Some of those methods, such as freesurfer 5, are already used as a starting point for a manual ner segmentation later 6. Specifically, we also included our recentlydeveloped patch based segmentation method in. Automatic atlasbased threelabel cartilage segmentation. What is the meaning of atlas in atlas based segmentation. Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. Msp extraction method the algorithm presented in 9 is used in this paper to automatically extract the msp. The general procedure is to compare a pixel with its immediate surrounding neighbors. Comparison of multiple segmentation approaches for lymph level targets and normal structures in head and neck cancer.
An atlasbased autosegmentation atlasbased auto method. Multiatlas based segmentation editing tool segediting. Hippocampus segmentation on epilepsy and alzheimers disease. The merge split decisions are made together in a globally optimal manner instead of by looking only at local region pairs. Enhancing atlas based segmentation with multiclass linear classifiers. Automatic atlas based threelabel cartilage segmentation from mr knee images liang shana, christopher zachb, cecil charlesc, marc niethammera,d adepartment of computer science, university of north carolina at chapel hill, usa. Atlas based segmentation is a powerful and popular technique for automatic delineation of structures in volumetric images 9,11,12,14,15,16,17,18, especially the hippocampus 18,19,20.
Acmbased automatic liver segmentation from 3d ct images. This paper revisited atlas based automatic segmentation techniques, in the context of mouse brain in vivo mrm images, focusing on the effects of image registration and the complexity of segmentation models. A hybrid approach to the skull stripping problem in mri. Label fusion based multi atlas segmentation has proven to be one of the most competitive techniques for medical image segmentation. Adaptive registration and atlas based segmentation by hyunjin. What is the meaning of atlas in atlasbased segmentation. Joint csmri reconstruction and segmentation with a uni. Index termsatlasbased image segmentation, medical image. International journal of radiation oncology biology physics 1 october 2011 vol. Multiatlasbased segmentation of the cardiac mr right ventricle 3 that can establish a more reliable matching compared to other regions. Zooming process with robust registration and atlas selection yangming ou, jimit doshi, guray erus, and christos davatzikos section of biomedical image analysis sbia department of radiology, university of pennsylvania abstract. Systematic evaluation of three different commercial. First, we quantify the undersegmentation in atlasbased segmentation with new volume overlap measures. Improving label fusion in multiatlas based segmentation by.
The reference atlas consists of two brains of the same shape one is the original histological image, and the other is the manually annotated regions. Automatic brain structural parcellation through registrationbased segmentationpropagation and multiatlasbased labelfusion dancebeanmulti atlassegmentation. Comparative clinical evaluation of atlas and deeplearning. The idea of this work is to use as an aid for beginners in the. Multiple automatically generated templates brain segmentation.
Multiatlas segmentation using robust featurebased registration. Inthispaper,wefocusonmultiatlassegmentation methods that map all labeled images onto the target image, which helps to reduce segmentation errors 6,8,11. Atlas based under segmentation christian wachinger 1. Automatic segmentation of the hearts left atrium offers great benefits for planning and outcome evaluation of atrial ablation procedures. We further propose the segnetmri model to merge the mri reconstruction and segmentation into an single model. Atlas based methods and active contours are two families of techniques widely used for the task of 3d medical image segmentation. Atlas based segmentation exploits knowledge from previously labeled training images to segment the target image. Staple was originally proposed for combining manual segmentations done by mul. Elekta s atlas based autosegmentation abas software significantly speeds contouring by giving you an advanced starting point. Such an approach is particularly well suited to the registration of cardiac images, where the two images may have signi cant di erences, or even missing correspondence. To validate, in the context of adaptive radiotherapy, three commercial software solutions for atlasbased segmentation. Atlasbasedsegmentation atlasbased segmentation is a method used to locate the interface between the test image and the optimally matched organs from labeled, segmented reference image data 30.
