Precise segmentation of 3-D magnetic resonance angiography

Segmentation-assisted vessel centerline extraction from cerebral CT Angiography

BACKGROUND

The accurate automated extraction of brain vessel centerlines from Computed tomographic angiography (CTA) images plays an important role in diagnosing and treating cerebrovascular diseases such as stroke. Despite its significance, this task is complicated by the complex cerebrovascular structure and heterogeneous imaging quality.

PURPOSE

This study aims to develop and validate a segmentation-assisted framework designed to improve the accuracy and efficiency of brain vessel centerline extraction from CTA images. We streamline the process of lumen segmentation generation without additional annotation effort from physicians, enhancing the effectiveness of centerline extraction.

METHODS

The framework integrates four modules: (1) pre-processing techniques that register CTA images with a CT atlas and divide these images into input patches, (2) lumen segmentation generation from annotated vessel centerlines using graph cuts and robust kernel regression, (3) a dual-branch topology-aware UNet (DTUNet) that optimizes the use of the annotated vessel centerlines and the generated lumen segmentation via a topology-aware loss (TAL) and its dual-branch structure, and (4) post-processing methods that skeletonize and refine the lumen segmentation predicted by the DTUNet.

RESULTS

An in-house dataset derived from a subset of the MR CLEAN Registry is used to evaluate the proposed framework. The dataset comprises 10 intracranial CTA images, and 40 cube CTA sub-images with a resolution of128 × 128 × 128 $128 \times 128 \times 128$ voxels. Via five-fold cross-validation on this dataset, we demonstrate that the proposed framework consistently outperforms state-of-the-art methods in terms of average symmetric centerline distance (ASCD) and overlap (OV). Specifically, it achieves an ASCD of 0.84, anOV 1.0 $\textrm {OV}_{1.0}$ of 0.839, and anOV 1.5 $\textrm {OV}_{1.5}$ of 0.885 for intracranial CTA images, and obtains an ASCD of 1.26, anOV 1.0 $\textrm {OV}_{1.0}$ of 0.779, and anOV 1.5 $\textrm {OV}_{1.5}$ of 0.824 for cube CTA sub-images. Subgroup analyses further suggest that the proposed framework holds promise in clinical applications for stroke diagnosis and treatment.

CONCLUSIONS

By automating the process of lumen segmentation generation and optimizing the network design of vessel centerline extraction, DTUnet achieves high performance without introducing additional annotation demands. This solution promises to be beneficial in various clinical applications in cerebrovascular disease management.

Self-supervised learning for accurately modelling hierarchical evolutionary patterns of cerebrovasculature

Cerebrovascular abnormalities are critical indicators of stroke and neurodegenerative diseases like Alzheimer's disease (AD). Understanding the normal evolution of brain vessels is essential for detecting early deviations and enabling timely interventions. Here, for the first time, we proposed a pipeline exploring the joint evolution of cortical volumes (CVs) and arterial volumes (AVs) in a large cohort of 2841 individuals. Using advanced deep learning for vessel segmentation, we built normative models of CVs and AVs across spatially hierarchical brain regions. We found that while AVs generally decline with age, distinct trends appear in regions like the circle of Willis. Comparing healthy individuals with those affected by AD or stroke, we identified significant reductions in both CVs and AVs, wherein patients with AD showing the most severe impact. Our findings reveal gender-specific effects and provide critical insights into how these conditions alter brain structure, potentially guiding future clinical assessments and interventions.

Using Mean Arterial Pressure in Hypertension Diagnosis versus Using Either Systolic or Diastolic Blood Pressure Measurements

