Adriënne M. Mendrik

Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2-weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86, and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2-weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86, and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.

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.

TIPS bilateral noise reduction in 4D CT perfusion scans produces high-quality cerebral blood flow maps

Cerebral computed tomography perfusion (CTP) scans are acquired to detect areas of abnormal perfusion in patients with cerebrovascular diseases. These 4D CTP scans consist of multiple sequential 3D CT scans over time. Therefore, to reduce radiation exposure to the patient, the amount of x-ray radiation that can be used per sequential scan is limited, which results in a high level of noise. To detect areas of abnormal perfusion, perfusion parameters are derived from the CTP data, such as the cerebral blood flow (CBF). Algorithms to determine perfusion parameters, especially singular value decomposition, are very sensitive to noise. Therefore, noise reduction is an important preprocessing step for CTP analysis. In this paper, we propose a time-intensity profile similarity (TIPS) bilateral filter to reduce noise in 4D CTP scans, while preserving the time-intensity profiles (fourth dimension) that are essential for determining the perfusion parameters. The proposed TIPS bilateral filter is compared to standard Gaussian filtering, and 4D and 3D (applied separately to each sequential scan) bilateral filtering on both phantom and patient data. Results on the phantom data show that the TIPS bilateral filter is best able to approach the ground truth (noise-free phantom), compared to the other filtering methods (lowest root mean square error). An observer study is performed using CBF maps derived from fifteen CTP scans of acute stroke patients filtered with standard Gaussian, 3D, 4D and TIPS bilateral filtering. These CBF maps were blindly presented to two observers that indicated which map they preferred for (1) gray/white matter differentiation, (2) detectability of infarcted area and (3) overall image quality. Based on these results, the TIPS bilateral filter ranked best and its CBF maps were scored to have the best overall image quality in 100% of the cases by both observers. Furthermore, quantitative CBF and cerebral blood volume values in both the phantom and the patient data showed that the TIPS bilateral filter resulted in realistic mean values with a smaller standard deviation than the other evaluated filters and higher contrast-to-noise ratios. Therefore, applying the proposed TIPS bilateral filtering method to 4D CTP data produces higher quality CBF maps than applying the standard Gaussian, 3D bilateral or 4D bilateral filter. Furthermore, the TIPS bilateral filter is computationally faster than both the 3D and 4D bilateral filters.

Noise Reduction in Computed Tomography Scans Using 3-D Anisotropic Hybrid Diffusion With Continuous Switch

Noise filtering techniques that maintain image contrast while decreasing image noise have the potential to optimize the quality of computed tomography (CT) images acquired at reduced radiation dose. In this paper, a hybrid diffusion filter with continuous switch (HDCS) is introduced, which exploits the benefits of three-dimensional edge-enhancing diffusion (EED) and coherence-enhancing diffusion (CED). Noise is filtered, while edges, tubular structures, and small spherical structures are preserved. From ten high dose thorax CT scans, acquired at clinical doses, ultra low dose ( 15 mAs ) scans were simulated and used to evaluate and compare HDCS to other diffusion filters, such as regularized Perona-Malik diffusion and EED. Quantitative results show that the HDCS filter outperforms the other filters in restoring the high dose CT scan from the corresponding simulated low dose scan. A qualitative evaluation was performed on filtered real low dose CT thorax scans. An expert observer scored artifacts as well as fine structures and was asked to choose one of three scans (two filtered (blinded), one unfiltered) for three different settings (trachea, lung, and mediastinal). Overall, the HDCS filtered scan was chosen most often.

Timing-Invariant Reconstruction for Deriving High-Quality CT Angiographic Data from Cerebral CT Perfusion Data

