Biophys & Med Imaging London ON Canada

ISBN: (纸本)9781510600195

Purpose Deep brain stimulation (DBS) is a common treatment for Parkinson's disease (PD) and involves the use of brain atlases or intrinsic landmarks to estimate the location of target deep brain structures, such as the subthalamic nucleus (STN) and the globus pallidus pars interna (GPi). However, these structures can be difficult to localize with conventional clinical magnetic resonance imaging (MRI), and thus targeting can be prone to error. Ultra-high-field imaging at 7T has the ability to clearly resolve these structures and thus atlases built with these data have the potential to improve targeting accuracy. Methods TI and T2-weighted images of 12 healthy control subjects were acquired using a 7T MR scanner. These images were then used with groupwise registration to generate an unbiased average template with T1w and T2w contrast. Deep brain structures were manually labelled in each subject by two raters and rater reliability was assessed. We compared the use of this unbiased atlas with two other methods of atlas-based segmentation (single-template and multi-template) for subthalamic nucleus (STN) segmentation on 7T MRI data. We also applied this atlas to clinical DBS data acquired at 1.5T to evaluate its efficacy for DBS target localization as compared to using a standard atlas. Results The unbiased templates provide superb detail of subcortical structures. Through one-way ANOVA tests, the unbiased template is significantly (p < 0.05) more accurate than a single-template in atlas-based segmentation and DBS target localization tasks. Conclusion The generated unbiased averaged templates provide better visualization of deep brain nuclei and an increase in accuracy over single-template and lower field strength atlases.

关键词： Computational atlases Ultra high field MRI Parkinson's disease atlas-based segmentation DBS planning Registration 7 Tesla

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Automated tissue classification of pediatric brains from magnetic resonance images using age-specific atlases

Automated tissue classification of pediatric brains from mag...

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SPIE Biomedical Applications in Molecular, Structural and Functional Imaging Conference

作者： Metzger, Andrew Benavides, Amanda Nopoulos, Peg Magnotta, Vincent Univ Iowa Dept Biomed Engn 200 Hawkins Dr Iowa City IA 52242 USA Univ Iowa Dept Psychiat 200 Hawkins Dr Iowa City IA 52242 USA Univ Iowa Dept Radiol 200 Hawkins Dr Iowa City IA 52242 USA

ISBN: (纸本)9781510600232

The goal of this project was to develop two age appropriate atlases (neonatal and one year old) that account for the rapid growth and maturational changes that occur during early development. Tissue maps from this age group were initially created by manually correcting the resulting tissue maps after applying an expectation maximization (EM) algorithm and an adult atlas to pediatric subjects. The EM algorithm classified each voxel into one of ten possible tissue types including several subcortical structures. This was followed by a novel level set segmentation designed to improve differentiation between distal cortical gray matter and white matter. To minimize the required manual corrections, the adult atlas was registered to the pediatric scans using high-dimensional, symmetric image normalization (SyN) registration. The subject images were then mapped to an age specific atlas space, again using SyN registration, and the resulting transformation applied to the manually corrected tissue maps. The individual maps were averaged in the age specific atlas space and blurred to generate the age appropriate anatomical priors. The resulting anatomical priors were then used by the EM algorithm to re-segment the initial training set as well as an independent testing set. The results from the adult and age-specific anatomical priors were compared to the manually corrected results. The age appropriate atlas provided superior results as compared to the adult atlas. The image analysis pipeline used in this work was built using the open source software package BRAINSTools.

关键词： atlas-based segmentation level set segmentation magnetic resonance imaging (MRI) tissue classification brain neonate image analysis

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