Myocardium segmentation from DE MRI with guided random walks and sparse shape representation

Purpose

For patients with myocardial infarction (MI), delayed enhancement (DE) cardiovascular magnetic resonance imaging (MRI) is a sensitive and well-validated technique for the detection and visualization of MI. The myocardium viability assessment with DE MRI is important in diagnosis and treatment management, where myocardium segmentation is a prerequisite. However, few academic works have focused on automated myocardium segmentation from DE images. In this study, we aim to develop an automatic myocardium segmentation algorithm that targets DE images.

Methods

We propose a segmentation framework based on both prior shape knowledge and image intensity. Instead of the strong request of the pre-segmentation of cine MRI in the same session, we use the sparse representation method to model the myocardium shape. Data from the Cardiac MR Left Ventricle Segmentation Challenge (2009) are used to build the shape template repository. The method of guided random walks is used to integrate the shape model and intensity information. An iterative approach is used to gradually improve the results.

Results

The proposed method was tested on the DE MRI data from 30 MI patients. The proposed method achieved Dice similarity coefficients (DSC) of 74.60?±?7.79% with 201 shape templates and 73.56?±?6.32% with 56 shape templates, which were close to the inter-observer difference (73.94?±?5.12%). To test the generalization of the proposed method to routine clinical images, the DE images of 10 successive new patients were collected, which were unseen during the method development and parameter tuning, and a DSC of 76.02?±?7.43% was achieved.

Conclusion

The authors propose a novel approach for the segmentation of myocardium from DE MRI by using the sparse representation-based shape model and guided random walks. The sparse representation method effectively models the prior shape with a small number of shape templates, and the proposed method has the potential to achieve clinically relevant results.

     

Construction of a neuroanatomical shape complex atlas from 3D MRI brain structures

Brain atlas construction has attracted significant attention lately in the neuroimaging community due to its application to the characterization of neuroanatomical shape abnormalities associated with various neurodegenerative diseases or neuropsychiatric disorders. Existing shape atlas construction techniques usually focus on the analysis of a single anatomical structure in which the important inter-structural information is lost. This paper proposes a novel technique for constructing a neuroanatomical shape complex atlas based on an information geometry framework. A shape complex is a collection of neighboring shapes - for example, the thalamus, amygdala and the hippocampus circuit - which may exhibit changes in shape across multiple structures during the progression of a disease. In this paper, we represent the boundaries of the entire shape complex using the zero level set of a distance transform function S(x). We then re-derive the relationship between the stationary state wave function ψ(x) of the Schr?dinger equation [formula in text] and the eikonal equation [formula in text] satisfied by any distance function. This leads to a one-to-one map (up to scale) between ψ(x) and S(x) via an explicit relationship. We further exploit this relationship by mapping ψ(x) to a unit hypersphere whose Riemannian structure is fully known, thus effectively turn ψ(x) into the square-root of a probability density function. This allows us to make comparisons - using elegant, closed-form analytic expressions - between shape complexes represented as square-root densities. A shape complex atlas is constructed by computing the Karcher mean ψˉ(x) in the space of square-root densities and then inversely mapping it back to the space of distance transforms in order to realize the atlas shape. We demonstrate the shape complex atlas computation technique via a set of experiments on a population of brain MRI scans including controls and epilepsy patients with either right anterior medial temporal or left anterior medial temporal lobectomies.     

Unsupervised brain imaging 3D anomaly detection and segmentation with transformers

Pathological brain appearances may be so heterogeneous as to be intelligible only as anomalies, defined by their deviation from normality rather than any specific set of pathological features. Amongst the hardest tasks in medical imaging, detecting such anomalies requires models of the normal brain that combine compactness with the expressivity of the complex, long-range interactions that characterise its structural organisation. These are requirements transformers have arguably greater potential to satisfy than other current candidate architectures, but their application has been inhibited by their demands on data and computational resources. Here we combine the latent representation of vector quantised variational autoencoders with an ensemble of autoregressive transformers to enable unsupervised anomaly detection and segmentation defined by deviation from healthy brain imaging data, achievable at low computational cost, within relative modest data regimes. We compare our method to current state-of-the-art approaches across a series of experiments with 2D and 3D data involving synthetic and real pathological lesions. On real lesions, we train our models on 15,000 radiologically normal participants from UK Biobank and evaluate performance on four different brain MR datasets with small vessel disease, demyelinating lesions, and tumours. We demonstrate superior anomaly detection performance both image-wise and pixel/voxel-wise, achievable without post-processing. These results draw attention to the potential of transformers in this most challenging of imaging tasks.     