The manifold learning approach used can be viewed as an atlasbased segmentation. Moreover, the choice of volume to label biases the algorithm. However, the approach that dominated early atlas guided segmentation was probabilistic atlas based segmentation ashburner and friston, 2005. This technique transfers segmentations from expertlabeled images, called atlases, to a novel image using deformable image registration. Atlas based approaches have been proposed to get automatic delineations of the organs at risk in the brain 1, and automatic delineations of the lymph nodes andor organs at risk in the head and neck region 2,3. Request pdf 3d geometric splitmerge segmentation of brain mri datasets in this paper, a novel method for mri volume segmentation based on region adaptive splitting and merging is proposed. Augmenting atlasbased liver segmentation for radiotherapy. Improving label fusion in multi atlas based segmentation by locally combining atlas selection and performance estimation citation for published version apa. With multi atlas based segmentation, the data from multiple atlases is used to segment segment a. Hippocampus segmentation on epilepsy and alzheimers. Broadly speaking, the research in mri segmentation can be categorized into three classes.
However, the approach that dominated early atlasguided segmentation was probabilistic atlasbased segmentation ashburner and friston, 2005. In the atlas based segmentation step, the center of the nar row band atlas is regarded as the initial contour to initialize level set function. I1, in with the corresponding manual segmentations an external file that. Methods of this style are typically referred to as atlas based segmentation methods. Us9122950b2 method and apparatus for learningenhanced. Both region based and curv e based units are discussed in the follo wing sections. Department of electronic systems and information processing, faculty of electrical engineering and computing, universiy of zagreb, unska 3, 0 zagreb, croatia phone. Atlas based automatic mouse brain image segmentation. As we will see below, this can be viewed as a special case of multiatlas segmentation, since all atlases are consulted for segmentation. Multi atlas segmentation with joint label fusion and. Feb 27, 2016 in this work, we propose a novel multiatlas segmentation to surface method called multiatlas cruise macruise, which achieves selfconsistent whole brain segmentations and cortical surfaces by combining multiatlas segmentation with the cortical reconstruction method cruise. I found a brain mri segmentation method that is based on atlas, but i dont know the meaning of atlas. In this article, several contributions to atlas based segmentation have been proposed. It is useful when you would like to correct large errors with a few user interactions such as dots or rough scribbles using one or multiple reference labels of the target object.
Comparison of two atlas based segmentation methods for head and neck cancer including rtogdefined lymph node levels conclusion a new method of atlas based segmentation which uses an automatic registration approximation technique to influence the intensity based deformation was found to be more. Exp erience has sho wn that an implemen tor of mac hine vision applications m ust b e able to c ho ose from a to olset of metho ds and p erhaps. Robust atlasbased segmentation of highly variable anatomy. When using atlas based segmentation, the choice of the atlas is crucial, and several strategies have been proposed. In brain mri analysis, image segmentation is commonly used for measuring and visualizing the brains anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and imageguided. Automated atlasbased segmentation of brain structures in mr. Manual contouring is a timeconsuming component of the treatment planning process. In practice, the use of singleatlas segmentation is limited for two reasons. Joint csmri reconstruction and segmentation with a. One can extend the power of both region and boundarybased segmentation methods by combining the strengths of the two. First, the tree structure enables the incorporation of higher order image information into segmentation. Atlasbased segmentation of medical images enlighten.
For example, we can make regionjoining decisions based not only on pixel or neighborhood similarity but also on alreadyextracted edges and completion of these edges. Adaptive registration and atlas based segmentation by hyunjin park cochairs. Index termsatlasbased image segmentation, medical image registration, atlas construction, statistical model, unbiased. Atlasbased segmentation methods can be categorized into three groups isgum et al. Atlasbased segmentation exploits knowledge from previously labeled training images to segment the target image. In recent years, researchers have investigated registration algorithms to match atlases to query subjects and also strategies for atlas construction. Split and merge iteratively split and merge regions to form the best segmentation. For a comprehensive survey of multiatlas segmentation methods and their applications, see 12. We provide evidence that segmenting only one organ of interest and merging.
Automatic atlasbased segmentation of nissl stained mouse. Automatic atlasbased threelabel cartilage segmentation from. Label fusion combines the transferred labels into the final segmentation. Specifically, we also included our recentlydeveloped patch based segmentation method in this software.