Hypertension is a severe and highly prevalent disease. It is considered a leading contributor to mortality worldwide. Diagnosis guidelines for hypertension use systolic and diastolic blood pressure (BP) together. Mean arterial pressure (MAP), which refers to the average of the arterial blood pressure through a single cardiac cycle, can be an alternative index that may capture the overall exposure of the person to a heightened pressure. A clinical hypothesis, however, suggests that in patients over 50 years old in age, systolic BP may be more predictive of adverse events, while in patients under 50 years old, diastolic BP may be slightly more predictive. In this study, we investigated the correlation between cerebrovascular changes, (impacted by hypertension), and MAP, systolic BP, and diastolic BP separately. Several experiments were conducted using real and synthetic magnetic resonance angiography (MRA) data, along with corresponding BP measurements. Each experiment employs the following methodology: First, MRA data were processed to remove noise, bias, or inhomogeneity. Second, the cerebrovasculature was delineated for MRA subjects using a 3D adaptive region growing connected components algorithm. Third, vascular features (changes in blood vessel’s diameters and tortuosity) that describe cerebrovascular alterations that occur prior to and during the development of hypertension were extracted. Finally, feature vectors were constructed, and data were classified using different classifiers, such as SVM, KNN, linear discriminant, and logistic regression, into either normotensives or hypertensives according to the cerebral vascular alterations and the BP measurements. The initial results showed that MAP would be more beneficial and accurate in identifying the cerebrovascular impact of hypertension (accuracy up to 95.2%) than just using either systolic BP (accuracy up to 89.3%) or diastolic BP (accuracy up to 88.9%). This result emphasizes the pathophysiological significance of MAP and supports prior views that this simple measure may be a superior index for the definition of hypertension and research on hypertension.

Attention-Assisted Adversarial Model for Cerebrovascular Segmentation in 3D TOF-MRA Volumes

Cerebrovascular segmentation in time-of-flight magnetic resonance angiography (TOF-MRA) volumes is essential for a variety of diagnostic and analytical applications. However, accurate cerebrovascular segmentation in 3D TOF-MRA is faced with multiple issues, including vast variations in cerebrovascular morphology and intensity, noisy background, and severe class imbalance between foreground cerebral vessels and background. In this work, a 3D adversarial network model called A-SegAN is proposed to segment cerebral vessels in TOF-MRA volumes. The proposed model is composed of a segmentation network A-SegS to predict segmentation maps, and a critic network A-SegC to discriminate predictions from ground truth. Based on this model, the aforementioned issues are addressed by the prevailing visual attention mechanism. First, A-SegS is incorporated with feature-attention blocks to filter out discriminative feature maps, though the cerebrovascular has varied appearances. Second, a hard-example-attention loss is exploited to boost the training of A-SegS on hard samples. Further, A-SegC is combined with an input-attention layer to attach importance to foreground cerebrovascular class. The proposed methods were evaluated on a self-constructed voxel-wise annotated cerebrovascular TOF-MRA segmentation dataset, and experimental results indicate that A-SegAN achieves competitive or better cerebrovascular segmentation results compared to other deep learning methods, effectively alleviating the above issues.

Segmentation of Infant Brain Using Nonnegative Matrix Factorization

This study develops an atlas-based automated framework for segmenting infants’ brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant’s brain at the isointense age (6–12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov–Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix factorization (NMF) and allocation to data clusters. Projecting an initial high-dimensional feature space onto a low-dimensional space of the significant fused features with the NMF allows for better separation of the goal structure and its background. The cluster centers in the latter space are determined at the training stage by the K-means clustering. In order to adapt to large infant brain inhomogeneities and segment the brain images more accurately, appearance descriptors of both the first-order and second-order are taken into account in the fused NMF feature space. Additionally, a second-order MGRF model is used to describe the appearance based on the voxel intensities and their pairwise spatial dependencies. An adaptive shape prior that is spatially variant is constructed from a training set of co-aligned images, forming an atlas database. Moreover, the spatial homogeneity of the shape is described with a spatially uniform 3D MGRF of the second-order for region labels. In vivo experiments on nine infant datasets showed promising results in terms of the accuracy, which was computed using three metrics: the 95-percentile modified Hausdorff distance (MHD), the Dice similarity coefficient (DSC), and the absolute volume difference (AVD). Both the quantitative and visual assessments confirm that integrating the proposed NMF-fused DTI feature and intensity MGRF models of visual appearance, the adaptive shape prior, and the shape homogeneity MGRF model is promising in segmenting the infant brain DTI.