Data-driven shape parameterization for segmentation of the right ventricle from 3D+t echocardiography

Model-based segmentation facilitates the accurate measurement of geometric properties of anatomy from ultrasound images. Regularization of the model surface is typically necessary due to the presence of noisy and incomplete boundaries. When simple regularizers are insufficient, linear basis shape models have been shown to be effective. However, for problems such as right ventricle (RV) segmentation from 3D+t echocardiography, where dense consistent landmarks and complete boundaries are absent, acquiring accurate training surfaces in dense correspondence is difficult.As a solution, this paper presents a framework which performs joint segmentation of multiple 3D+t sequences while simultaneously optimizing an underlying linear basis shape model. In particular, the RV is represented as an explicit continuous surface, and segmentation of all frames is formulated as a single continuous energy minimization problem. Shape information is automatically shared between frames, missing boundaries are implicitly handled, and only coarse surface initializations are necessary.The framework is demonstrated to successfully segment both multiple-view and multiple-subject collections of 3D+t echocardiography sequences, and the results confirm that the linear basis shape model is an effective model constraint. Furthermore, the framework is shown to achieve smaller segmentation errors than a state-of-art commercial semi-automatic RV segmentation package.     

一种精确提取三维脑图像对称面的方法

在脑图像的结构和功能研究中,提取三维脑图像中间矢状位对称面(MSP)起着很重要的作用。本文提出一种自动分步精确优化的计算方法,通过结合子空间粗略定位及原始空间小范围精确优化计算,提高计算精度,可有效简化初始对称面的选择,并使用Powell优化算法高效计算出对称面。采用本算法对多种条件的模拟数据和真实数据进行性能测试,对于不同模态、噪声水平和不均匀场等数据均获得了较其他代表性方法更好的提取结果。     

Fast extraction of minimal paths in 3D images and applications to virtual endoscopy

The aim of this article is to build trajectories for virtual endoscopy inside 3D medical images, using the most automatic way. Usually the construction of this trajectory is left to the clinician who must define some points on the path manually using three orthogonal views. But for a complex structure such as the colon, those views give little information on the shape of the object of interest. The path construction in 3D images becomes a very tedious task and precise a priori knowledge of the structure is needed to determine a suitable trajectory. We propose a more automatic path tracking method to overcome those drawbacks: we are able to build a path, given only one or two end points and the 3D image as inputs. This work is based on previous work by Cohen and Kimmel [Int. J. Comp. Vis. 24 (1) (1997) 57] for extracting paths in 2D images using Fast Marching algorithm.Our original contribution is twofold. On the first hand, we present a general technical contribution which extends minimal paths to 3D images and gives new improvements of the approach that are relevant in 2D as well as in 3D to extract linear structures in images. It includes techniques to make the path extraction scheme faster and easier, by reducing the user interaction.We also develop a new method to extract a centered path in tubular structures. Synthetic and real medical images are used to illustrate each contribution.On the other hand, we show that our method can be efficiently applied to the problem of finding a centered path in tubular anatomical structures with minimum interactivity, and that this path can be used for virtual endoscopy. Results are shown in various anatomical regions (colon, brain vessels, arteries) with different 3D imaging protocols (CT, MR).     

Automatic cerebral and cerebellar hemisphere segmentation in 3D MRI: Adaptive disconnection algorithm

This paper describes the automatic Adaptive Disconnection method to segment cerebral and cerebellar hemispheres of human brain in three-dimensional magnetic resonance imaging (MRI). Using the partial differential equations based shape bottlenecks algorithm cooperating with an information potential value clustering process, it detects and cuts, first, the compartmental connections between the cerebrum, the cerebellum and the brainstem in the white matter domain, and then, the interhemispheric connections of the extracted cerebrum and cerebellum volumes. As long as the subject orientation in the scanner is given, the variations in subject location and normal brain morphology in different images are accommodated automatically, thus no stereotaxic image registration is required. The modeling of partial volume effect is used to locate cerebrum, cerebellum and brainstem boundaries, and make the interhemispheric connections detectable. The Adaptive Disconnection method was tested with 10 simulated images from the BrainWeb database and 39 clinical images from the LONI Probabilistic Brain Atlas database. It obtained lower error rates than a traditional shape bottlenecks algorithm based segmentation technique (BrainVisa) and linear and nonlinear registration based brain hemisphere segmentation methods. Segmentation accuracies were evaluated against manual segmentations. The Adaptive Disconnection method was also confirmed not to be sensitive to the noise and intensity non-uniformity in the images. We also applied the Adaptive Disconnection method to clinical images of 22 healthy controls and 18 patients with schizophrenia. A preliminary cerebral volumetric asymmetry analysis based on these images demonstrated that the Adaptive Disconnection method is applicable to study abnormal brain asymmetry in schizophrenia.     

