Efficient classifier generation and weighted voting for atlas-based segmentation: two small steps faster and closer to the combination oracle

Artaechevarria, Xabier; Muñoz-Barrutia, Arrate; Ortiz‐de‐Solórzano, Carlos

doi:10.1117/12.769401

Cited by 25 publications

(28 citation statements)

References 13 publications

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“…Therefore, local miss-matches degrade the quality of the outcome. Similar observations were reported elsewhere in the context of prostate and brain segmentation ( Artaechevarria et al, 2009( Artaechevarria et al, , 2008.…”

Section: Nmisupporting

confidence: 76%

“…The first step toward weighted atlasbased segmentation consists in developing a similarity criterion between the target image and aligned atlas images. Normalized mutual information (NMI), normalized cross correlation (NCC) and mean square distance (MSD) are the most common similarity measures used for implementation of weighted atlas-based segmentation ( Yushkevich et al, 2010 ;Artaechevarria et al, 2008 ). These similarity measures are briefly described below.…”

Section: Global Weightingmentioning

confidence: 99%

“…The voxel the majority of classifiers agree on is labeled as bone ( Eq. 3 ) ( Heckemann et al, 2006 ;Artaechevarria et al, 2009 ;Yushkevich et al, 2010 ;Artaechevarria et al, 2008 ).…”

Section: General Averagingmentioning

confidence: 99%

“…One strategy to overcome the misalignment error consists in assigning weights to the atlas images globally (as opposed to local or voxel-wise approach) on the basis of morphological similarity between target and atlas images. By this approach, aligned atlas images presenting the higher degree of anatomy and pose similarities contribute more effectively to the resulting segmentation ( Artaechevarria et al, 2009 ;Chandra et al, 2012 ;Ying et al, 2013 ;Artaechevarria et al, 2008 ). The first step toward weighted atlasbased segmentation consists in developing a similarity criterion between the target image and aligned atlas images.…”

Section: Global Weightingmentioning

confidence: 99%

“…A number of studies using multi-atlas based segmentation employed STA-PLE algorithm to find the optimal combination of segmentations suggested by the different classifiers ( Artaechevarria et al, 2009 ;Artaechevarria et al, 2008 ). STAPLE is an expectation-maximization algorithm for simultaneous truth and performance level estimation that considers a collection of segmentations and computes a probabilistic estimate of the true segmentation and a measure of the performance level represented by each segmentation.…”

Section: Staplementioning

confidence: 99%

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Comparison of atlas-based techniques for whole-body bone segmentation

Arabi

Zaidi

2017

Medical Image Analysis

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a b s t r a c tWe evaluate the accuracy of whole-body bone extraction from whole-body MR images using a number of atlas-based segmentation methods. The motivation behind this work is to find the most promising approach for the purpose of MRI-guided derivation of PET attenuation maps in whole-body PET/MRI. To this end, a variety of atlas-based segmentation strategies commonly used in medical image segmentation and pseudo-CT generation were implemented and evaluated in terms of whole-body bone segmentation accuracy. Bone segmentation was performed on 23 whole-body CT/MR image pairs via leave-one-out cross validation procedure. The evaluated segmentation techniques include: (i) intensity averaging (IA), (ii) majority voting (MV), (iii) global and (iv) local (voxel-wise) weighting atlas fusion frameworks implemented utilizing normalized mutual information (NMI), normalized cross-correlation (NCC) and mean square distance (MSD) as image similarity measures for calculating the weighting factors, along with other atlas-dependent algorithms, such as (v) shape-based averaging (SBA) and (vi) Hofmann's pseudo-CT generation method. The performance evaluation of the different segmentation techniques was carried out in terms of estimating bone extraction accuracy from whole-body MRI using standard metrics, such as Dice similarity (DSC) and relative volume difference (RVD) considering bony structures obtained from intensity thresholding of the reference CT images as the ground truth. Considering the Dice criterion, global weighting atlas fusion methods provided moderate improvement of whole-body bone segmentation (DSC = 0.65 ± 0.05) compared to non-weighted IA (DSC = 0.60 ± 0.02). The local weighed atlas fusion approach using the MSD similarity measure outperformed the other strategies by achieving a DSC of 0.81 ± 0.03 while using the NCC and NMI measures resulted in a DSC of 0.78 ± 0.05 and 0.75 ± 0.04, respectively. Despite very long computation time, the extracted bone obtained from both SBA (DSC = 0.56 ± 0.05) and Hofmann's methods (DSC = 0.60 ± 0.02) exhibited no improvement compared to non-weighted IA. Finding the optimum parameters for implementation of the atlas fusion approach, such as weighting factors and image similarity patch size, have great impact on the performance of atlas-based segmentation approaches. The voxel-wise atlas fusion approach exhibited excellent performance in terms of cancelling out the non-systematic registration errors leading to accurate and reliable segmentation results. Denoising and normalization of MR images together with optimization of the involved parameters play a key role in improving bone extraction accuracy.