Algorithms for segmenting cerebral time-of-flight magnetic resonance angiograms from volunteers and anemic patients

Purpose: To evaluate six cerebral arterial segmentation algorithms in a set of patients with a wide range of hemodynamic characteristics to determine real-world performance. Approach: Time-of-flight magnetic resonance angiograms were acquired from 33 subjects: normal controls ( N = 11 ), sickle cell disease ( N = 11 ), and non-sickle anemia ( N = 11 ) using a 3 Tesla Philips Achieva scanner. Six segmentation algorithms were tested: (1) Otsu's method, (2) K-means, (3) region growing, (4) active contours, (5) minimum cost path, and (6) U-net machine learning. Segmentation algorithms were tested with two region-selection methods: global, which selects the entire volume; and local, which iteratively tracks the arteries. Five slices were manually segmented from each patient by two readers. Agreement between manual and automatic segmentation was measured using Matthew's correlation coefficient (MCC). Results: Median algorithm segmentation times ranged from 0.1 to 172.9 s for a single angiogram versus 10 h for manual segmentation. Algorithms had inferior performance to inter-observer vessel-based ( p < 0.0001 , MCC = 0.65 ) and voxel-based ( p < 0.0001 , MCC = 0.73 ) measurements. There were significant differences between algorithms ( p < 0.0001 ) and between patients ( p < 0.0042 ). Post-hoc analyses indicated (1) local minimum cost path performed best with vessel-based ( p = 0.0261 , MCC = 0.50 ) and voxel-based ( p = 0.0131 , MCC = 0.66 ) analyses; and (2) higher vessel-based performance in non-sickle anemia ( p = 0.0002 ) and lower voxel-based performance in sickle cell ( p = 0.0422 ) compared with normal controls. All reported MCCs are medians. Conclusions: The best-performing algorithm (local minimum cost path, voxel-based) had 9.59% worse performance than inter-observer agreement but was 3 orders of magnitude faster. Automatic segmentation was non-inferior in patients with sickle cell disease and superior in non-sickle anemia.

A novel 3D segmentation approach for extracting retinal layers from optical coherence tomography images

PURPOSE

Accurate segmentation of retinal layers of the eye in 3D Optical Coherence Tomography (OCT) data provides relevant information for clinical diagnosis. This manuscript describes a 3D segmentation approach that uses an adaptive patient-specific retinal atlas, as well as an appearance model for 3D OCT data.

METHODS

To reconstruct the atlas of 3D retinal scan, the central area of the macula (macula mid-area) where the fovea could be clearly identified, was segmented initially. Markov Gibbs Random Field (MGRF) including intensity, spatial information, and shape of 12 retinal layers were used to segment the selected area of retinal fovea. A set of coregistered OCT scans that were gathered from 200 different individuals were used to build a 2D shape prior. This shape prior was adapted subsequently to the first order appearance and second order spatial interaction MGRF model. After segmenting the center of the macula "foveal area", the labels and appearances of the layers that were segmented were utilized to segment the adjacent slices. The final step was repeated recursively until a 3D OCT scan of the patient was segmented.

RESULTS

This approach was tested in 50 patients with normal and with ocular pathological conditions. The segmentation was compared to a manually segmented ground truth. The results were verified by clinical retinal experts. Dice Similarity Coefficient (DSC), 95% bidirectional modified Hausdorff Distance (HD), Unsigned Mean Surface Position Error (MSPE), and Average Volume Difference (AVD) metrics were used to quantify the performance of the proposed approach. The proposed approach was proved to be more accurate than the current state-of-the-art 3D OCT approaches.

CONCLUSIONS

The proposed approach has the advantage of segmenting all the 12 retinal layers rapidly and more accurately than current state-of-the-art 3D OCT approaches.

Quantitative Analysis of the Cerebral Vasculature on Magnetic Resonance Angiography

The arterial connections in the Circle of Willis are a central source of collateral blood flow and play an important role in pathologies such as stroke and mental illness. Analysis of the Circle of Willis and its variants can shed light on optimal methods of diagnosis, treatment planning, surgery, and quantification of outcomes. We developed an automated, standardized, objective, and high-throughput approach for categorizing and quantifying the Circle of Willis vascular anatomy using magnetic resonance angiography images. This automated algorithm for processing of MRA images isolates and automatically identifies key features of the cerebral vasculature such as branching of the internal intracranial internal carotid artery and the basilar artery. Subsequently, physical features of the segments of the anterior cerebral artery were acquired on a sample and intra-patient comparisons were made. We demonstrate the feasibility of using our approach to automatically classify important structures of the Circle of Willis and extract biomarkers from cerebrovasculature. Automated image analysis can provide clinically-relevant vascular features such as aplastic arteries, stenosis, aneurysms, and vessel caliper for endovascular procedures. The developed algorithm could facilitate clinical studies by supporting high-throughput automated analysis of the cerebral vasculature.