Learning a cortical parcellation of the brain robust to the MRI segmentation with convolutional neural networks

The parcellation of the human cortex into meaningful anatomical units is a common step of various neuroimaging studies. There have been multiple successful efforts to process magnetic resonance (MR) brain images automatically and identify specific anatomical regions, following atlases defined from cortical landmarks. Those definitions usually rely first on a high-quality brain surface reconstruction. On the other hand, when high accuracy is not a requirement, simpler methods based on warping a probabilistic atlas have been widely adopted. Here, we develop a cortical parcellation method for MR brain images based on Convolutional Neural Networks (ConvNets), a machine-learning method, with the goal of automatically transferring the knowledge obtained from surface analyses onto something directly applicable on simpler volume data. We train a ConvNet on a large (thousand) set of cortical ribbons of multiple MRI cohorts, to reproduce parcellations obtained from a surface method, in this case FreeSurfer. Further, to make the model applicable in a broader context, we force the model to generalize to unseen segmentations. The model is evaluated on unseen data of unseen cohorts. We characterize the behavior of the model during learning, and quantify its reliance on the dataset itself, which tends to give support for the necessity of large training sets, augmentation, and multiple contrasts. Overall, ConvNets can provide an efficient way to parcel MRI images, following the guidance established within more complex methods, quickly and accurately. The trained model is embedded within a open-source parcellation tool available at https://github.com/bthyreau/parcelcortex.     

Fully automatic segmentation of the Femur from 3D-CT images using primitive shape recognition and statistical shape models

Purpose

Femur segmentation is well established and widely used in computer-assisted orthopedic surgery. However, most of the robust segmentation methods such as statistical shape models (SSM) require human intervention to provide an initial position for the SSM. In this paper, we propose to overcome this problem and provide a fully automatic femur segmentation method for CT images based on primitive shape recognition and SSM.

Method

Femur segmentation in CT scans was performed using primitive shape recognition based on a robust algorithm such as the Hough transform and RANdom SAmple Consensus. The proposed method is divided into 3 steps: (1) detection of the femoral head as sphere and the femoral shaft as cylinder in the SSM and the CT images, (2) rigid registration between primitives of SSM and CT image to initialize the SSM into the CT image, and (3) fitting of the SSM to the CT image edge using an affine transformation followed by a nonlinear fitting.

Results

The automated method provided good results even with a high number of outliers. The difference of segmentation error between the proposed automatic initialization method and a manual initialization method is less than 1 mm.

Conclusion

The proposed method detects primitive shape position to initialize the SSM into the target image. Based on primitive shapes, this method overcomes the problem of inter-patient variability. Moreover, the results demonstrate that our method of primitive shape recognition can be used for 3D SSM initialization to achieve fully automatic segmentation of the femur.     

Contrastive rendering with semi-supervised learning for ovary and follicle segmentation from 3D ultrasound

Segmentation of ovary and follicles from 3D ultrasound (US) is the crucial technique of measurement tools for female infertility diagnosis. Since manual segmentation is time-consuming and operator-dependent, an accurate and fast segmentation method is highly demanded. However, it is challenging for current deep-learning based methods to segment ovary and follicles precisely due to ambiguous boundaries and insufficient annotations. In this paper, we propose a contrastive rendering (C-Rend) framework to segment ovary and follicles with detail-refined boundaries. Furthermore, we incorporate the proposed C-Rend with a semi-supervised learning (SSL) framework, leveraging unlabeled data for better performance. Highlights of this paper include: (1) A rendering task is performed to estimate boundary accurately via enriched feature representation learning. (2) Point-wise contrastive learning is proposed to enhance the similarity of intra-class points and contrastively decrease the similarity of inter-class points. (3) The C-Rend plays a complementary role for the SSL framework in uncertainty-aware learning, which could provide reliable supervision information and achieve superior segmentation performance. Through extensive validation on large in-house datasets with partial annotations, our method outperforms state-of-the-art methods in various evaluation metrics for both the ovary and follicles.     