Automated atlasbased segmentation of brain structures in. Each patch proposes a segmentation of the lv that somehow must be fused. Oct 01, 2019 given a set of labelled mr images atlases and unlabelled images subjects, maget produces a segmentation for each subject using a multi atlas voting procedure based on a template library made up of images from the subject set. Evaluation of the effect of doubling atlases using. Automatic atlasbased threelabel cartilage segmentation from mr knee images liang shana, christopher zachb, cecil charlesc, marc niethammera,d adepartment of computer science, university of north carolina at chapel hill, usa bmicrosoft research cambridge, uk cdepartment of radiology, duke university, usa dbiomedical research imaging center, university of north carolina at chapel hill, usa. The mergesplit decisions are made together in a globally optimal manner instead of by looking only at local region pairs. To summarize, this thesis focuses on atlasbased segmentation. Our contribution is closely related to this idea, comparing atlasbased segmentation approaches qualitatively and quantitatively according to their strategy, target and accuracy reported in the literature. The commercial atlas based contouring software mim maestro 6. The prosp ects of ha ving a single segmen tation system w ork w ell for all problems app ear to b e dim. Multiatlas segmentation of the whole hippocampus and.
Multiatlas segmentation of the cardiac mr right ventricle. Multiatlas based segmentation editing tool segediting description. Ji, hongwei 11 presented an autocontext model acmbased automatic liver segmentation algorithm, which combined acm, multiatlases and meanshift techniques to segment liver from 3d ct images. Pluima a image sciences institute, university medical center utrecht, the netherlands bdepartment of radiotherapy, university medical center utrecht, the netherlands. Atlas based segmentation exploits knowledge from previously labeled training imagestosegmentthetargetimage. Atlas based segmentation atlas based segmentation is a method used to locate the interface between the test image and the optimally matched organs from labeled, segmented reference image data 30. Learning based atlas selection for multiple atlas segmentation gerard sanroma, guorong wu, yaozong gao, dinggang shen department of radiology and bric, university of north carolina at chapel hill, usa. Multiatlas segmentation has emerged as an alternative but it has a sim. Improving label fusion in multiatlas based segmentation.
Each pixel is labeled by the closest point of the atlas belonging to the same tissue class. Automatic atlas based segmentation approaches have been widely used in. Pdf on apr 25, 2007, torsten rohlfing and others published quo vadis, atlasbased segmentation. However, the high anatomical variability of the left atrium presents significant challenges for atlasguided segmentation. We provide evidence that segmenting only one organ of interest and merging all. Region merging recursively merge regions that are similar. The overall goal of atlas based segmentation is to assist radiologists in the detection and diagnosis of diseases. Multiatlasregistrationmar12,7,11,hasbeen widely used to ameliorate these problems. For a comprehensive survey of multiatlas segmentation methods and. Hippocampus segmentation based on local linear mapping.
Atlas based segmentation, is now a common im age processing tool. As we will see below, this can be viewed as a special case of multi atlas segmentation, since all atlases are consulted for segmentation. In this work we present a coupled framework where the two methods are combined together, in order to exploit eachs advantage while avoid their respective drawbacks. Nov 27, 2019 atlas based segmentation is a method used to locate the interface between the test image and the optimally matched organs from labeled, segmented reference image data. Toennies, a new approach for model based adaptive region growing in medical image analysis, 2001 j. The initial contour is near around the liver region boundary. Atlasbased autosegmentation is promising in help solving contouring problem in rtp hierarchical registration scheme and incorporating atlas object shape info helps robust atlas registration and segmentation using multiple atlases significantly improve accuracy of abas gpuacceleration makes computation feasible in practice. Disclosed herein are techniques for enhancing the accuracy of atlasbased autosegmentation abas using an automated structure classifier that was trained using a machine learning algorithm. Region splitting recursively divide regions that are heterogeneous. By using agematched atlases to segment a test subject, these techniques may handle the variability in cortical folding and maturation levels relatively well, but they. Learningbased atlas selection for multipleatlas segmentation.