A novel computer-aided diagnosis system for the early detection of hypertension based on cerebrovascular alterations

•

3-D CNN segmentation succeeded in delineating cerebrovasculature accurately.

•

Segmentation approach is automatic and applicable on healthy/pathological vessels.

•

Blood flow variability challenge was addressed by processing MRA scans locally.

•

Proposed vascular features were efficient to quantify cerebral changes.

•

Proposed CAD system could help clinicians predict hypertension before its onset.

Hypertension is a leading cause of mortality in the USA. While simple tools such as the sphygmomanometer are widely used to diagnose hypertension, they could not predict the disease before its onset. Clinical studies suggest that alterations in the structure of human brains’ cerebrovasculature start to develop years before the onset of hypertension. In this research, we present a novel computer-aided diagnosis (CAD) system for the early detection of hypertension. The proposed CAD system analyzes magnetic resonance angiography (MRA) data of human brains to detect and track the cerebral vascular alterations and this is achieved using the following steps: i) MRA data are preprocessed to eliminate noise effects, correct the bias field effect, reduce the contrast inhomogeneity using the generalized Gauss-Markov random field (GGMRF) model, and normalize the MRA data, ii) the cerebral vascular tree of each MRA volume is segmented using a 3-D convolutional neural network (3D-CNN), iii) cerebral features in terms of diameters and tortuosity of blood vessels are estimated and used to construct feature vectors, iv) feature vectors are then used to train and test various artificial neural networks to classify data into two classes; normal and hypertensive. A balanced data set of 66 subjects were used to test the CAD system. Experimental results reported a classification accuracy of 90.9% which supports the efficacy of the CAD system components to accurately model and discriminate between normal and hypertensive subjects. Clinicians would benefit from the proposed CAD system to detect and track cerebral vascular alterations over time for people with high potential of developing hypertension and to prepare appropriate treatment plans to mitigate adverse events.

Computer aided detection of deep inferior epigastric perforators in computed tomography angiography scans

The deep inferior epigastric artery perforator (DIEAP) flap is the most common free flap used for breast reconstruction after a mastectomy. It makes use of the skin and fat of the lower abdomen to build a new breast mound either at the same time of the mastectomy or in a second surgery. This operation requires preoperative imaging studies to evaluate the branches - the perforators - that irrigate the tissue that will be used to reconstruct the breast mound. These branches will support tissue viability after the microsurgical ligation of the inferior epigastric vessels to the receptor vessels in the thorax. Usually through a computed tomography angiography (CTA), each perforator is manually identified and characterized by the imaging team, who will subsequently draw a map for the identification of the best vascular support for the reconstruction. In the current work we propose a semi-automatic methodology that aims at reducing the time and subjectivity inherent to the manual annotation. In 21 CTAs from patients proposed for breast reconstruction with DIEAP flaps, the subcutaneous region of each perforator was extracted, by means of a tracking procedure, whereas the intramuscular portion was detected through a minimum cost approach. Both were subsequently compared with the radiologist manual annotation. Results showed that the semi-automatic procedure was able to correctly detect the course of the DIEAPs with a minimum error (average error of 0.64 and 0.50 mm regarding the extraction of subcutaneous and intramuscular paths, respectively), taking little time to do so. The objective methodology is a promising tool in the automatic detection of perforators in CTA and can contribute to spare human resources and reduce subjectivity in the aforementioned task.