Joint optimization of segmentation and shape prior from level-set-based statistical shape model,and its application to the automated segmentation of abdominal organs

The goal of this study is to provide a theoretical framework for accurately optimizing the segmentation energy considering all of the possible shapes generated from the level-set-based statistical shape model (SSM). The proposed algorithm solves the well-known open problem, in which a shape prior may not be optimal in terms of an objective functional that needs to be minimized during segmentation. The algorithm allows the selection of an optimal shape prior from among all possible shapes generated from an SSM by conducting a branch-and-bound search over an eigenshape space. The proposed algorithm does not require predefined shape templates or the construction of a hierarchical clustering tree before graph-cut segmentation. It jointly optimizes an objective functional in terms of both the shape prior and segmentation labeling, and finds an optimal solution by considering all possible shapes generated from an SSM. We apply the proposed algorithm to both pancreas and spleen segmentation using multiphase computed tomography volumes, and we compare the results obtained with those produced by a conventional algorithm employing a branch-and-bound search over a search tree of predefined shapes, which were sampled discretely from an SSM. The proposed algorithm significantly improves the segmentation performance in terms of the Jaccard index and Dice similarity index. In addition, we compare the results with the state-of-the-art multiple abdominal organs segmentation algorithm, and confirmed that the performances of both algorithms are comparable to each other. We discuss the high computational efficiency of the proposed algorithm, which was determined experimentally using a normalized number of traversed nodes in a search tree, and the extensibility of the proposed algorithm to other SSMs or energy functionals.     

A review of 3D vessel lumen segmentation techniques: Models,features and extraction schemes

Vascular diseases are among the most important public health problems in developed countries. Given the size and complexity of modern angiographic acquisitions, segmentation is a key step toward the accurate visualization, diagnosis and quantification of vascular pathologies.Despite the tremendous amount of past and on-going dedicated research, vascular segmentation remains a challenging task. In this paper, we review state-of-the-art literature on vascular segmentation, with a particular focus on 3D contrast-enhanced imaging modalities (MRA and CTA). We structure our analysis along three axes: models, features and extraction schemes. We first detail model-based assumptions on the vessel appearance and geometry which can embedded in a segmentation approach. We then review the image features that can be extracted to evaluate these models. Finally, we discuss how existing extraction schemes combine model and feature information to perform the segmentation task.Each component (model, feature and extraction scheme) plays a crucial role toward the efficient, robust and accurate segmentation of vessels of interest. Along each axis of study, we discuss the theoretical and practical properties of recent approaches and highlight the most advanced and promising ones.     

Asymmetry in dopamine D(2/3) receptors of caudate nucleus is lost with age

Molecular and functional imaging techniques reveal evidence for lateralization of human cerebral function. Based on animal data, we hypothesized that asymmetry in dopamine neurotransmission declines during normal aging. In order to test this hypothesis, we measured dopamine D2/3 receptor availability with [18F]desmethoxyfallypride-PET (DMFP) in putamen and caudate nucleus (NC) of 21 healthy, right-handed males (24-60 years; 35+/-10). For volumetric analysis, high-resolution T1-weighted MR-images were obtained in 18 of the PET-subjects in order to assess possible age-related decreases in NC and putamen volume. The calculated DMFP binding potentials (BP) showed a right-ward asymmetry in NC of young subjects that decreased with age (r = 0.577, p = 0.006; Pearson correlation; two-tailed). An age-independent analysis showed a right-ward asymmetry in NC of the whole subject group (left: 1.49+/-0.35; right: 1.65+/-0.43 [mean+/-S.D.]; p = 0.020). No such side lateralization or age-effects could be found in the putamen. Volumes tended to be asymmetric in the putamen (right: 4.85+/-0.56 cm3; left: 4.64+/-0.86 cm3 [mean+/-S.D.]; p = 0.063), but not in NC. The decline of putamen volume during aging was significant in the right putamen (r = -0.613; p = 0.007; Pearson correlation; two-tailed). There were no other significant correlations between striatal volumes and age or BP. Because ventral striatal dopamine neurotransmission is involved in cognitive processes, this loss of physiological asymmetry in NC dopamine transmission during aging might be involved in age-related declines of cognitive performance.     

