Ventricular volume and its progression are known to be linked to several brain diseases such as dementia and schizophrenia. Therefore accurate measurement of ventricle volume is vital for longitudinal studies on these disorders, making automated ventricle segmentation algorithms desirable. In the past few years, deep neural networks have shown to outperform the classical models in many imaging domains. However, the success of deep networks is dependent on manually labeled data sets, which are expensive to acquire especially for higher dimensional data in the medical domain. In this work, we show that deep neural networks can be trained on muchcheaper-to-acquire pseudo-labels (e.g., generated by other automated less accurate methods) and still produce more accurate segmentations compared to the quality of the labels. To show this, we use noisy segmentation labels generated by a conventional region growing algorithm to train a deep network for lateral ventricle segmentation. Then on a large manually annotated test set, we show that the network significantly outperforms the conventional region growing algorithm which was used to produce the training labels for the network. Our experiments report a Dice Similarity Coefficient (DSC) of 0.874 for the trained network compared to 0.754 for the conventional region growing algorithm (p < 0.001).
The number and location of cerebral microbleeds (CMBs) in patients with traumatic brain injury (TBI) is
important to determine the severity of trauma and may hold prognostic value for patient outcome. However,
manual assessment is subjective and time-consuming due to the resemblance of CMBs to blood vessels, the
possible presence of imaging artifacts, and the typical heterogeneity of trauma imaging data. In this work, we
present a computer aided detection system based on 3D convolutional neural networks for detecting CMBs in 3D
susceptibility weighted images. Network architectures with varying depth were evaluated. Data augmentation
techniques were employed to improve the networks’ generalization ability and selective sampling was implemented
to handle class imbalance. The predictions of the models were clustered using a connected component analysis.
The system was trained on ten annotated scans and evaluated on an independent test set of eight scans. Despite
this limited data set, the system reached a sensitivity of 0.87 at 16.75 false positives per scan (2.5 false positives
per CMB), outperforming related work on CMB detection in TBI patients.
The assessment of the presence of intracranial hemorrhage is a crucial step in the work-up of patients requiring emergency care. Fast and accurate detection of intracranial hemorrhage can aid treating physicians by not only expediting and guiding diagnosis, but also supporting choices for secondary imaging, treatment and intervention. However, the automatic detection of intracranial hemorrhage is complicated by the variation in appearance on non-contrast CT images as a result of differences in etiology and location. We propose a method using a convolutional neural network (CNN) for the automatic detection of intracranial hemorrhage. The method is trained on a dataset comprised of cerebral CT studies for which the presence of hemorrhage has been labeled for each axial slice. A separate test dataset of 20 images is used for quantitative evaluation and shows a sensitivity of 0.87, specificity of 0.97 and accuracy of 0.95. The average processing time for a single three-dimensional (3D) CT volume was 2.7 seconds. The proposed method is capable of fast and automated detection of intracranial hemorrhages in non-contrast CT without being limited to a specific subtype of pathology.
Segmentation of the arteries and veins of the cerebral vasculature is important for improved visualization and for the detection of vascular related pathologies including arteriovenous malformations. We propose a 3D fully convolutional neural network (CNN) using a time-to-signal image as input and the distance to the center of gravity of the brain as spatial feature integrated in the final layers of the CNN. The method was trained and validated on 6 and tested on 4 4D CT patient imaging data. The reference standard was acquired by manual annotations by an experienced observer. Quantitative evaluation showed a mean Dice similarity coefficient of
0.94 ± 0.03 and 0.97 ± 0.01, a mean absolute volume difference of 4.36 ± 5.47 % and 1.79 ± 2.26 % for artery and vein respectively and an overall accuracy of 0.96 ± 0.02. The average calculation time per volume on the test set was approximately one minute. Our method shows promising results and enables fast and accurate segmentation of arteries and veins in full 4D CT imaging data.
Segmentation of anatomical structures is fundamental in the development of computer aided diagnosis systems for cerebral pathologies. Manual annotations are laborious, time consuming and subject to human error and observer variability. Accurate quantification of cerebrospinal fluid (CSF) can be employed as a morphometric measure for diagnosis and patient outcome prediction. However, segmenting CSF in non-contrast CT images is complicated by low soft tissue contrast and image noise. In this paper we propose a state-of-the-art method using a multi-scale three-dimensional (3D) fully convolutional neural network (CNN) to automatically segment all CSF within the cranial cavity. The method is trained on a small dataset comprised of four manually annotated cerebral CT images. Quantitative evaluation of a separate test dataset of four images shows a mean Dice similarity coefficient of 0.87 ± 0.01 and mean absolute volume difference of 4.77 ± 2.70 %. The average prediction time was 68 seconds. Our method allows for fast and fully automated 3D segmentation of cerebral CSF in non-contrast CT, and shows promising results despite a limited amount of training data.