A Novel Framework for Early Detection of Hypertension using Magnetic Resonance Angiography

Hypertension is a leading mortality cause of 410,000 patients in USA. Cerebrovascular structural changes that occur as a result of chronically elevated cerebral perfusion pressure are hypothesized to precede the onset of systemic hypertension. A novel framework is presented in this manuscript to detect and quantify cerebrovascular changes (i.e. blood vessel diameters and tortuosity changes) using magnetic resonance angiography (MRA) data. The proposed framework consists of: 1) A novel adaptive segmentation algorithm to delineate large as well as small blood vessels locally using 3-D spatial information and appearance features of the cerebrovascular system; 2) Estimating the cumulative distribution function (CDF) of the 3-D distance map of the cerebrovascular system to quantify alterations in cerebral blood vessels' diameters; 3) Calculation of mean and Gaussian curvatures to quantify cerebrovascular tortuosity; and 4) Statistical and correlation analyses to identify the relationship between mean arterial pressure (MAP) and cerebral blood vessels' diameters and tortuosity alterations. The proposed framework was validated using MAP and MRA data collected from 15 patients over a 700-days period. The novel adaptive segmentation algorithm recorded a 92.23% Dice similarity coefficient (DSC), a 94.82% sensitivity, a 99.00% specificity, and a 10.00% absolute vessels volume difference (AVVD) in delineating cerebral blood vessels from surrounding tissues compared to the ground truth. Experiments demonstrated that MAP is inversely related to cerebral blood vessel diameters (p-value < 0.05) globally (over the whole brain) and locally (at circle of Willis and below). A statistically significant direct correlation (p-value < 0.05) was found between MAP and tortuosity (medians of Gaussian and mean curvatures, and average of mean curvature) globally and locally (at circle of Willis and below). Quantification of the cerebrovascular diameter and tortuosity changes may enable clinicians to predict elevated blood pressure before its onset and optimize medical treatment plans of pre-hypertension and hypertension.

Automated framework for accurate segmentation of leaf images for plant health assessment

Leaf segmentation is significantly important in assisting ecologists to automatically detect symptoms of disease and other stressors affecting trees. This paper employs state-of-the-art techniques in image processing to introduce an accurate framework for segmenting leaves and diseased leaf spots from images. The proposed framework integrates an appearance model that visually represents the current input image with the color prior information generated from RGB color images that were formerly saved in our database. Our framework consists of four main steps: (1) Enhancing the accuracy of the segmentation at minimum time by making use of contrast changes to automatically identify the region of interest (ROI) of the entire leaf, where the pixel-wise intensity relations are described by an electric field energy model. (2) Modeling the visual appearance of the input image using a linear combination of discrete Gaussians (LCDG) to predict the marginal probability distributions of the grayscale ROI main three classes. (3) Calculating the pixel-wise probabilities of these three classes for the color ROI based on the color prior information of database images that are segmented manually, where the current and prior pixel-wise probabilities are used to find the initial labels. (4) Refining the labels with the generalized Gauss-Markov random field model (GGMRF), which maintains the continuity. The proposed segmentation approach was applied to the leaves of mangrove trees in Abu Dhabi in the United Arab Emirates. Experimental validation showed high accuracy, with a Dice similarity coefficient 90% for distinguishing leaf spot from healthy leaf area.

Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI

OBJECTIVE

Early diagnosis of acute renal transplant rejection (ARTR) is critical for accurate treatment. Although the current gold standard, diagnostic technique is renal biopsy, it is not preferred due to its invasiveness, long recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection.

METHODS

This paper presents a computer-aided diagnostic (CAD) system for early ARTR detection using (3D + b-value) diffusion-weighted (DW) magnetic resonance imaging (MRI) data. The CAD process starts from kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The evolution is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and on-going kidney-background visual appearances. A B-spline-based three-dimensional data alignment is employed to handle local deviations due to breathing and heart beating. Then, empirical cumulative distribution functions of apparent diffusion coefficients of the segmented DW-MRI at different b-values are collected as discriminatory transplant status features. Finally, a deep-learning-based classifier with stacked nonnegative constrained autoencoders is employed to distinguish between rejected and nonrejected renal transplants.

RESULTS

In our initial "leave-one-subject-out" experiment on 100 subjects, [Formula: see text] of the subjects were correctly classified. The subsequent four-fold and ten-fold cross-validations gave the average accuracy of [Formula: see text] and [Formula: see text], respectively.

CONCLUSION

These results demonstrate the promise of this new CAD system to reliably diagnose renal transplant rejection.

SIGNIFICANCE

The technology presented here can significantly impact the quality of care of renal transplant patients since it has the potential to replace the gold standard in kidney diagnosis, biopsy.