Automatic registration using implicit shape representations: applications in intraoperative 3D rotational angiography to preoperative CTA registration

Purpose  A solution for automatic registration of 3D Rotational Angiography (XA) to CT/MR of the liver. Targeted for use in treatment planning of liver interventions. Methods  A shape-based approach to registration is proposed that does not require specification of landmarks nor is it prone to local minima like purely intensity-based registration methods. Through the use of vessel characteristics, accurate registration is possible even in the presence of deformations induced by catheters and respiratory motion. Results  Registration was performed on eight pairs of multiphase CT angiography and 3D rotational digital angiography datasets. Quantitative validation of the registration accuracy using vessel landmarks was performed on these datasets. The validation study showed that the method has a registration error of 9.41  ±  4.13 mm. In addition, the computation time is well below 60 s making it attractive for clinical application. Conclusion  A new method for fully automatic 3DXA to CT/MR image registration was developed and found to be efficient and accurate using clinically realistic datasets.     

Validation of a fully automated 3D hippocampal segmentation method using subjects with Alzheimer's disease mild cognitive impairment, and elderly controls

We introduce a new method for brain MRI segmentation, called the auto context model (ACM), to segment the hippocampus automatically in 3D T1-weighted structural brain MRI scans of subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In a training phase, our algorithm used 21 hand-labeled segmentations to learn a classification rule for hippocampal versus non-hippocampal regions using a modified AdaBoost method, based on approximately 18,000 features (image intensity, position, image curvatures, image gradients, tissue classification maps of gray/white matter and CSF, and mean, standard deviation, and Haar filters of size 1x1x1 to 7x7x7). We linearly registered all brains to a standard template to devise a basic shape prior to capture the global shape of the hippocampus, defined as the pointwise summation of all the training masks. We also included curvature, gradient, mean, standard deviation, and Haar filters of the shape prior and the tissue classified images as features. During each iteration of ACM - our extension of AdaBoost - the Bayesian posterior distribution of the labeling was fed back in as an input, along with its neighborhood features as new features for AdaBoost to use. In validation studies, we compared our results with hand-labeled segmentations by two experts. Using a leave-one-out approach and standard overlap and distance error metrics, our automated segmentations agreed well with human raters; any differences were comparable to differences between trained human raters. Our error metrics compare favorably with those previously reported for other automated hippocampal segmentations, suggesting the utility of the approach for large-scale studies.     

Multiscale properties of weighted total variation flow with applications to denoising and registration

Images consist of structures of varying scales: large scale structures such as flat regions, and small scale structures such as noise, textures, and rapidly oscillatory patterns. In the hierarchical (BV, L²) image decomposition, Tadmor, et al. (2004) start with extracting coarse scale structures from a given image, and successively extract finer structures from the residuals in each step of the iterative decomposition. We propose to begin instead by extracting the finest structures from the given image and then proceed to extract increasingly coarser structures. In most images, noise could be considered as a fine scale structure. Thus, starting the image decomposition with finer scales, rather than large scales, leads to fast denoising. We note that our approach turns out to be equivalent to the nonstationary regularization in Scherzer and Weickert (2000). The continuous limit of this procedure leads to a time-scaled version of total variation flow.Motivated by specific clinical applications, we introduce an image depending weight in the regularization functional, and study the corresponding weighted TV flow. We show that the edge-preserving property of the multiscale representation of an input image obtained with the weighted TV flow can be enhanced and localized by appropriate choice of the weight. We use this in developing an efficient and edge-preserving denoising algorithm with control on speed and localization properties. We examine analytical properties of the weighted TV flow that give precise information about the denoising speed and the rate of change of energy of the images.An additional contribution of the paper is to use the images obtained at different scales for robust multiscale registration. We show that the inherently multiscale nature of the weighted TV flow improved performance for registration of noisy cardiac MRI images, compared to other methods such as bilateral or Gaussian filtering. A clinical application of the multiscale registration algorithm is also demonstrated for aligning viability assessment magnetic resonance (MR) images from 8 patients with previous myocardial infarctions.