A Benchmark Framework for Multiregion Analysis of Vesselness Filters.

Vessel enhancement (aka vesselness) filters, are part of angiographic image processing for more than twenty years. Their popularity comes from their ability to enhance tubular structures while filtering out other structures, especially as a preliminary step of vessel segmentation. Choosing the right vesselness filter among the many available can be difficult, and their parametrization requires an accurate understanding of their underlying concepts and a genuine expertise. In particular, using default parameters is often not enough to reach satisfactory results on specific data. Currently, only few benchmarks are available to help the users choosing the best filter and its parameters for a given application. In this article, we present a generic framework to compare vesselness filters. We use this framework to compare seven gold standard filters. Our experiments are performed on three public datasets: the hepatic Ircad dataset (CT images), the Bullit dataset (brain MRA images) and the synthetic VascuSynth dataset. We analyse the results of these seven filters both quantitatively and qualitatively. In particular, we assess their performances in key areas: the organ of interest, the whole vascular network neighbourhood and the vessel neighbourhood split into several classes, based on their diameters. We also focus on the vessels bifurcations, which are often missed by vesselness filters. We provide the code of the benchmark, which includes up-to-date C++ implementations of the seven filters, as well as the experimental setup (parameter optimization, result analysis, etc.). An online demonstrator is also provided to help the community apply and visually compare these vesselness filters.

Early Alterations of Intra-Mural Elastic Lamellae Revealed by Synchrotron X-ray Micro-CT Exploration of Diabetic Aortas.

Diabetes is a major concern of our society as it affects one person out of 11 around the world. Elastic fiber alterations due to diabetes increase the stiffness of large arteries, but the structural effects of these alterations are poorly known. To address this issue, we used synchrotron X-ray microcomputed tomography with in-line phase contrast to image in three dimensions C57Bl6J (control) and db/db (diabetic) mice with a resolution of 650 nm/voxel and a field size of 1.3 mm3. Having previously shown in younger WT and db/db mouse cohorts that elastic lamellae contain an internal supporting lattice, here we show that in older db/db mice the elastic lamellae lose this scaffold. We coupled this label-free method with automated image analysis to demonstrate that the elastic lamellae from the arterial wall are structurally altered and become 11% smoother (286,665 measurements). This alteration suggests a link between the loss of the 3D lattice-like network and the waviness of the elastic lamellae. Therefore, waviness measurement appears to be a measurable elasticity indicator and the 3D lattice-like network appears to be at the origin of the existence of this waviness. Both could be suitable indicators of the overall elasticity of the aorta.

SegSRGAN: Super-resolution and segmentation using generative adversarial networks - Application to neonatal brain MRI.

BACKGROUND AND OBJECTIVE: One of the main issues in the analysis of clinical neonatal brain MRI is the low anisotropic resolution of the data. In most MRI analysis pipelines, data are first re-sampled using interpolation or single image super-resolution techniques and then segmented using (semi-)automated approaches. In other words, image reconstruction and segmentation are then performed separately. In this article, we propose a methodology and a software solution for carrying out simultaneously high-resolution reconstruction and segmentation of brain MRI data. METHODS: Our strategy mainly relies on generative adversarial networks. The network architecture is described in detail. We provide information about its implementation, focusing on the most crucial technical points (whereas complementary details are given in a dedicated GitHub repository). We illustrate the behavior of the proposed method for cortex analysis from neonatal MR images. RESULTS: The results of the method, evaluated quantitatively (Dice, peak signal-to-noise ratio, structural similarity, number of connected components) and qualitatively on a research dataset (dHCP) and a clinical one (Epirmex), emphasize the relevance of the approach, and its ability to take advantage of data-augmentation strategies. CONCLUSIONS: Results emphasize the potential of our proposed method/software with respect to practical medical applications. The method is provided as a freely available software tool, which allows one to carry out his/her own experiments, and involve the method for the super-resolution reconstruction and segmentation of arbitrary cerebral structures from any MR image dataset.

A no-reference respiratory blur estimation index in nuclear medicine for image quality assessment.

Few indexes are available for nuclear medicine image quality assessment, particularly for respiratory blur assessment. A variety of methods for the identification of blur parameters has been proposed in literature mostly for photographic pictures but these methods suffer from a high sensitivity to noise, making them unsuitable to evaluate nuclear medicine images. In this paper, we aim to calibrate and test a new blur index to assess image quality.Blur index calibration was evaluated by numerical simulation for various lesions size and intensity of uptake. Calibrated blur index was then tested on gamma-camera phantom acquisitions, PET phantom acquisitions and real-patient PET images and compared to human visual evaluation.For an optimal filter parameter of 9, non-weighted and weighted blur index led to an automated classification close to the human one in phantom experiments and identified each time the sharpest image in all the 40 datasets of 4 images. Weighted blur index was significantly correlated to human classification (ρ = 0.69 [0.45;0.84] P < .001) when used on patient PET acquisitions.The provided index allows to objectively characterize the respiratory blur in nuclear medicine acquisition, whether in planar or tomographic images and might be useful in respiratory gating applications.