3D kidney segmentation from abdominal diffusion MRI using an appearance-guided deformable boundary

A new technique for more accurate automatic segmentation of the kidney from its surrounding abdominal structures in diffusion-weighted magnetic resonance imaging (DW-MRI) is presented. This approach combines a new 3D probabilistic shape model of the kidney with a first-order appearance model and fourth-order spatial model of the diffusion-weighted signal intensity to guide the evolution of a 3D geometric deformable model. The probabilistic shape model was built from labeled training datasets to produce a spatially variant, independent random field of region labels. A Markov-Gibbs random field spatial model with up to fourth-order interactions was adequate to capture the inhomogeneity of renal tissues in the DW-MRI signal. A new analytical approach estimated the Gibbs potentials directly from the DW-MRI data to be segmented, in order that the segmentation procedure would be fully automatic. Finally, to better distinguish the kidney object from the surrounding tissues, marginal gray level distributions inside and outside of the deformable boundary were modeled with adaptive linear combinations of discrete Gaussians (first-order appearance model). The approach was tested on a cohort of 64 DW-MRI datasets with b-values ranging from 50 to 1000 s/mm2. The performance of the presented approach was evaluated using leave-one-subject-out cross validation and compared against three other well-known segmentation methods applied to the same DW-MRI data using the following evaluation metrics: 1) the Dice similarity coefficient (DSC); 2) the 95-percentile modified Hausdorff distance (MHD); and 3) the percentage kidney volume difference (PKVD). High performance of the new approach was confirmed by the high DSC (0.95±0.01), low MHD (3.9±0.76) mm, and low PKVD (9.5±2.2)% relative to manual segmentation by an MR expert (a board certified radiologist).

Classification of pressure ulcer tissues with 3D convolutional neural network

A 3D convolution neural network (CNN) of deep learning architecture is supplied with essential visual features to accurately classify and segment granulation, necrotic eschar, and slough tissues in pressure ulcer color images. After finding a region of interest (ROI), the features are extracted from both the original and convolved with a pre-selected Gaussian kernel 3D HSI images, combined with first-order models of current and prior visual appearance. The models approximate empirical marginal probability distributions of voxel-wise signals with linear combinations of discrete Gaussians (LCDG). The framework was trained and tested on 193 color pressure ulcer images. The classification accuracy and robustness were evaluated using the Dice similarity coefficient (DSC), the percentage area distance (PAD), and the area under the ROC curve (AUC). The obtained preliminary DSC of 92%, PAD of 13%, and AUC of 95% are promising. Graphical Abstract The Classification of Pressure Ulcer Tissues Based on 3D Convolutional Neural Network.

A fast stochastic framework for automatic MR brain images segmentation

This paper introduces a new framework for the segmentation of different brain structures (white matter, gray matter, and cerebrospinal fluid) from 3D MR brain images at different life stages. The proposed segmentation framework is based on a shape prior built using a subset of co-aligned training images that is adapted during the segmentation process based on first- and second-order visual appearance characteristics of MR images. These characteristics are described using voxel-wise image intensities and their spatial interaction features. To more accurately model the empirical grey level distribution of the brain signals, we use a linear combination of discrete Gaussians (LCDG) model having positive and negative components. To accurately account for the large inhomogeneity in infant MRIs, a higher-order Markov-Gibbs Random Field (MGRF) spatial interaction model that integrates third- and fourth- order families with a traditional second-order model is proposed. The proposed approach was tested and evaluated on 102 3D MR brain scans using three metrics: the Dice coefficient, the 95-percentile modified Hausdorff distance, and the absolute brain volume difference. Experimental results show better segmentation of MR brain images compared to current open source segmentation tools.

Accurate Lungs Segmentation on CT Chest Images by Adaptive Appearance-Guided Shape Modeling

