Nonrigid registration with corresponding points constraint for automatic segmentation of cardiac DSCT images
 Xuesong Lu^{1},
 Rongqian Yang^{2}Email author,
 Qinlan Xie^{1},
 Shanxing Ou^{3},
 Yunfei Zha^{4} and
 Defeng Wang^{5, 6}Email author
DOI: 10.1186/s1293801703231
© The Author(s) 2017
Received: 14 July 2016
Accepted: 10 February 2017
Published: 28 March 2017
Abstract
Background
Dualsource computed tomography (DSCT) is a very effective way for diagnosis and treatment of heart disease. The quantitative information of spatiotemporal DSCT images can be important for the evaluation of cardiac function. To avoid the shortcoming of manual delineation, it is imperative to develop an automatic segmentation technique for 4D cardiac images.
Methods
In this paper, we implement the heart segmentationpropagation framework based on nonrigid registration. The corresponding points of anatomical substructures are extracted by using the extension of ndimensional scale invariant feature transform method. They are considered as a constraint term of nonrigid registration using the freeform deformation, in order to restrain the large variations and boundary ambiguity between subjects.
Results
We validate our method on 15 patients at ten time phases. Atlases are constructed by the training dataset from ten patients. On the remaining data the median overlap is shown to improve significantly compared to original mutual information, in particular from 0.4703 to 0.5015 (\( p = 5.0 \times 10^{  4} \)) for left ventricle myocardium and from 0.6307 to 0.6519 (\( p = 6.0 \times 10^{  4} \)) for right atrium.
Conclusions
The proposed method outperforms standard mutual information of intensity only. The segmentation errors had been significantly reduced at the left ventricle myocardium and the right atrium. The mean surface distance of using our framework is around 1.73 mm for the whole heart.
Keywords
Dualsource computed tomography (DSCT) Nonrigid registration Mutual information Corresponding points ndimensional scale invariant feature transform (nSIFT) Automatic heart segmentationBackground
In recent years, the morbidity of cardiovascular diseases (CVDs) is rapidly increasing in China. An estimated 3.0 million Chinese died from CVDs in 2011, accounting for 41% of all deaths [1]. An early diagnosis and treatment for this illness is of great use to reduce the death toll. Although the doctors diagnose it by electrocardiogram or imaging of patient, the shortcoming of these means is absence of quantitative information. Recent advances in evaluation of cardiac function based on medical images have shown tremendous potential towards achieving quantitative diagnosis [2, 3]. Among the various imaging modalities, cardiac magnetic resonance imaging (MRI) is a mainstream technology because of nonionizing radiation [4].
However, computed tomography (CT) has been widely used in the form of not only 3D images describing the cardiac anatomy but also 3D + time image sequences including anatomical and functional information [5]. Some advanced techniques such as multislice CT (MSCT) [6] and dualsource CT (DSCT) [7] have demonstrated high specificity and distinguishability in cardiac structure. To evaluate the cardiac function, doctors commonly extract the cardiac chambers, large vessels or coronary arteries from a patient scan. Classical manual delineation is no longer suitable for 3D and 3D + time images due to the quantity of data. The other reason is observer variations of manual annotation might affect the reliability and repeatability of quantitative evaluation. It is highly desirable to develop an automatic segmentation for clinical problem.