PURPOSE To suggest a simple and robust technique used to reconstruct high-quality computed tomographic (CT) angiographic images from CT perfusion data and to compare it with currently used CT angiography techniques. MATERIALS AND METHODS Institutional review board approval was waived for this retrospective study, which included 25 consecutive patients who had had a stroke. Temporal maximum intensity projection (tMIP) CT angiographic images were created by using prior temporal filtering as a timing-insensitive technique to produce CT angiographic images from CT perfusion data. The temporal filter strength was optimized to gain maximal contrast-to-noise ratios (CNRs) in the circle of Willis. The resulting timing-invariant (TI) CT angiography was compared with standard helical CT angiography, the arterial phase of dynamic CT angiography, and nonfiltered tMIP CT angiography. Vascular contrast, image noise, and CNR were measured. Four experienced observers scored all images for vascular noise, vascular contour, detail of small and medium arteries, venous superimposition, and overall image quality in a blinded side-by-side comparison. Measurements were compared with a paired t test; P ≤ .05 indicated a significant difference. RESULTS On average, optimized temporal filtering in TI CT angiography increased CNR by 18% and decreased image noise by 18% at the expense of a decrease in vascular contrast of 3% when compared with nonfiltered tMIP CT angiography. CNR, image noise, vascular noise, vascular contour, detail visibility of small and medium arteries, and overall image quality of TI CT angiograms were superior to those of standard CT angiography, tMIP CT angiography, and the arterial phase of dynamic CT angiography at a vascular contrast that was similar to that of standard CT angiography. Venous superimposition was similar for all techniques. Image quality of the arterial phase of dynamic CT angiography was rated inferior to that of standard CT angiography. CONCLUSION TI CT angiographic images constructed by using temporally filtered tMIP CT angiographic data have excellent image quality that is superior to that achieved with currently used techniques, but they suffer from modest venous superimposition.

Automatic segmentation of intracranial arteries and veins in four-dimensional cerebral CT perfusion scans

PURPOSE CT angiography (CTA) scans are the current standard for vascular analysis of patients with cerebrovascular diseases, such as acute stroke and subarachnoid hemorrhage. An additional CT perfusion (CTP) scan is acquired of these patients to assess the perfusion of the cerebral tissue. The aim of this study is to extend the diagnostic yield of the CTP scans to also include vascular information. METHODS CTP scans are acquired by injecting contrast material and repeatedly scanning the head over time. Therefore, time-intensity profiles are available for each voxel in the scanned volume, resulting in a 4D dataset. These profiles can be utilized to differentiate not only between vessels and background but also between arteries and veins. In this article, a fully automatic method is proposed for the segmentation of the intracranial arteries and veins from 4D cerebral CTP scans. Furthermore, a vessel enhanced volume is presented, in which the vasculature is highlighted and background structures are suppressed. Combining this volume with the artery/vein segmentation results in an arteriogram and a venogram, which could serve as additional means for vascular analysis in patients with cerebrovascular diseases. The artery/vein segmentation is quantitatively evaluated by comparing the results to manual segmentations by two expert observers. RESULTS Results (paired two-tailed t-test) show that the accuracies of the proposed artery/vein labeling are not significantly different from the accuracies of the expert observer manual labeling (ground truth). Moreover, sensitivity and specificity of the proposed artery/vein labeling, relative to both expert observer ground truths, were similar to the sensitivity and specificity of the expert observer labeling compared to each other. CONCLUSIONS The proposed method for artery/vein segmentation is shown to be very accurate for arteries and veins with normal perfusion. Combining the artery/vein segmentation with the vessel enhanced volume produces an arteriogram and a venogram, which have the potential to extend the diagnostic yield of CTP scans and replace the additional CTA scan, but could also be helpful to radiologists in addition to the CTA scan.

Robustness of automated methods for brain volume measurements across different MRI field strengths

Introduction Pooling of multicenter brain imaging data is a trend in studies on ageing related brain diseases. This poses challenges to MR-based brain segmentation. The performance across different field strengths of three widely used automated methods for brain volume measurements was assessed in the present study. Methods Ten subjects (mean age: 64 years) were scanned on 1.5T and 3T MRI on the same day. We determined robustness across field strength (i.e., whether measured volumes between 3T and 1.5T scans in the same subjects were similar) for SPM12, Freesurfer 5.3.0 and FSL 5.0.7. As a frame of reference, 3T MRI scans from 20 additional subjects (mean age: 71 years) were segmented manually to determine accuracy of the methods (i.e., whether measured volumes corresponded with expert-defined volumes). Results Total brain volume (TBV) measurements were robust across field strength for Freesurfer and FSL (mean absolute difference as % of mean volume ≤ 1%), but less so for SPM (4%). Gray matter (GM) and white matter (WM) volume measurements were robust for Freesurfer (1%; 2%) and FSL (2%; 3%) but less so for SPM (5%; 4%). For intracranial volume (ICV), SPM was more robust (2%) than FSL (3%) and Freesurfer (9%). TBV measurements were accurate for SPM and FSL, but less so for Freesurfer. For GM volume, SPM was accurate, but accuracy was lower for Freesurfer and FSL. For WM volume, Freesurfer was accurate, but SPM and FSL were less accurate. For ICV, FSL was accurate, while SPM and Freesurfer were less accurate. Conclusion Brain volumes and ICV could be measured quite robustly in scans acquired at different field strengths, but performance of the methods varied depending on the assessed compartment (e.g., TBV or ICV). Selection of an appropriate method in multicenter brain imaging studies therefore depends on the compartment of interest.