Multiscale brain MRI super-resolution using deep 3D convolutional networks.

The purpose of super-resolution approaches is to overcome the hardware limitations and the clinical requirements of imaging procedures by reconstructing high-resolution images from low-resolution acquisitions using post-processing methods. Super-resolution techniques could have strong impacts on structural magnetic resonance imaging when focusing on cortical surface or fine-scale structure analysis for instance. In this paper, we study deep three-dimensional convolutional neural networks for the super-resolution of brain magnetic resonance imaging data. First, our work delves into the relevance of several factors in the performance of the purely convolutional neural network-based techniques for the monomodal super-resolution: optimization methods, weight initialization, network depth, residual learning, filter size in convolution layers, number of the filters, training patch size and number of training subjects. Second, our study also highlights that one single network can efficiently handle multiple arbitrary scaling factors based on a multiscale training approach. Third, we further extend our super-resolution networks to the multimodal super-resolution using intermodality priors. Fourth, we investigate the impact of transfer learning skills onto super-resolution performance in terms of generalization among different datasets. Lastly, the learnt models are used to enhance real clinical low-resolution images. Results tend to demonstrate the potential of deep neural networks with respect to practical medical image applications.

n D Variational Restoration of Curvilinear Structures With Prior-Based Directional Regularization.

Curvilinear structure restoration in image processing procedures is a difficult task, which can be compounded when these structures are thin, i.e., when their smallest dimension is close to the resolution of the sensor. Many recent restoration methods involve considering a local gradient-based regularization term as prior, assuming gradient sparsity. An isotropic gradient operator is typically not suitable for thin curvilinear structures, since gradients are not sparse for these. In this paper, we propose a mixed gradient operator that combines a standard gradient in the isotropic image regions, and a directional gradient in the regions where specific orientations are likely. In particular, such information can be provided by curvilinear structure detectors (e.g., RORPO or Frangi filters). Our proposed mixed gradient operator, that can be viewed as a companion tool of such detectors, is proposed in a discrete framework and its formulation/computation holds in any dimension; in other words, it is valid in [Formula: see text], n ≥ 1 . We show how this mixed gradient can be used to construct image priors that take edge orientation, as well as intensity, into account, and then involved in various image processing tasks while preserving curvilinear structures. The experiments carried out on 2D, 3D, real, and synthetic images illustrate the relevance of the proposed gradient, and its use in variational frameworks for both denoising and segmentation tasks.

Data-driven respiratory gating for ventilation/perfusion lung scan.

BACKGROUND: Ventilation/perfusion lung scan is subject to blur due to respiratory motion whether with planar acquisition or single photon emission computed tomography (SPECT). We propose a data-driven gating method for extracting different respiratory phases from lung scan list-mode or dynamic data. METHODS: The algorithm derives a surrogate respiratory signal from an automatically detected diaphragmatic region of interest. The time activity curve generated is then filtered using a Savitzky-Golay filter. We tested this method on an oscillating phantom in order to evaluate motion blur decrease and on one lung SPECT. RESULTS: Our algorithm reduced motion blur on phantom acquisition: mean full width at half maximum 8.1 pixels on non-gated acquisition versus 5.3 pixels on gated acquisition and 4.1 pixels on reference image. Automated detection of the diaphragmatic region and time-activity curves generation were successful on patient acquisition. CONCLUSIONS: This algorithm is compatible with a clinical use considering its runtime. Further studies will be needed in order to validate this method.

An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI.

Brain structure analysis in the newborn is a major health issue. This is especially the case for preterm neonates, in order to obtain predictive information related to the child development. In particular, the cortex is a structure of interest, that can be observed in magnetic resonance imaging (MRI). However, neonatal MRI data present specific properties that make them challenging to process. In this context, multi-atlas approaches constitute an efficient strategy, taking advantage of images processed beforehand. The method proposed in this article relies on such a multi-atlas strategy. More precisely, it uses two paradigms: first, a non-local model based on patches; second, an iterative optimization scheme. Coupling both concepts allows us to consider patches related not only to the image information, but also to the current segmentation. This strategy is compared to other multi-atlas methods proposed in the literature. Experiments on dHCP datasets show that the proposed approach provides robust cortex segmentation results.

Curvilinear Structure Analysis by Ranking the Orientation Responses of Path Operators.

The analysis of thin curvilinear objects in 3D images is a complex and challenging task. In this article, we introduce a new, non-linear operator, called RORPO (Ranking the Orientation Responses of Path Operators). Inspired by the multidirectional paradigm currently used in linear filtering for thin structure analysis, RORPO is built upon the notion of path operator from mathematical morphology. This operator, unlike most operators commonly used for 3D curvilinear structure analysis, is discrete, non-linear and non-local. From this new operator, two main curvilinear structure characteristics can be estimated: an intensity feature, that can be assimilated to a quantitative measure of curvilinearity; and a directional feature, providing a quantitative measure of the structure's orientation. We provide a full description of the structural and algorithmic details for computing these two features from RORPO, and we discuss computational issues. We experimentally assess RORPO by comparison with three of the most popular curvilinear structure analysis filters, namely Frangi Vesselness, Optimally Oriented Flux, and Hybrid Diffusion with Continuous Switch. In particular, we show that our method provides up to 8 percent more true positive and 50 percent less false positives than the next best method, on synthetic and real 3D images.