At present, automatic segmentation methods of the heart fall into two broad categories. Boundarybased segmentation incorporating prior knowledge is just one of those things. Assen et al. [8] presented a 3D active shape model for semiautomatic segmentation of cardiac CT and MR volumes. A fuzzy cmeans based fuzzy inference system was incorporated into the model. A new method was proposed for the local assessment of boundary detection [9]. It took any boundary detection function and evaluated its performance for a single model landmark in terms of an estimated geometric boundary detection error. The authors demonstrated this method can automatically segment computed tomography and magnetic resonance images. Shang introduced a novel scheme for the segmentation of 4D MR cardiac images [10]. 3D spatially hierarchical expressions of the statistical shape models for the cardiac chambers were constructed through principal component analysis (PCA) of the manually segmented training set. The limitation of these approaches is that the surfaces from different substructures of the heart are prone to intersect each other in segmentation results. An automatic method was proposed to segment the left ventricles and then identify their borders robustly [11]. The strengths of four techniques: automatic threshold selection, boundary extraction, deformation flow tracking, and convex shape modeling were effectively combined. In a review of segmentation methods of cardiac MR images using the shortaxis view, the authors proposed the two main categories: segmentation based on no or weak prior, and segmentation based on strong prior [12].
Another popular method, called registrationbased segmentation, is to propagate the segmentation of an atlas image using deformation field after registration. Zhuang et al. [13] proposed a fully automatic whole heart segmentation framework. The locally affine registration method and the freeform deformations with adaptive control point status were applied to registration procedure. Peyrat et al. [14] presented a framework for the nonlinear spatiotemporal registration of 4D timeseries of images based on the Diffeomorphic Demons algorithm. The authors declared that registration should be consistent over time as opposed to 3D registration which solely aims at mapping homologous points at a given timepoint. A novel multiatlas segmentation incorporating the intensity, gradient and contextual information was suggested for cardiac MR images [15]. Experimental results show that the accuracy of multiatlas segmentation can be significantly improved by using the augmented feature vector. Berendsen et al. [16] proposed a new registration with application to organ segmentation in cervical MR. A statistical model, trained on the shapes of a set of segmentations, was integrated as a penalty term in a freeform registration framework. Compared with registration without the use of statistical knowledge, the segmentations were significantly improved.
In this paper, we propose a nonrigid registration algorithm with corresponding points constraint. The feature point pairs are extracted from the fixed and moving image by the extension of ndimensional scale invariant feature transform (nSIFT) method. Automatic segmentation of 4D cardiac CT images is implemented by the use of propagation framework based on registration. Our method addresses in the large variations and boundary ambiguity of the heart structures for the registration between subjects. We evaluate the method on fifteen patients at ten time phases, among which the training dataset is from ten patients for atlas construction. The remaining data is used to test the performance of our approach with comparison to original mutual information.
Methods
Atlas construction
In order to produce a high quality atlas, some researchers involved the statistical shape model into registration procedure [6, 18, 19]. A statistical atlas can provide some advantages in the postprocessing and analysis of largely variable datasets. The encoding of population variation indicates that their spatial relationships are known. Here we achieved the registration incorporating shape information of multipleobject for atlas construction so as to avoid the complexity of statistical shape model. The important steps are described as follows:

A combined transformation T = T _{ global } + T _{ local } is employed. The global transformation is an affine model, and the local transformation is a freeform deformation (FFD) model based on Bsplines [20].

αMutual information (αMI) [21] is combined with Kappa Statistic [22] of six substructures, which is regarded as the similarity measure.

An iterative stochastic gradient descent optimization strategy [23] is used to obtain the optimal deformation field.
Registration between atlas and unsegmented image
The goal of image registration in this subsection is to relate any point in the atlas intensity image to the unsegmented image. In other words, this purpose is to find the optimal transformation T: \( (x, y, z, t) \to (x^{\prime } , y^{\prime } , z^{\prime } , t^{\prime } ) \). We use coarsetofine strategy to perform this nonrigid registration. The affine model is applied to rough alignment of the images. Afterwards, the Bsplines FFD model is chosen to estimate local motion parameters. The affine alignment result is considered as the initial parameters of nonrigid registration using the Bsplines FFD model.
Corresponding points based on nSIFT features
In order to achieve high accuracy of intersubject registration, we extract the feature point pairs from the fixed and moving image by the extension of nSIFT [26] method. In the first step, multiscale Harris corner and extrema detector in the DoG (Difference of Gaussian) space are used to locate the distinctive points in the unregistered images.
Multiscale Harris corner detector
Local extrema detector in the DoG space
Similar to Ref. [26], a multilevel image pyramid is created by downsample of the Gaussian smoothed image. Therefore, starting from the first image at each level, a series of Gaussian blurred images are generated. For each neighboring pair of blurred images, a DoG image is generated. Within each pyramid level, a voxel of a DoG image is compared with the neighboring voxels, the corresponding voxel in the scale above and all the neighbors, and the corresponding voxel in the scale below and all the neighbors. Finally, we locate extrema with magnitude greater than a threshold T _{3}.
Experiments
The extraction algorithm of the corresponding points was implemented using the Insight Toolkit (ITK). All registrations were performed in the software package elastix (see http://elastix.isi.uu.nl). All programs were run on a Windows computer with an Intel Dual Core 2.40 GHz CPU and 64.0 GB memory.
Data
The 4D cardiac data was acquired by a dualsource CT scanner (Siemens Somatom Definition, Germany). Fifteen patients were scanned with 10% R–R interval phases. They have confirmed pathologies including myocardium infarction, aortic valve stenosis, dilated cardiomyopathy, atrial fibrillation, and tricuspid regurgitation. The image dimensions were \( 512 \times 512 \times 131 \sim 265 \) voxels of size \( 0.348 \times 0.348 \times 0.5 \) mm. It is common to see that these cases displayed a wide diversity of heart shapes. To avoid the bias of using an atlas with similar heart shape, we constructed an atlas at each time phase from ten patients with all pathologies of above. The images from the remaining five patients are considered as unsegmented images.
The manual segmentations of all these data were performed as the gold standard. They were done by either a clinician or a researcher with knowledge of heart anatomy using an opensource tool ITKSNAP (see http://www.itksnap.org). During this operation, right atrium (RA) and right ventricle (RV) were delineated firstly. Then left ventricle myocardium (LVM) and the blood cavities of left atrium (LA), left ventricle (LV) were separately segmented. The region of aorta (AO) was generated in the end (examples of manual segmentation can be found in Fig. 6). There are ten atlas intensity images and their label images, while the unsegmented images consist of fifty volume data. In total fifty registrations were performed for our proposed algorithm.
Choice of parameters
In extraction procedure of the point pairs, we have determined some parameters empirically for the good result. For multiscale Harris corner detector, the integration scale was set to \( \upsigma_{\text{I}} = 1.5 \times 2^{i} (i = 0, \ldots , 4) \) and \( \upsigma_{\text{D}} = 0.7\sigma_{I} \). The threshold parameters were set to \( {\text{T}}_{1} = {\text{T}}_{2} = 0.1 \) with \( \upalpha = 0.04 \). For local extrema detector in the DoG space, the scale factor was set to 2, the scale for Gaussian blur was set to 1.5, and \( T_{3} = 0.0075 \). We selected \( T_{4} = 0.9 \) for feature matching using nSIFT descriptor.
An affine initial registration was performed before nonrigid registration using the Bsplines FFD. To avoid the local extrema, we employed a multiresolution scheme with four levels. Gaussian smoothing instead of downsampling was applied to the moving images, with \( \upsigma = 8.0, 4.0, 2.0, \) and 1.0 voxels for x, y, and z directions. As for the Bspline control points, the grid spacing of 80, 40, 20, and 10 mm in all directions was applied to four resolution levels respectively. A value of \( \upomega = 0.01 \) can provide a good balance between the two terms of the cost function. During the parameter optimization, \( A = 50 \), \( \tau = 0.602 \), \( a = 2000 \) were set, as well as 1000 iterations were used.
Evaluation method
Results
The mean and standard deviation of DSC results using the three methods for six substructures
Structures  Methods  DSC (mean ± SD)  Cohen’s d 

AO  CC  0.7716 ± 0.0006  – 
MI  0.7759 ± 0.0150  0.410  
MI ± CP  0.7826 ± 0.0150  0.452  
LA  CC  0.6643 ± 0.0035  – 
MI  0.6980 ± 0.0460  1.044  
MI ± CP  0.7027 ± 0.0490  0.100  
LV  CC  0.6769 ± 0.0030  – 
MI  0.6863 ± 0.0520  0.258  
MI ± CP  0.6901 ± 0.0520  0.073  
LVM  CC  0.3731 ± 0.0346  – 
MI  0.4768 ± 0.0630  2.061  
MI ± CP  0.5076 ± 0.0490  0.551  
RA  CC  0.5657 ± 0.0099  – 
MI  0.6180 ± 0.0890  0.834  
MI ± CP  0.6332 ± 0.0820  0.179  
RV  CC  0.6076 ± 0.0052  – 
MI  0.6904 ± 0.0330  3.566  
MI ± CP  0.6935 ± 0.0320  0.096 
The mean and standard deviation of surface distance measure using MI + CP for six substructures
Structures  Surface error (mm) 