In this work a fully automated detection method for artery input function (AIF) and venous output function (VOF) in 4D-computer tomography (4D-CT) data is presented based on unsupervised classification of the
time intensity curves (TIC) as input data. Bone and air voxels are first masked out using thresholding of the
baseline measurement. The TICs for each remaining voxel are converted to time-concentration-curves (TCC)
by subtracting the baseline value from the TIC. Then, an unsupervised K-means classifier is applied to each
TCC with an area under the curve (AUC) larger than 95% of the maximum AUC of all TCCs. The results are
three clusters, which yield average TCCs for vein and artery voxels in the brain, respectively. A third cluster
generally represents a vessel outside the brain. The algorithm was applied to five 4D-CT patient data who were
scanned on the suspicion of ischemic stroke. For all _ve patients, the algorithm yields reasonable classification
of arteries and veins as well as reasonable and reproducible AIFs and VOF. To our knowledge, this is the first
application of an unsupervised classification method to automatically identify arteries and veins in 4D-CT data.
Preliminary results show the feasibility of using K-means clustering for the purpose of artery-vein detection in
4D-CT patient data.
This work presents a novel hardware implementation of a levelset algorithm for carotid lumen segmentation
in computed tomography. We propose to use a field programmable gate array (FPGA) to iteratively solve
the underlying finite difference scheme. A FPGA processor can be programmed to have a dedicated hardware
architecture including specific data path and processor core design with different types of parallelizations which is
fully tailored and optimized toward its application. The method has been applied to ten carotid bifurcation of six
stroke patients and the results have been compared to the results obtained from the same method implemented
in C++. Visual inspections revealed similar segmentation results. The average computation time in software
was 1663 ± 86 seconds, the computation time on the FPGA processor was 28 seconds yielding approximately a
60-fold speed-up which to our knowledge has been unmmatched before for this class of algorithms.
In this study, a pattern recognition-based framework is presented to automatically segment the complete cerebral
vasculature from 4D Computed Tomography (CT) patient data. Ten consecutive patients whom were admitted
to our hospital on a suspicion of ischemic stroke were included in this study. A background mask and bone
mask were calculated based on intensity thresholding and morphological operations, and the following six image
features were proposed: 1) a subtraction image of a subtraction image consisting of timing-invariant CTA and
non-constrast CT, 2) the area under the curve of a gamma variate function fitted to the tissue curves, 3-5) three
optimized parameter values of this gamma variate function, and 6) a vessel likeliness function. After masking
bone and background, these features were used to train a linear discriminant voxel classifier (LDC) on regions
of interest (ROIs), which were annotated in soft tissue (white matter and gray matter) and vessels by an expert
observer. The LDC was trained in a leave-one-out manner in which 9 patients tissue ROIs were used for training
and the remaining patient tissue ROIs were used for testing the classifier. To evaluate the frame work, for each
training cycle the accuracy was calculated by dividing the true positives and negatives by the true positives and
negatives and false positives and negatives. The resulting averaged accuracy was 0:985±0:014 with a range of
0:957 to 0:999.
A method is proposed to segment anatomical regions of the brain from 4D computer tomography (CT) patient
data. The method consists of a three step voxel classification scheme, each step focusing on structures that are
increasingly difficult to segment. The first step classifies air and bone, the second step classifies vessels and the
third step classifies white matter, gray matter and cerebrospinal fluid. As features the time averaged intensity
value and the temporal intensity change value were used. In each step, a k-Nearest-Neighbor classifier was used
to classify the voxels. Training data was obtained by placing regions of interest in reconstructed 3D image data.
The method has been applied to ten 4D CT cerebral patient data. A leave-one-out experiment showed consistent
and accurate segmentation results.