Directed Connected Operators: Asymmetric Hierarchies for Image Filtering and Segmentation.

Connected operators provide well-established solutions for digital image processing, typically in conjunction with hierarchical schemes. In graph-based frameworks, such operators basically rely on symmetric adjacency relations between pixels. In this article, we introduce a notion of directed connected operators for hierarchical image processing, by also considering non-symmetric adjacency relations. The induced image representation models are no longer partition hierarchies (i.e., trees), but directed acyclic graphs that generalize standard morphological tree structures such as component trees, binary partition trees or hierarchical watersheds. We describe how to efficiently build and handle these richer data structures, and we illustrate the versatility of the proposed framework in image filtering and image segmentation.

Connected filtering based on multivalued component-trees.

In recent papers, a new notion of component-graph was introduced. It extends the classical notion of component-tree initially proposed in mathematical morphology to model the structure of gray-level images. Component-graphs can indeed model the structure of any-gray-level or multivalued-images. We now extend the antiextensive filtering scheme based on component-trees, to make it tractable in the framework of component-graphs. More precisely, we provide solutions for building a component-graph, reducing it based on selection criteria, and reconstructing a filtered image from a reduced component-graph. In this paper, we first consider the cases where component-graphs still have a tree structure; they are then called multivalued component-trees. The relevance and usefulness of such multivalued component-trees are illustrated by applicative examples on hierarchically classified remote sensing images.

Topology-Preserving Rigid Transformation of 2D Digital Images.

We provide conditions under which 2D digital images preserve their topological properties under rigid transformations. We consider the two most common digital topology models, namely dual adjacency and well-composedness. This paper leads to the proposal of optimal preprocessing strategies that ensure the topological invariance of images under arbitrary rigid transformations. These results and methods are proved to be valid for various kinds of images (binary, gray-level, label), thus providing generic and efficient tools, which can be used in particular in the context of image registration and warping.

Topology preserving warping of 3-D binary images according to continuous one-to-one mappings.

The estimation of one-to-one mappings is one of the most intensively studied topics in the research field of nonrigid registration. Although the computation of such mappings can be now accurately and efficiently performed, the solutions for using them in the context of binary image deformation is much less satisfactory. In particular, warping a binary image with such transformations may alter its discrete topological properties if common resampling strategies are considered. In order to deal with this issue, this paper proposes a method for warping such images according to continuous and bijective mappings while preserving their discrete topological properties (i.e., their homotopy type). Results obtained in the context of the atlas-based segmentation of complex anatomical structures highlight the advantages of the proposed approach.

Topology preserving warping of binary images: application to atlas-based skull segmentation.

Lots of works have been recently carried out in the field of non-rigid registration to ensure the estimation of one-to-one mappings. However, warping a binary image with such transformations may alter its discrete topological properties if common resampling strategies are considered. This paper proposes an original method for warping a binary image according to some continuous and bijective mapping, while preserving its discrete topological properties. Results obtained in the context of atlas-based segmentation highlight the interest of the approach. Indeed, the method has been successfully applied to the segmentation of skull structures from a database of 15 CT-scans, providing both geometrically and topologically satisfactory results.

Region-growing segmentation of brain vessels: an atlas-based automatic approach.

PURPOSE: To propose an atlas-based method that uses both phase and magnitude images to integrate anatomical information in order to improve the segmentation of blood vessels in cerebral phase-contrast magnetic resonance angiography (PC-MRA). MATERIAL AND METHODS: An atlas of the whole head was developed to store the anatomical information. The atlas divides a magnitude image into several vascular areas, each of which has specific vessel properties. It can be applied to any magnitude image of an entire or nearly entire head by deformable matching, which helps to segment blood vessels from the associated phase image. The segmentation method used afterwards consists of a topology-preserving, region-growing algorithm that uses adaptive threshold values depending on the current region of the atlas. This algorithm builds the arterial and venous trees by iteratively adding voxels that are selected according to their grayscale value and the variation of values in their neighborhood. The topology preservation is guaranteed because only simple points are selected during the growing process. RESULTS: The method was performed on 40 PC-MRA images of the brain. The results were validated using maximum-intensity projection (MIP) and three-dimensional surface rendering visualization, and compared with results obtained with two non-atlas-based methods. CONCLUSION: The results show that the proposed method significantly improves the segmentation of cerebral vascular structures from PC-MRA. These experiments tend to prove that the use of vascular atlases is an effective way to optimize vessel segmentation of cerebral images.