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Fully-automatic left ventricular segmentation from long-axis cardiac cine MR scans

MEDICAL IMAGE ANALYSIS 2017年 39卷 44-55页

作者： Shahzad, Rahil Tao, Qian Dzyubachyk, Oleh Staring, Marius Lelieveldt, Boudewijn P. F. van der Geest, Rob J. Leiden Univ Med Ctr Dept Radiol Div Image Proc POB 9600 NL-2300 RC Leiden Netherlands Delft Univ Technol Intelligent Syst Dept POB 5031 NL-2600 GA Delft Netherlands

With an increasing number of large-scale population-based cardiac magnetic resonance (CMR) imaging studies being conducted nowadays, there comes the mammoth task of image annotation and image analysis. Such population-based studies would greatly benefit from automated pipelines, with an efficient CMR image analysis workflow. The purpose of this work is to investigate the feasibility of using a fully-automatic pipeline to segment the left ventricular endocardium and epicardium simultaneously on two orthogonal (vertical and horizontal) long-axis cardiac cine MRI scans. The pipeline is based on a multi-atlas-based segmentation approach and a spatio-temporal registration approach. The performance of the method was assessed by: (i) comparing the automatic segmentations to those obtained manually at both the end-diastolic and end-systolic phase, (ii) comparing the automatically obtained clinical parameters, including end-diastolic volume, end-systolic volume, stroke volume and ejection fraction, with those defined manually and (iii) by the accuracy of classifying subjects to the appropriate risk category based on the estimated ejection fraction. Automatic segmentation of the left ventricular endocardium was achieved with a Dice similarity coefficient (DSC) of 0.93 on the end-diastolic phase for both the vertical and horizontal long-axis scan;on the end-systolic phase the DSC was 0.88 and 0.85, respectively. For the epicardium, a DSC of 0.94 and 0.95 was obtained on the end-diastolic vertical and horizontal long-axis scans;on the end-systolic phase the DSC was 0.90 and 0.88, respectively. With respect to the clinical volumetric parameters, Pearson correlation coefficient (R) of 0.97 was obtained for the end-diastolic volume, 0.95 for end-systolic volume, 0.87 for stroke volume and 0.84 for ejection fraction. Risk category classification based on ejection fraction showed that 80% of the subjects were assigned to the correct risk category and only one subject (< 1%) was more

关键词： atlas-based segmentation Registration Cardiac MRI Left ventricular segmentation Long-axis cine MRI

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Longitudinal Study Using Voxel-based Relaxometry: Association Between Cartilage T_1ρ and T₂ and Patient Reported Outcome Changes in Hip Osteoarthritis

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JOURNAL OF MAGNETIC RESONANCE IMAGING 2017年第5期45卷 1523-1533页

作者： Pedoia, Valentina Gallo, Matthew C. Souza, Richard B. Majumdar, Sharmila Univ Calif San Francisco Musculoskeletal Quantitat Imaging Res Grp Dept Radiol & Biomed Imaging San Francisco CA 94143 USA Univ Calif San Francisco Dept Phys Therapy & Rehabil Sci San Francisco CA 94143 USA

Purpose: To study the local distribution of hip cartilage T-1 rho and T-2 relaxation times and their association with changes in patient reported outcome measures (PROMs) using a fully automatic, local, and unbiased method in subjects with and without hip osteoarthritis (OA). Materials and Methods: The 3 Tesla MRI studies of the hip were obtained for 37 healthy controls and 16 subjects with radiographic hip OA. The imaging protocol included a three-dimensional (3D) SPGR sequence and a combined 3D Tip and T2 sequence. Quantitative cartilage analysis was compared between a traditional region of interest (R01)-based method and a fully automatic voxel-based relaxometry (VBR) method. Additionally, VBR was used to assess local T11, and T2 differences between subjects with and without OA, and to evaluate the association between Tip and T2 and 18 month changes PROMs. Results: Results for the two methods were consistent in the acetabular (R 0.79;coefficients of variation [CV] 2.9%) and femoral cartilage (R = 0.90;CV = 2.6%). VBR revealed local patterns of T-1 rho, and T-2 elevation in OA subjects, particularly in the posterosuperior acetabular cartilage (T-1 rho: P = 0.02;T-2: P = 0.038). Overall, higher T-1, and T-2 values at baseline, particularly in the anterosuperior acetabular cartilage (T-1 rho: Rho =-0.42;P = 0.002;T-2: Rho =-0.44;P= 0.002), were associated with worsening PROMS at 18-month follow-up. Conclusion: VBR is an accurate and robust method for quantitative MRI analysis in hip cartilage. VBR showed the capability to detect local variations in T-1 rho and T-2 values in subjects with and without osteoarthritis, and voxel based correlations demonstrated a regional dependence between baseline T-11, and T-2 values and changes in PROMs.