AO  0.73 ± 0.13 
LA  1.78 ± 0.78 
LV  1.60 ± 0.13 
LVM  2.47 ± 0.72 
RA  2.30 ± 0.79 
RV  1.50 ± 0.62 
Discussion
In the experiments, six substructures were extracted to support quantitative evaluation of cardiac functions. The proposed MI + CP approach achieves more accurate segmentation result as the multiscale Harris corner and extrema detector are adopted. In Fig. 4 it can be observed that these corresponding points are nonuniformly scattered on some substructures of cardiac images. This will result in the small Cohen’s d values of some substructures, such as left ventricle and right ventricle. One possible solution is to add some corresponding points on the edge of the specific substructures, since it can reduce local minima more directly. Another possibility is to employ some physical transformation models [30] to accommodate the large cardiac variations between two cases, which will at the same time make the proposed method more effectively.
There are two limitations of this work. Firstly, we cannot investigate the sensitivity of this framework for different atlases. It is a challenge to construct the comprehensive atlases on large and highly variable image datasets. In [6], Hoogendoorn et al. utilized spatiotemporal statistical model of the human heart based on 4D multislice CT to synthesize the high resolution atlas. This method isn’t suitable for the modeling of all cardiac substructures although it may reduce the segmentation errors. Another way is to employ a parameterfree approach that directly produces a vector field, such as the diffeomorphic demons [31]. Secondly, further improvement in computational accuracy is still required. At the left ventricle myocardium, the DSC result using the proposed framework is only 0.5076 ± 0.0490. It indicates that depending on only this propagation framework is insufficient to handle this substructure. Perhaps incorporating the boundarybased segmentation technique [32] into this process will improve this limitation.
Conclusions
We have introduced a novel registration algorithm for the implementation of the heart segmentationpropagation framework. Our aim is to improve the segmentation accuracy of DSCT images under the condition of the large variations and boundary ambiguity. An extension of nSIFT method was developed to generate the corresponding points from atlas and unsegmented image. Nonrigid registration was achieved by mutual information with corresponding points constraint based on the freeform deformation. We have tested the performance using 4D cardiac images of fifteen patients. It was shown that the median overlap of our method improves significantly on most of anatomical substructures except left ventricle, in comparison to original mutual information. The reason should be that there are no enough corresponding points to support this substructure. In fact, the segmentation of the left ventricle is more challenging than the right ventricle and other parts because large displacements frequently occur between adjacent images or the papillary muscles fuse with the wall [33]. The segmentation errors had been significantly reduced by the proposed algorithm, in particular left ventricle myocardium and right atrium. The proposed segmentation framework achieved a mean surface distance of 1.73 mm for the whole heart between the propagated segmentation and the gold standard segmentation.
In future work, the diffeomorphic demons model could be used for atlas construction. It could be valuable to further investigate the effect of different atlases on the segmentationpropagation framework. Additionally, it is also important to develop the approach that enables us to propagate the atlas from a cardiac phase to another cardiac phase.
Abbreviations
 CVDs:

cardiovascular diseases
 MRI:

magnetic resonance imaging
 CT:

computed tomography
 MSCT:

multislice CT
 DSCT:

dualsource CT
 PCA:

principal component analysis
 nSIFT:

ndimensional scale invariant feature transform
 FFD:

freeform deformation
 αMI:

αmutual information
 DoG:

difference of gaussian
 ITK:

Insight Toolkit
 AO:

aorta
 LA:

left atrium
 LV:

left ventricle
 LVM:

left ventricle myocardium
 RA:

right atrium
 RV:

right ventricle
 DSC:

dice similarity coefficient
Declarations
Authors’ contributions
XL and QX carried out the study and drafted the manuscript. RY participated in the design of the study and performed the statistical analysis. SO and YZ recruited patients and acquired the data. DW conceived of the study and revised the manuscript significantly. All authors read and approved the final manuscript.
Acknowledgements
The authors would like to thank Guangzhou General Hospital of Guangzhou Military Area Command for providing the images, L. Li and S. L. Lan for their help on cardiac anatomical knowledge and manual segmentation. The work described in this paper was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No.: CUHK 14113214), a grant from the Science, Technology and Innovation Commission of Shenzhen Municipality (Project No.: CXZZ20140606164105361), grants from grant from the Innovation and Technology Commission (Project No.: GHP/028/14SZ, ITS/293/14FP), and grants from CUHK Technology and Business Development Fund (Project No.: TBF16MED002 and TBF16MED004).
Competing interests
The authors declare that they have no competing interests.
Availability of data and materials
The raw data used for segmentation to draw the conclusion has been described in “Experiments” section. No further material will be provided.
Funding
This research was supported by the Scientific Research Project of the State Ethnic Affairs Commission of China under Grant No. 14ZNZ024, and Natural Science Foundation of Hubei Province under Grant No. 2016CFB489, and the Pearl River S&T Nova Program of Guangzhou under Grant No. 2014J2200049, and the Guangdong Provincial Science and Technology Program under Grant No. 2013B090600057.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ Affiliations
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