Image Denoising with k-nearest Neighbor and Support Vector Regression

Denoising is an important application of image processing, especially for medical image data. These images tend to be very noisy when a low radiation dose, less harmful to the patient, is used for acquisition. For computed tomography (CT) data, it is possible to simulate realistic low dose images from the raw scanner data. We use this data to construct a supervised denoising system, that learns an optimal mapping from input features to denoised voxel values. As input features we use several general filters and the output of existing standard noise reduction filters, notably non-linear diffusion schemes. After feature selection, these are mapped to the denoised values by k-nearest neighbor and support vector regression. The resulting regression denoising systems are shown to perform significantly better than non-linear diffusion schemes, Gaussian smoothing and median filtering in experiments on CT chest scans

Automatic brain tissue segmentation in MR images using Random Forests and Conditional Random Fields.

BACKGROUND The segmentation of brain tissue into cerebrospinal fluid, gray matter, and white matter in magnetic resonance imaging scans is an important procedure to extract regions of interest for quantitative analysis and disease assessment. Manual segmentation requires skilled experts, being a laborious and time-consuming task; therefore, reliable and robust automatic segmentation methods are necessary. NEW METHOD We propose a segmentation framework based on a Conditional Random Field for brain tissue segmentation, with a Random Forest encoding the likelihood function. The features include intensities, gradients, probability maps, and locations. Additionally, skull stripping is critical for achieving an accurate segmentation; thus, after extracting the brain we propose to refine its boundary during segmentation. RESULTS The proposed framework was evaluated on the MR Brain Image Segmentation Challenge and the Internet Brain Segmentation Repository databases. The segmentations of brain tissues obtained with the proposed algorithm were competitive both in normal and diseased subjects. The skull stripping refinement significantly improved the results, when comparing against no refinement. COMPARISON WITH EXISTING METHODS In the MR Brain Image Segmentation Challenge database, the results were competitive when comparing with top methods. In the Internet Brain Segmentation Repository database, the proposed approach outperformed other well-established algorithms. CONCLUSIONS The combination of a Random Forest and Conditional Random Field for brain tissue segmentation performed well for normal and diseased subjects. Additionally, refinement of the skull stripping at segmentation time is feasible in learning-based methods and significantly improves the segmentation of cerebrospinal fluid and intracranial volume.

Improved Arterial Visualization in Cerebral CT Perfusion–Derived Arteriograms Compared with Standard CT Angiography: A Visual Assessment Study

BACKGROUND AND PURPOSE: Invasive cerebral DSA has largely been replaced by CTA, which is noninvasive but has a compromised arterial view due to superimposed bone and veins. The purpose of this study was to evaluate whether arterial visualization in CTPa is superior to standard CTA, which would eliminate the need for an additional CTA scan to assess arterial diseases and therefore reduce radiation dose. MATERIALS AND METHODS: In this study, we included 24 patients with subarachnoid hemorrhage for whom CTA and CTP were available. Arterial quality and presence of superimposed veins and bone in CTPa were compared with CTA and scored by 2 radiologists by using a VAS (0%–100%). Average VAS scores were determined and VAS scores per patient were converted to a 10-point NRS. Arterial visualization was considered to be improved when the highest rate (NRS 10, VAS > 90%) was scored for arterial quality, and the lowest rate (NRS 1, VAS < 10%), for the presence of superimposed veins and bone. A sign test with continuity correction was used to test whether the number of cases with these rates was significant. RESULTS: Average VAS scores in the proximal area were 94% (arterial quality), 4% (presence of bone), and 7% (presence of veins). In this area, the sign test showed that a significant number of cases scored NRS 10 for arterial quality (P < .02) and NRS 1 for the presence of superimposed veins and bone (P < .01). CONCLUSIONS: Cerebral CTPa shows improved arterial visualization in the proximal area compared with CTA, with similar arterial quality but no superimposed bone and veins.