关键词： T1ρ T2 hip cartilage osteoarthritis voxel-based relaxometry atlas-based segmentation

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Mapping the Order and Pattern of Brain Structural MRI Changes Using Change-Point Analysis in Premanifest Huntington's Disease

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HUMAN BRAIN MAPPING 2017年第10期38卷 5035-5050页

作者： Wu, Dan Faria, Andreia V. Younes, Laurent Mori, Susumu Brown, Timothy Johnson, Hans Paulsen, Jane S. Ross, Christopher A. Miller, Michael I. Johns Hopkins Univ Sch Med Russell H Morgan Dept Radiol & Radiol Sci Baltimore MD 21218 USA Johns Hopkins Univ Ctr Imaging Sci Baltimore MD 21218 USA Johns Hopkins Univ Inst Computat Med Baltimore MD 21218 USA Johns Hopkins Univ Dept Appl Math & Stat Baltimore MD 21218 USA Kennedy Krieger Inst FM Kirby Res Ctr Funct Brain Imaging Baltimore MD USA Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA Univ Iowa Dept Psychiat Iowa City IA USA Univ Iowa Dept Neurol Iowa City IA 52242 USA Univ Iowa Dept Psychol & Neurosci Iowa City IA USA Johns Hopkins Univ Sch Med Dept Psychiat Div Neurobiol Baltimore MD USA Johns Hopkins Univ Sch Med Dept Neurol Div Neurobiol Baltimore MD USA Johns Hopkins Univ Sch Med Dept Neurosci & Pharmacol Div Neurobiol Baltimore MD USA Johns Hopkins Univ Sch Med Program Cellular & Mol Med Baltimore MD USA Johns Hopkins Univ Dept Biomed Engn Baltimore MD USA

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder that progressively affects motor, cognitive, and emotional functions. Structural MRI studies have demonstrated brain atrophy beginning many years prior to clinical onset ("premanifest" period), but the order and pattern of brain structural changes have not been fully characterized. In this study, we investigated brain regional volumes and diffusion tensor imaging (DTI) measurements in premanifest HD, and we aim to determine (1) the extent of MRI changes in a large number of structures across the brain by atlas-based analysis, and (2) the initiation points of structural MRI changes in these brain regions. We adopted a novel multivariate linear regression model to detect the inflection points at which the MRI changes begin (namely, "change-points"), with respect to the CAG-age product (CAP, an indicator of extent of exposure to the effects of CAG repeat expansion). We used approximately 300 T1-weighted and DTI data from premanifest HD and control subjects in the PREDICT-HD study, with atlas-based whole brain segmentation and change-point analysis. The results indicated a distinct topology of structural MRI changes: the change-points of the volumetric measurements suggested a central-toperipheral pattern of atrophy from the striatum to the deep white matter;and the change points of DTI measurements indicated the earliest changes in mean diffusivity in the deep white matter and posterior white matter. While interpretation needs to be cautious given the cross-sectional nature of the data, these findings suggest a spatial and temporal pattern of spread of structural changes within the HD brain. (C) 2017 Wiley Periodicals, Inc.

关键词： change-point analysis premanifest HD brain atrophy diffusion tensor MRI atlas-based segmentation spatiotemporal order

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Anatomical Region segmentation for Objective Surgical Skill Assessment with Operating Room Motion Data

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JOURNAL OF NEUROLOGICAL SURGERY PART B-SKULL BASE 2017年第6期78卷 490-496页

作者： Li, Yangming Bly, Randall A. Harbison, R. Alex Humphreys, Ian M. Whipple, Mark E. Hannaford, Blake Moe, Kris S. Univ Washington Dept Elect Engn 185 Stevens Way Seattle WA 98195 USA Univ Washington Dept Otolaryngol Head & Neck Surg Sch Med Seattle WA 98195 USA Univ Washington Sch Med Seattle Childrens Hosp Seattle WA USA