To accurately segment pathological and healthy lungs for reliable computer-aided disease diagnostics, a stack of chest CT scans is modeled as a sample of a spatially inhomogeneous joint 3D Markov-Gibbs random field (MGRF) of voxel-wise lung and chest CT image signals (intensities). The proposed learnable MGRF integrates two visual appearance sub-models with an adaptive lung shape submodel. The first-order appearance submodel accounts for both the original CT image and its Gaussian scale space (GSS) filtered version to specify local and global signal properties, respectively. Each empirical marginal probability distribution of signals is closely approximated with a linear combination of discrete Gaussians (LCDG), containing two positive dominant and multiple sign-alternate subordinate DGs. The approximation is separated into two LCDGs to describe individually the lungs and their background, i.e., all other chest tissues. The second-order appearance submodel quantifies conditional pairwise intensity dependencies in the nearest voxel 26-neighborhood in both the original and GSS-filtered images. The shape submodel is built for a set of training data and is adapted during segmentation using both the lung and chest appearances. The accuracy of the proposed segmentation framework is quantitatively assessed using two public databases (ISBI VESSEL12 challenge and MICCAI LOLA11 challenge) and our own database with, respectively, 20, 55, and 30 CT images of various lung pathologies acquired with different scanners and protocols. Quantitative assessment of our framework in terms of Dice similarity coefficients, 95-percentile bidirectional Hausdorff distances, and percentage volume differences confirms the high accuracy of our model on both our database (98.4±1.0%, 2.2±1.0 mm, 0.42±0.10%) and the VESSEL12 database (99.0±0.5%, 2.1±1.6 mm, 0.39±0.20%), respectively. Similarly, the accuracy of our approach is further verified via a blind evaluation by the organizers of the LOLA11 competition, where an average overlap of 98.0% with the expert's segmentation is yielded on all 55 subjects with our framework being ranked first among all the state-of-the-art techniques compared.

Adaptive Kalman snake for semi-autonomous 3D vessel tracking

In this paper, we propose a robust semi-autonomous algorithm for 3D vessel segmentation and tracking based on an active contour model and a Kalman filter. For each computed tomography angiography (CTA) slice, we use the active contour model to segment the vessel boundary and the Kalman filter to track position and shape variations of the vessel boundary between slices. For successful segmentation via active contour, we select an adequate number of initial points from the contour of the first slice. The points are set manually by user input for the first slice. For the remaining slices, the initial contour position is estimated autonomously based on segmentation results of the previous slice. To obtain refined segmentation results, an adaptive control spacing algorithm is introduced into the active contour model. Moreover, a block search-based initial contour estimation procedure is proposed to ensure that the initial contour of each slice can be near the vessel boundary. Experiments were performed on synthetic and real chest CTA images. Compared with the well-known Chan-Vese (CV) model, the proposed algorithm exhibited better performance in segmentation and tracking. In particular, receiver operating characteristic analysis on the synthetic and real CTA images demonstrated the time efficiency and tracking robustness of the proposed model. In terms of computational time redundancy, processing time can be effectively reduced by approximately 20%.

Automatic localization of the fetal cerebellum on 3D ultrasound volumes

PURPOSE

Assessment of the fetal cerebellar volume on 3D ultrasound data sets is very important in the clinical evaluation of the fetal growth and health. However, the irregular shape of the cerebellum and the strong artifacts of ultrasound images complicate the segmentation without manual intervention. In this paper, the authors propose an approach to locate the cerebellum automatically, which is considered as a prework of the segmentation.

METHODS

The authors present a weighted Hough transform and a constrained randomized Hough transform to detect the fetal brain midline and the skull, respectively. By combining the location information of these two structures with local image features, a constrained probabilistic boosting tree is then proposed to search the cerebellum.

RESULTS

This algorithm was tested on ultrasound volumes of the fetal head with the gestational age ranging from 20 to 33 weeks. Compared with manual measurements, this algorithm obtained a satisfactory performance with the mean Dice similarity coefficient of 0.92 and the average processing time of 0.75 s per case.

CONCLUSIONS

The results demonstrate that the proposed method is an automatic, fast, and accurate tool for searching the fetal cerebellum on ultrasound volumes.