Next to aneurysm size, aneurysm growth over time is an important indicator for aneurysm rupture risk. Manual
assessment of aneurysm growth is a cumbersome procedure, prone to inter-observer and intra-observer variability. In
clinical practice, mainly qualitative assessment and/or diameter measurement are routinely performed. In this paper a
semi-automated method for quantifying aneurysm volume growth over time in CTA data is presented. The method treats
a series of longitudinal images as a 4D dataset. Using a 4D groupwise non-rigid registration method, deformations with
respect to the baseline scan are determined. Combined with 3D aneurysm segmentation in the baseline scan, volume
change is assessed using the deformation field at the aneurysm wall. For ten patients, the results of the method are
compared with reports from expert clinicians, showing that the quantitative results of the method are in line with the
assessment in the radiology reports. The method is also compared to an alternative method in which the volume is
segmented in each 3D scan individually, showing that the 4D groupwise registration method agrees better with manual
assessment.
KEYWORDS: Image segmentation, Arteries, Image classification, Calcium, Tissues, Magnetic resonance imaging, 3D modeling, 3D image processing, Signal attenuation, Health informatics
This paper presents a level set based method for segmenting the outer vessel wall and plaque components of the carotid
artery in CTA. The method employs a GentleBoost classification framework that classifies pixels as calcified region or
not, and inside or outside the vessel wall. The combined result of both classifications is used to construct a speed
function for level set based segmentation of the outer vessel wall; the segmented lumen is used to initialize the level set.
The method has been optimized on 20 datasets and evaluated on 80 datasets for which manually annotated data was
available as reference. The average Dice similarity of the outer vessel wall segmentation was 92%, which compares
favorably to previous methods.
Accurately quantifying aneurysm shape parameters is of clinical importance, as it is an important factor in choosing the
right treatment modality (i.e. coiling or clipping), in predicting rupture risk and operative risk and for pre-surgical
planning. The first step in aneurysm quantification is to segment it from other structures that are present in the image. As
manual segmentation is a tedious procedure and prone to inter- and intra-observer variability, there is a need for an
automated method which is accurate and reproducible. In this paper a novel semi-automated method for segmenting
aneurysms in Computed Tomography Angiography (CTA) data based on Geodesic Active Contours is presented and
quantitatively evaluated. Three different image features are used to steer the level set to the boundary of the aneurysm,
namely intensity, gradient magnitude and variance in intensity. The method requires minimum user interaction, i.e.
clicking a single seed point inside the aneurysm which is used to estimate the vessel intensity distribution and to
initialize the level set. The results show that the developed method is reproducible, and performs in the range of interobserver
variability in terms of accuracy.
A novel 2D slice based automatic method for model based segmentation of the outer vessel wall of the common carotid artery in CTA data set is introduced. The method utilizes a lumen segmentation and AdaBoost, a fast and robust machine learning algorithm, to initially classify (mark) regions outside and inside the vessel wall using the distance from the lumen and intensity profiles sampled radially from the gravity center of the lumen. A similar method using the distance from the lumen and the image intensity as features is used to classify calcium regions. Subsequently, an ellipse shaped deformable model is fitted to the classification result. The method has achieved smaller detection error than the inter observer variability, and the method is robust against variation of the training data sets.
An automatic method is presented to segment the internal carotid arteries through the difficult part of the skull
base in CT angiography. The method uses the entropy per slice to select a cross sectional plane below the skull
base. In this plane 2D circular structures are detected by the Hough transform. The center points are used to
initialize a level set which evolves with a prior shape constraint on its topology. In contrast with some related
vessel segmentation methods, our approach does not require the acquisition of an additional CT scan for bone
masking. Experiments on twenty internal carotids in ten patients show that 19 seed points are correctly identified
(95%) and 18 carotids (90%) are successfully segmented without any human interaction.
An extension to level set based segmentation is proposed for vascular tree delineation. The method starts with topology extraction, by a shape constrained level set evolution steered by a strictly positive, image base speed function to ensure some oversegmentation. Next, the skeleton of the resulting oversegmentation is determined, which then is used to initialise another level set steered by a speed function with both negative and positive speed forces based on image features, to obtain a most accurate segmentation. The novelty of our approach lies in the shape constraint that is imposed implicitly on the first level set evolution. We apply repeatedly re-initializations of this evolution with a topology preserving skeleton of the current zero level set. We compare this method with a plain level set evolution steered by the same full range speed function. Both are initialised by placing a single seed point at the root of the vessel tree. Pilot experiments on twelve multislice CT data sets of the Circle of Willis show that our method is capable of segmenting the smaller branches at the distal part of the vessel tree structures and has the potential to segment vessels which are distal to a severe stenosis or occlusion.
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