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Section: Nmisupporting

confidence: 76%

Section: Global Weightingmentioning

confidence: 99%

“…The voxel the majority of classifiers agree on is labeled as bone ( Eq. 3 ) ( Heckemann et al, 2006 ;Artaechevarria et al, 2009 ;Yushkevich et al, 2010 ;Artaechevarria et al, 2008 ).…”

Section: General Averagingmentioning

confidence: 99%

Section: Global Weightingmentioning

confidence: 99%

Section: Staplementioning

confidence: 99%

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Comparison of atlas-based techniques for whole-body bone segmentation

Arabi

Zaidi

2017

Medical Image Analysis

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Local label learning (LLL) for subcortical structure segmentation: Application to hippocampus segmentation

Hao

Wang

Zhang

et al. 2013

Human Brain Mapping

112

135

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Automatic and reliable segmentation of subcortical structures is an important but difficult task in quantitative brain image analysis. Multi-atlas based segmentation methods have attracted great interest due to their promising performance. Under the multi-atlas based segmentation framework, using deformation fields generated for registering atlas images onto a target image to be segmented, labels of the atlases are first propagated to the target image space and then fused to get the target image segmentation based on a label fusion strategy. While many label fusion strategies have been developed, most of these methods adopt predefined weighting models that are not necessarily optimal. In this study, we propose a novel local label learning strategy to estimate the target image's segmentation label using statistical machine learning techniques. In particular, we use a L1-regularized support vector machine (SVM) with a k nearest neighbor (kNN) based training sample selection strategy to learn a classifier for each of the target image voxel from its neighboring voxels in the atlases based on both image intensity and texture features. Our method has produced segmentation results consistently better than state-of-the-art label fusion methods in validation experiments on hippocampal segmentation of over 100 MR images obtained from publicly available and in-house datasets. Volumetric analysis has also demonstrated the capability of our method in detecting hippocampal volume changes due to Alzheimer's disease.

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An adaptive probabilistic atlas for anomalous brain segmentation in MR images

et al. 2019

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Purpose: Automated segmentation of brain structures (objects) in MR three-dimensional (3D) images for quantitative analysis has been a challenge and probabilistic atlases (PAs) are among the most well-succeeded approaches. However, the existing models do not adapt to possible object anomalies due to the presence of a disease or a surgical procedure. Post-processing operation does not solve the problem, for example, tissue classification to detect and remove such anomalies inside the resulting segmentation mask, because segmentation errors on healthy tissues cannot be fixed. Such anomalies very often alter the shape and texture of the brain structures, making them different from the appearance of the model. In this paper, we present an effective and efficient adaptive probabilistic atlas, named AdaPro, to circumvent the problem and evaluate it on a challenging taskthe segmentation of the left hemisphere, right hemisphere, and cerebellum, without pons and medulla, in 3D MR-T1 brain images of Epilepsy patients. This task is challenging due to temporal lobe resections, artifacts, and the absence of contrast in some parts between the structures of interest. Methods: In AdaPro, we first build one probabilistic atlas per object of interest from a training set with normal 3D images and the corresponding 3D object masks. Second, we incorporate a texture classifier based on convex optimization which dynamically indicates the regions of the target 3D image where the PAs (shape constraints) should be further adapted. This strategy is mathematically more elegant and avoids problems with post-processing. Third, we add a new object-based delineation algorithm based on combinatorial optimization and diffusion filtering. AdaPro can then be used to locate and delineate the objects in the coordinate space of the atlas or of the test image. We also compare AdaPro with three other state-of-the-art methods: an statistical shape model based on synergistic object search and delineation, and two methods based on multi-atlas label fusion (MALF). Results: We evaluate the methods quantitatively on 3D MR-T1 brain images of 2T and 3T from epilepsy patients, before and after temporal lobe resections, and on the template and native coordinate spaces. The results show that AdaPro is considerably faster and consistently more accurate than the baselines with statistical significance in both coordinate spaces. Conclusion: AdaPro can be used as a fast and effective step for brain tissue segmentation and it can also be easily extended to segment subcortical brain structures. By choice of its components, probabilistic atlas, texture classifier, and delineation algorithm, it can also be extended to other organs and imaging modalities.

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Efficient classifier generation and weighted voting for atlas-based segmentation: two small steps faster and closer to the combination oracle

Cited by 25 publications

References 13 publications

Comparison of atlas-based techniques for whole-body bone segmentation

Comparison of atlas-based techniques for whole-body bone segmentation

Local label learning (LLL) for subcortical structure segmentation: Application to hippocampus segmentation

An adaptive probabilistic atlas for anomalous brain segmentation in MR images

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