Background Most existing objective surgical motion analysis schemes are limited to structured surgical tasks or recognition of motion patterns for certain categories of surgeries. Analyzing instrument motion data with respect to anatomical structures can break the limit, and an anatomical region segmentation algorithm is required for the analysis. Methods An atlas was generated by manually segmenting the skull base into nine regions, including left/right anterior/posterior ethmoid sinuses, frontal sinus, left and right maxillary sinuses, nasal airway, and sphenoid sinus. These regions were selected based on anatomical and surgical significance in skull base and sinus surgery. Six features, including left and right eye center, nasofrontal beak, anterior tip of nasal spine, posterior edge of hard palate at midline, and clival body at foramen magnum, were used for alignment. The B-spline deformable registration was adapted to fine tune the registration, and bony boundaries were automatically extracted for final precision improvement. The resultant deformation field was applied to the atlas, and the motion data were clustered according to the deformed atlas. Results Eight maxillofacial computed tomography scans were used in experiments. One was manually segmented as the atlas. The others were segmented by the proposed method. Motion data were clustered into nine groups for every dataset and outliers were filtered. Conclusions The proposed algorithm improved the efficiency of motion data clustering and requires limited human interaction in the process. The anatomical region segmentations effectively filtered out the portion of motion data that are out of surgery sites and grouped them according to anatomical similarities.

关键词： anatomical region atlas-based segmentation motion analysis objective skill assessment operating room data skull base sinus surgery

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Image segmentation using Local Probabilistic atlases Coupled with Topological Information 12

Image Segmentation using Local Probabilistic Atlases Coupled...

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12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP)

作者： Galisot, Gaetan Brouard, Thierry Ramel, Jean-Yves Chaillou, Elodie Univ Tours LI Tours 64 Ave Jean Portalis F-37000 Tours France Univ Tours IFCE CNRS PRCINRA F-37380 Nouzilly France

ISBN: (纸本)9789897582257

atlas-based segmentation is a widely used method for Magnetic Resonance Imaging (MRI) segmentation. It is also a very efficient method for the automatic segmentation of brain structures. In this paper, we propose a more adaptive and interactive atlas-based method. The proposed model allows to combine several local probabilistic atlases with a topological graph. Local atlases can provide more precise information about the structure's shape and the spatial relationships between each of these atlases are learned and stored inside a graph representation. In this way, local registrations need less computational time and image segmentation can be guided by the user in an incremental way. Pixel classification is achieved with the help of a hidden Markov random field that is able to integrate the a priori information with the intensities coming from different modalities. The proposed method was tested on the OASIS dataset, used in the MICCAI' 12 challenge for multi-atlas labeling.

关键词： atlas-based segmentation 3D Brain Images Topological Information Markov Random Field

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SUB-CORTICAL SHAPE MORPHOLOGY AND VOXEL-based FEATURES FOR ALZHEIMER'S DISEASE CLASSIFICATION 14

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

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IEEE 14th International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Tripathi, Shashank Nozadi, Seyed Hossein Shakeri, Mahsa Kadoury, Samuel Polytech Montreal Med Lab Montreal PQ Canada CHU St Justine Res Ctr Montreal PQ Canada

ISBN: (纸本)9781509011728

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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Automated Cardiovascular segmentation in Patients with Congenital Heart Disease from 3D CMR Scans: Combining Multi-atlases and Level-Sets 1st

Automated Cardiovascular Segmentation in Patients with Conge...

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Workshop on Reconstruction and Analysis of Moving Body Organs (RAMBO) / Workshop on Whole-Heart and Great Vessel segmentation from 3D Cardiovascular MRI in Congenital Heart Disease (HVSMR)

作者： Shahzad, Rahil Gao, Shan Tao, Qian Dzyubachyk, Oleh van der Geest, Rob Leiden Univ Med Ctr Dept Radiol Div Image Proc LKEB Leiden Netherlands

ISBN: (纸本)9783319522807;9783319522791

This paper presents an automatic method that enables segmentation of the whole heart and the great vessels from 3D MRI scans. The proposed method is built upon a multi-atlas-based segmentation approach and consists of a number of intermediate steps to enable accurate segmentation of the ventricular myocardial tissue, intra-cardiac blood pool, and the aorta. The method was tested on the datasets provided by the HVSMR 2016 MICCAI challenge organizers. Results on the testing data show that, the proposed method achieved an average Dice index of 0.89 for the blood pool and 0.75 for the ventricular myocardial tissue. The corresponding average surface distance error were 1.6mm and 1.1 mm, respectively.