A comprehensive non-invasive framework for automated evaluation of acute renal transplant rejection using DCE-MRI

The objective was to develop a novel and automated comprehensive framework for the non-invasive identification and classification of kidney non-rejection and acute rejection transplants using 2D dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The proposed approach consists of four steps. First, kidney objects are segmented from the surrounding structures with a geometric deformable model. Second, a non-rigid registration approach is employed to account for any local kidney deformation. In the third step, the cortex of the kidney is extracted in order to determine dynamic agent delivery, since it is the cortex that is primarily affected by the perfusion deficits that underlie the pathophysiology of acute rejection. Finally, we use an analytical function-based model to fit the dynamic contrast agent kinetic curves in order to determine possible rejection candidates. Five features that map the data from the original data space to the feature space are chosen with a k-nearest-neighbor (KNN) classifier to distinguish between acute rejection and non-rejection transplants. Our study includes 50 transplant patients divided into two groups: 27 patients with stable kidney function and the remainder with impaired kidney function. All of the patients underwent DCE-MRI, while the patients in the impaired group also underwent ultrasound-guided fine needle biopsy. We extracted the kidney objects and the renal cortex from DCE-MRI for accurate medical evaluation with an accuracy of 0.97 ± 0.02 and 0.90 ± 0.03, respectively, using the Dice similarity metric. In a cohort of 50 participants, our framework classified all cases correctly (100%) as rejection or non-rejection transplant candidates, which is comparable to the gold standard of biopsy but without the associated deleterious side-effects. Both the 95% confidence interval (CI) statistic and the receiver operating characteristic (ROC) analysis document the ability to separate rejection and non-rejection groups. The average plateau (AP) signal magnitude and the gamma-variate model functional parameter α have the best individual discriminating characteristics.

Interactive 3D Analysis of Blood Vessel Trees and Collateral Vessel Volumes in Magnetic Resonance Angiograms in the Mouse Ischemic Hindlimb Model

The quantitative analysis of blood vessel volumes from magnetic resonance angiograms (MRA) or μCT images is difficult and time-consuming. This fact, when combined with a study that involves multiple scans of multiple subjects, can represent a significant portion of research time. In order to enhance analysis options and to provide an automated and fast analysis method, we developed a software plugin for the ImageJ and Fiji image processing frameworks that enables the quick and reproducible volume quantification of blood vessel segments. The novel plugin named Volume Calculator (VolCal), accepts any binary (thresholded) image and produces a three-dimensional schematic representation of the vasculature that can be directly manipulated by the investigator.

Using MRAs of the mouse hindlimb ischemia model, we demonstrate quick and reproducible blood vessel volume calculations with 95 – 98% accuracy. In clinical settings this software may enhance image interpretation and the speed of data analysis and thus enhance intervention decisions for example in peripheral vascular disease or aneurysms.

In summary, we provide a novel, fast and interactive quantification of blood vessel volumes for single blood vessels or sets of vessel segments with particular focus on collateral formation after an ischemic insult.

Dynamic contrast-enhanced MRI-based early detection of acute renal transplant rejection

A novel framework for the classification of acute rejection versus nonrejection status of renal transplants from 2-D dynamic contrast-enhanced magnetic resonance imaging is proposed. The framework consists of four steps. First, kidney objects are segmented from adjacent structures with a level set deformable boundary guided by a stochastic speed function that accounts for a fourth-order Markov-Gibbs random field model of the kidney/background shape and appearance. Second, a Laplace-based nonrigid registration approach is used to account for local deformations caused by physiological effects. Namely, the target kidney object is deformed over closed, equispaced contours (iso-contours) to closely match the reference object. Next, the cortex is segmented as it is the functional kidney unit that is most affected by rejection. To characterize rejection, perfusion is estimated from contrast agent kinetics using empirical indexes, namely, the transient phase indexes (peak signal intensity, time-to-peak, and initial up-slope), and a steady-phase index defined as the average signal change during the slowly varying tissue phase of agent transit. We used a kn-nearest neighbor classifier to distinguish between acute rejection and nonrejection. Performance of our method was evaluated using the receiver operating characteristics (ROC). Experimental results in 50 subjects, using a combinatoric kn-classifier, correctly classified 92% of training subjects, 100% of the test subjects, and yielded an area under the ROC curve that approached the ideal value. Our proposed framework thus holds promise as a reliable noninvasive diagnostic tool.

Computer-aided diagnosis systems for lung cancer: challenges and methodologies

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

A novel approach for global lung registration using 3D Markov-Gibbs appearance model

A new approach to align 3D CT data of a segmented lung object with a given prototype (reference lung object) using an affine transformation is proposed. Visual appearance of the lung from CT images, after equalizing their signals, is modeled with a new 3D Markov-Gibbs random field (MGRF) with pairwise interaction model. Similarity to the prototype is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of voxel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by a gradient search. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms.