Martin Rajchl

MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

Active Cardiac Sarcoidosis First Clinical Experience of Simultaneous Positron Emission Tomography- Magnetic Resonance Imaging for the Diagnosis of Cardiac Disease

The hybridization of positron emission tomography (PET) and magnetic resonance imaging (MRI) within a single imaging bore is a major advance in noninvasive imaging. Intrinsic coregistration of metabolic/molecular probe imaging with morphological, functional, and tissue imaging presents new opportunities for disease characterization. Sarcoidosis is a multisystem inflammatory disease hallmarked by inflammation, noncaseating granuloma formation, and organ dysfunction. Cardiac involvement accounts for up to 25% of disease-related mortality and is conventionally diagnosed with the Japanese Ministry criteria.1 However, studies using cardiac PET and MRI suggest a robust capacity to identify cardiac involvement2,3—PET through identification of active inflammation and MRI through identification of mature fibrosis or scar. In this report, we describe the first clinical use of simultaneous PET-MRI to assist in the diagnosis of cardiac disease: active cardiac sarcoidosis. A 72-year-old woman was referred with a 12-month history of increasing shortness of breath and intermittent chest pain. A coronary angiogram and echocardiogram showed normal coronary arteries but an ejection fraction of 35%. Her history was significant for inflammatory polyarthritis, treated with etanercept and hydroxycholoquine, and biopsy of an enlarged scalene lymph node showing noncaseating granulomas. …

Prostate Segmentation: An Efficient Convex Optimization Approach With Axial Symmetry Using 3-D TRUS and MR Images

We propose a novel global optimization-based approach to segmentation of 3-D prostate transrectal ultrasound (TRUS) and T2 weighted magnetic resonance (MR) images, enforcing inherent axial symmetry of prostate shapes to simultaneously adjust a series of 2-D slice-wise segmentations in a “global” 3-D sense. We show that the introduced challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In this regard, we propose a novel coherent continuous max-flow model (CCMFM), which derives a new and efficient duality-based algorithm, leading to a GPU-based implementation to achieve high computational speeds. Experiments with 25 3-D TRUS images and 30 3-D T2w MR images from our dataset, and 50 3-D T2w MR images from a public dataset, demonstrate that the proposed approach can segment a 3-D prostate TRUS/MR image within 5-6 s including 4-5 s for initialization, yielding a mean Dice similarity coefficient of 93.2% ± 2.0% for 3-D TRUS images and 88.5% ± 3.5% for 3-D MR images. The proposed method also yields relatively low intra- and inter-observer variability introduced by user manual initialization, suggesting a high reproducibility, independent of observers.

Left ventricle segmentation in MRI via convex relaxed distribution matching.

A fundamental step in the diagnosis of cardiovascular diseases, automatic left ventricle (LV) segmentation in cardiac magnetic resonance images (MRIs) is still acknowledged to be a difficult problem. Most of the existing algorithms require either extensive training or intensive user inputs. This study investigates fast detection of the left ventricle (LV) endo- and epicardium surfaces in cardiac MRI via convex relaxation and distribution matching. The algorithm requires a single subject for training and a very simple user input, which amounts to a single point (mouse click) per target region (cavity or myocardium). It seeks cavity and myocardium regions within each 3D phase by optimizing two functionals, each containing two distribution-matching constraints: (1) a distance-based shape prior and (2) an intensity prior. Based on a global measure of similarity between distributions, the shape prior is intrinsically invariant with respect to translation and rotation. We further introduce a scale variable from which we derive a fixed-point equation (FPE), thereby achieving scale-invariance with only few fast computations. The proposed algorithm relaxes the need for costly pose estimation (or registration) procedures and large training sets, and can tolerate shape deformations, unlike template (or atlas) based priors. Our formulation leads to a challenging problem, which is not directly amenable to convex-optimization techniques. For each functional, we split the problem into a sequence of sub-problems, each of which can be solved exactly and globally via a convex relaxation and the augmented Lagrangian method. Unlike related graph-cut approaches, the proposed convex-relaxation solution can be parallelized to reduce substantially the computational time for 3D domains (or higher), extends directly to high dimensions, and does not have the grid-bias problem. Our parallelized implementation on a graphics processing unit (GPU) demonstrates that the proposed algorithm requires about 3.87 s for a typical cardiac MRI volume, a speed-up of about five times compared to a standard implementation. We report a performance evaluation over 400 volumes acquired from 20 subjects, which shows that the obtained 3D surfaces correlate with independent manual delineations. We further demonstrate experimentally that (1) the performance of the algorithm is not significantly affected by the choice of the training subject and (2) the shape description we use does not change significantly from one subject to another. These results support the fact that a single subject is sufficient for training the proposed algorithm.

Interactive Hierarchical-Flow Segmentation of Scar Tissue From Late-Enhancement Cardiac MR Images

We propose a novel multi-region image segmentation approach to extract myocardial scar tissue from 3-D whole-heart cardiac late-enhancement magnetic resonance images in an interactive manner. For this purpose, we developed a graphical user interface to initialize a fast max-flow-based segmentation algorithm and segment scar accurately with progressive interaction. We propose a partially-ordered Potts (POP) model to multi-region segmentation to properly encode the known spatial consistency of cardiac regions. Its generalization introduces a custom label/region order constraint to Potts model to multi-region segmentation. The combinatorial optimization problem associated with the proposed POP model is solved by means of convex relaxation, for which a novel multi-level continuous max-flow formulation, i.e., the hierarchical continuous max-flow (HMF) model, is proposed and studied. We demonstrate that the proposed HMF model is dual or equivalent to the convex relaxed POP model and introduces a new and efficient hierarchical continuous max-flow based algorithm by modern convex optimization theory. In practice, the introduced hierarchical continuous max-flow based algorithm can be implemented on the parallel GPU to achieve significant acceleration in numerics. Experiments are performed in 50 whole heart 3-D LE datasets, 35 with left-ventricular and 15 with right-ventricular scar. The experimental results are compared to full-width-at-half-maximum and Signal-threshold to reference-mean methods using manual expert myocardial segmentations and operator variabilities and the effect of user interaction are assessed. The results indicate a substantial reduction in image processing time with robust accuracy for detection of myocardial scar. This is achieved without the need for additional region constraints and using a single optimization procedure, substantially reducing the potential for error.

3-D Carotid Multi-Region MRI Segmentation by Globally Optimal Evolution of Coupled Surfaces

In this paper, we propose a novel global optimization based 3-D multi-region segmentation algorithm for T1-weighted black-blood carotid magnetic resonance (MR) images. The proposed algorithm partitions a 3-D carotid MR image into three regions: wall, lumen, and background. The algorithm performs such partitioning by simultaneously evolving two coupled 3-D surfaces of carotid artery adventitia boundary (AB) and lumen-intima boundary (LIB) while preserving their anatomical inter-surface consistency such that the LIB is always located within the AB. In particular, we show that the proposed algorithm results in a fully time implicit scheme that propagates the two linearly ordered surfaces of the AB and LIB to their globally optimal positions during each discrete time frame by convex relaxation. In this regard, we introduce the continuous max-flow model and prove its duality/equivalence to the convex relaxed optimization problem with respect to each evolution step. We then propose a fully parallelized continuous max-flow-based algorithm, which can be readily implemented on a GPU to achieve high computational efficiency. Extensive experiments, with four users using 12 3T MR and 26 1.5T MR images, demonstrate that the proposed algorithm yields high accuracy and low operator variability in computing vessel wall volume. In addition, we show the algorithm outperforms previous methods in terms of high computational efficiency and robustness with fewer user interactions.

Fast interactive multi-region cardiac segmentation with linearly ordered labels

We present a novel and fast interactive approach to multi-modality cardiac image segmentation, which employs the linearly ordered surfaces as an additional constraint. We show using such a geometrical constraint helps to significantly reduce user interaction and improve the accuracy of segmentation results at the same time. We solve the proposed multiregion segmentation problem with the order constraints by means of convex optimization, resulting in a fast and reliable flow maximization approach which implicitly embeds the linear order prior without introducing extra computation load. In this regard, a new fully parallelized continuous max-flow algorithm is proposed and implemented using GPGPU to segment a 3D volume within one second. We demonstrate our results over pathological trans-esophageal echocardiogram, cardiac CT and delayed enhancement MRI data sets.

Accuracy and reproducibility of semi-automated late gadolinium enhancement quantification techniques in patients with hypertrophic cardiomyopathy

BackgroundThe presence and extent of late gadolinium enhancement (LGE) has been associated with adverse events in patients with hypertrophic cardiomyopathy (HCM). Signal intensity (SI) threshold techniques are routinely employed for quantification; Full-Width at Half-Maximum (FWHM) techniques are suggested to provide greater reproducibility than Signal Threshold versus Reference Mean (STRM) techniques, however the accuracy of these approaches versus the manual assignment of optimal SI thresholds has not been studied. In this study, we compared all known semi-automated LGE quantification techniques for accuracy and reproducibility among patients with HCM.MethodsSeventy-six HCM patients (51 male, age 54 ±13 years) were studied. Total LGE volume was quantified using 7 semi-automated techniques and compared to expert manual adjustment of the SI threshold to achieve optimal segmentation. Techniques tested included STRM based thresholds of >2, 3, 4, 5 and 6 SD above mean SI of reference myocardium, the FWHM technique, and the Otsu-auto-threshold (OAT) technique. The SI threshold chosen by each technique was recorded for all slices. Bland-Altman analysis and intra-class correlation coefficients (ICC) were reported for each semi-automated technique versus expert, manually adjusted LGE segmentation. Intra- and inter-observer reproducibility assessments were also performed.ResultsFifty-two of 76 (68%) patients showed LGE on a total of 202 slices. For accuracy, the STRM >3SD technique showed the greatest agreement with manual segmentation (ICC =0.97, mean difference and 95% limits of agreement =1.6 ± 10.7 g) while STRM >6SD, >5SD, 4SD and FWHM techniques systematically underestimated total LGE volume. Slice based analysis of selected SI thresholds similarly showed the STRM >3SD threshold to most closely approximate manually adjusted SI thresholds (ICC =0.88). For reproducibility, the intra- and inter-observer reproducibility of the >3SD threshold demonstrated an acceptable mean difference and 95% limits of agreement of -0.5 ± 6.8 g and -0.9 ± 5.6 g, respectively.ConclusionsFWHM segmentation provides superior reproducibility, however systematically underestimates total LGE volume compared to manual segmentation in patients with HCM. The STRM >3SD technique provides the greatest accuracy while retaining acceptable reproducibility and may therefore be a preferred approach for LGE quantification in this population.

Efficient Convex Optimization Approach to 3D Non-rigid MR-TRUS Registration

In this study, we propose an efficient non-rigid MR-TRUS deformable registration method to improve the accuracy of targeting suspicious locations during a 3D ultrasound (US) guided prostate biopsy. The proposed deformable registration approach employs the multi-channel modality independent neighbourhood descriptor (MIND) as the local similarity feature across the two modalities of MR and TRUS, and a novel and efficient duality-based convex optimization based algorithmic scheme is introduced to extract the deformations which align the two MIND descriptors. The registration accuracy was evaluated using 10 patient images by measuring the TRE of manually identified corresponding intrinsic fiducials in the whole gland and peripheral zone, and performance metrics (DSC, MAD and MAXD) for the apex, mid-gland and base of the prostate were also calculated by comparing two manually segmented prostate surfaces in the registered 3D MR and TRUS images. Experimental results show that the proposed method yielded an overall mean TRE of 1.74 mm, which is favorably comparable to a clinical requirement for an error of less than 2.5 mm.

Dual optimization based prostate zonal segmentation in 3D MR images

Efficient and accurate segmentation of the prostate and two of its clinically meaningful sub-regions: the central gland (CG) and peripheral zone (PZ), from 3D MR images, is of great interest in image-guided prostate interventions and diagnosis of prostate cancer. In this work, a novel multi-region segmentation approach is proposed to simultaneously segment the prostate and its two major sub-regions from only a single 3D T2-weighted (T2w) MR image, which makes use of the prior spatial region consistency and incorporates a customized prostate appearance model into the segmentation task. The formulated challenging combinatorial optimization problem is solved by means of convex relaxation, for which a novel spatially continuous max-flow model is introduced as the dual optimization formulation to the studied convex relaxed optimization problem with region consistency constraints. The proposed continuous max-flow model derives an efficient duality-based algorithm that enjoys numerical advantages and can be easily implemented on GPUs. The proposed approach was validated using 18 3D prostate T2w MR images with a body-coil and 25 images with an endo-rectal coil. Experimental results demonstrate that the proposed method is capable of efficiently and accurately extracting both the prostate zones: CG and PZ, and the whole prostate gland from the input 3D prostate MR images, with a mean Dice similarity coefficient (DSC) of 89.3±3.2% for the whole gland (WG), 82.2±3.0% for the CG, and 69.1±6.9% for the PZ in 3D body-coil MR images; 89.2±3.3% for the WG, 83.0±2.4% for the CG, and 70.0±6.5% for the PZ in 3D endo-rectal coil MR images. In addition, the experiments of intra- and inter-observer variability introduced by user initialization indicate a good reproducibility of the proposed approach in terms of volume difference (VD) and coefficient-of-variation (CV) of DSC.