First, a new labeling technique to combine the output of atlas based segmentation with intensity classification segmentation has been presented. Regionbased segmentation algorithms postulate that neighboring pixels within the same region have similar intensity values, of which the splitandmerge 3 technique is probably the most well known. The registration between the narrow band regions is fast than the whole liver region. Comparative advantage of the atlasbased segmentation with respect to the other segmentation methods is the ability to segment the image with no well defined relation between regions and pixels intensities. Atlas based segmentation methods can be categorized into three groups isgum et al. Also disclosed is a technique for training the automated structure classifier using atlas data applied to the machine learning algorithm. A common tendency of atlasbased segmentation to undersegment has largely. Hippocampus segmentation on epilepsy and alzheimers disease studies with multiple convolutional neural networks diedre carmo, bruna silva, clarissa yasuda, letcia rittner, roberto lotufoa aschool of electrical and computer engineering and faculty of medical sciences, unicamp, campinas, so paulo, brazil. Atlas based segmentation of medical images is an image analysis task which involves labelling a desired anatomy or set of anatomy from images generated by medical imaging modalities.
For many applications, a clinical expert can manually label several images. Our hypothesis ties the undersegmentation to the asymmetry of most segmentation setups where we seek to identify a single organ and merge all surrounding structures. Multiatlas based segmentation inria sophia antipolis. We study the widespread, but rarely discussed, tendency of atlas based segmentation to undersegment the organs of interest. Brain structure segmentation methods that combine atlas registration with a sta. Multiatlas based multiimage segmentation 1 an algorithm for effective atlasbased groupwise segmentation, which has been published as. Fessler with the rapid developments in image registration techniques, registrations are applied not only as linear transforms but also as warping transforms with increasing frequency. Improving label fusion in multiatlas based segmentation by locally combining atlas selection and performance estimationq t.
In this paper, we focus on multiatlas segmentation methods that map all labeled images onto the target image, which helps to reduce segmentation errors 6,8,11. Comparison of two atlasbased segmentation methods for head. Adaptive registration and atlas based segmentation by. Medical image computing mic is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine. Multiple atlas segmentation mas has recently gained popularity for labeling the anatomical structures of a target image 1, 2, 10, 11. Regions are allowed to merge if they are separated mostly by weak edges. Our hypothesis ties the undersegmentation to the asymmetry of most segmentation setups where we seek to identify a single organ and merge all surrounding structures into one large background class. Atlasbased segmentation methods also aim to segment different targets, such as, for instance, brain structures, brain tissues, or lesions.
In general, mars has integrated several stateoftheart multi atlas based segmentation methods, such as majority voting, local weighted voting, and nonlocal patch based segmentation methods. Combining multiatlas segmentation with brain surface estimation. Learningbased atlas selection for multipleatlas segmentation gerard sanroma, guorong wu, yaozong gao, dinggang shen department of radiology and bric, university of north carolina at chapel hill, usa. Vessel segmentation in mri using a variational image.
Atlasbased segmentation is a method used to locate the interface between the test image and the optimally matched organs from labeled, segmented reference image data. The commercial atlasbased contouring software mim maestro 6. In this paper, we focus on multi atlas segmentation methods that map all labeled images onto the target image, which helps to reduce segmentation errors 6,8,11. A widely used method consists to extract this prior knowledge from a reference image often called atlas. Theuseofasinglelabeledvolume atlasislimitedin registrationbased segmentation because it is hard for one atlas to represent the whole data population, especially if input images observe large variation. We define this process as atlas based segmentation. In this paper, we demonstrate an automatic method for left atrium segmentation using weighted voting label fusion and a variant of the demons. It segments an unknown target image by transferring the labels from a population of annotated exemplars i.
Atlasbased segmentation exploits knowledge from previously labeled training imagestosegmentthetargetimage. In general, mars has integrated several stateoftheart multiatlas based segmentation methods, such as majority voting, local weighted voting, and nonlocal patch based segmentation methods. It is a fast and accurate method that nds out the msp based on bilateral symmetry maximization. In addition to all of this, building an atlas is a challenging problem in and of itself. Hongjun jia, pewthian yap, dinggang shen, iterative multiatlasbased multiimage segmentation with treebased registration, accepted for neuroimage.
As an entry to the miccai 2012 prostate segmentation challenge, this paper presents a multi atlas based automatic. This field develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care. Multiatlas segmentation using robust featurebased registration 3 the fused segmentation proposal can be further re. Atlasbased segmentation techniques are also challenged by the same issues that make learningbased segmentation of the fetal cp challenging. First, we quantify the under segmentation in atlas based segmentation with new volume overlap measures. The highcost associated to manual segmentation has stimulated the search for e ective automatic segmentation methods.
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