关键词： atlas-based segmentation Registration Tissue maps Hough transform Level-sets

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Automatic and hierarchical segmentation of the human skeleton in CT images

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PHYSICS IN MEDICINE AND BIOLOGY 2017年第7期62卷 2812-2833页

作者： Fu, Yabo Liu, Shi Li, H. Harold Yang, Deshan Washington Univ St Louis Dept Radiat Oncol Sch Med St Louis MO 63130 USA

Accurate segmentation of each bone of the human skeleton is useful in many medical disciplines. The results of bone segmentation could facilitate bone disease diagnosis and post-treatment assessment, and support planning and image guidance for many treatment modalities including surgery and radiation therapy. As a medium level medical image processing task, accurate bone segmentation can facilitate automatic internal organ segmentation by providing stable structural reference for inter- or intra-patient registration and internal organ localization. Even though bones in CT images can be visually observed with minimal difficulty due to the high image contrast between the bony structures and surrounding soft tissues, automatic and precise segmentation of individual bones is still challenging due to the many limitations of the CT images. The common limitations include low signalto-noise ratio, insufficient spatial resolution, and indistinguishable image intensity between spongy bones and soft tissues. In this study, a novel and automatic method is proposed to segment all the major individual bones of the human skeleton above the upper legs in CT images based on an articulated skeleton atlas. The reported method is capable of automatically segmenting 62 major bones, including 24 vertebrae and 24 ribs, by traversing a hierarchical anatomical tree and by using both rigid and deformable image registration. The degrees of freedom of femora and humeri are modeled to support patients in different body and limb postures. The segmentation results are evaluated using the Dice coefficient and point- to- surface error (PSE) against manual segmentation results as the ground- truth. The results suggest that the reported method can automatically segment and label the human skeleton into detailed individual bones with high accuracy. The overall average Dice coefficient is 0.90. The average PSEs are 0.41 mm for the mandible, 0.62 mm for cervical vertebrae, 0.92 mm for thoracic vertebrae

关键词： atlas-based segmentation articulated registration bony structure

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Resection planning for robotic acoustic neuroma surgery

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JOURNAL OF MEDICAL IMAGING 2017年第2期4卷 025002-025002页

作者： McBrayer, Kepra L. Wanna, George B. Dawant, Benoit M. Balachandran, Ramya Labadie, Robert F. Noble, Jack H. Vanderbilt Univ Dept Elect Engn & Comp Sci 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ Med Ctr Dept Otolaryngol Head & Neck Surg Nashville TN USA Vanderbilt Univ Med Ctr Dept Neurol Surg Nashville TN USA

Acoustic neuroma surgery is a procedure in which a benign mass is removed from the internal auditory canal (IAC). Currently, this surgical procedure requires manual drilling of the temporal bone followed by exposure and removal of the acoustic neuroma. This procedure is physically and mentally taxing to the surgeon. Our group is working on the development of an acoustic neuroma surgery robot (ANSR) to perform the initial drilling procedure. Planning the ANSR's drilling region using preoperative CT requires expertise and takes about 35 min. We propose an approach for automatically producing a resection plan for the ANSR that would avoid damage to sensitive ear structures and require minimal editing by the surgeon. We first compute an atlas-based segmentation of the mastoid section of the temporal bone, refine it based on the position of anatomical landmarks, and apply a safety margin to the result to produce the automatic resection plan. In experiments with CTs from nine subjects, our automated process resulted in a resection plan that was verified to be safe in every case. Approximately 2 min were required in each case for the surgeon to verify and edit the plan to permit functional access to the IAC. We measured a mean Dice coefficient of 0.99 and surface error of 0.08 mm between the final and automatically proposed plans. These preliminary results indicate that our approach is a viable method for resection planning for the ANSR and drastically reduces the surgeon's planning effort. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： acoustic neuroma atlas-based segmentation internal auditory canal cochlea facial nerve

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LOCAL atlas SELECTION AND PERFORMANCE ESTIMATION IN MULTI-atlas based segmentation

LOCAL ATLAS SELECTION AND PERFORMANCE ESTIMATION IN MULTI-AT...

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8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Langerak, T. R. van der Heide, U. A. Kotte, A. N. T. J. Berendsen, F. F. Pluim, J. P. W. Univ Med Ctr Utrecht Image Sci Inst Utrecht Netherlands Univ Med Ctr Utrecht Dept Radiotherapy Utrecht Netherlands

ISBN: (纸本)9781424441280

In multi-atlas based segmentation, a new image is segmented by registering multiple atlas images and propagating the corresponding atlas segmentations. These propagated segmentations are then combined in a process called label fusion. This paper presents a new, local method that divides the propagated segmentations in multiple, user-definable regions. A label fusion process can then be applied to each of these regions separately and the end result can be constructed out of multiple partial results. The new method is compared to non-local label fusion methods, as well as with another local method called ALMAS. It is shown that local selection does not lead to a significant improvement in cases where existing methods already have a good result, but that our method significantly improves the result of atlas-based segmentation in cases where existing methods are less successful.

关键词： atlas-based segmentation Local atlas Selection Label Fusion Registration segmentation

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