Cryo-image Analysis of Tumor Cell Migration, Invasion, and Dispersal in a Mouse Xenograft Model of Human Glioblastoma Multiforme

Purpose

The goals of this study were to create cryo-imaging methods to quantify characteristics (size, dispersal, and blood vessel density) of mouse orthotopic models of glioblastoma multiforme (GBM) and to enable studies of tumor biology, targeted imaging agents, and theranostic nanoparticles.

Procedures

Green fluorescent protein-labeled, human glioma LN-229 cells were implanted into mouse brain. At 20?C38?days, cryo-imaging gave whole brain, 4-GB, 3D microscopic images of bright field anatomy, including vasculature, and fluorescent tumor. Image analysis/visualization methods were developed.

Results

Vessel visualization and segmentation methods successfully enabled analyses. The main tumor mass volume, the number of dispersed clusters, the number of cells/cluster, and the percent dispersed volume all increase with age of the tumor. Histograms of dispersal distance give a mean and median of 63 and 56???m, respectively, averaged over all brains. Dispersal distance tends to increase with age of the tumors. Dispersal tends to occur along blood vessels. Blood vessel density did not appear to increase in and around the tumor with this cell line.

Conclusion

Cryo-imaging and software allow, for the first time, 3D, whole brain, microscopic characterization of a tumor from a particular cell line. LN-229 exhibits considerable dispersal along blood vessels, a characteristic of human tumors that limits treatment success.     

A Comparative Study of Primary and Recurrent Human Glioblastoma Multiforme Using the Small Animal Imaging and Molecular Expressive Profiles

Purpose

Glioblastoma multiforme (GBM) is the most malignant brain tumor with the characteristics of highly infiltrative growth and recurrent rate. In this study, we used animal imaging and molecular expressive profiles to investigate the characteristics of the primary tumor (GBM-3) cells and recurrent tumor (S1R1) cells from different GBM patients.

Procedures

Bioluminescent imaging and 3T magnetic resonance imaging (MRI) were used for assessing the orthotopical tumor development of GBM cells harboring a polycistronic reporter gene system. Western blot analysis and quantitative polymerase chain reaction were used to compare the molecular expressive profiles of two types of GBM cells.

Results

S1R1 cells exhibited apparent invasive ability compared to GBM-3 cells using in vitro invasion assay. In vivo bioluminescent imaging showed that intracranial tumors are formed by both types of GBM cells, but the bioluminescent signal was also detected in the lumbar region at late-stage tumor formed by S1R1 cells. The MRI showed that intracranial tumors formed by S1R1 cells were highly infiltrative compared to that formed by GBM-3 cells. Additionally, these two GBM types expressed different patterns of molecules associated with tumor development. Moreover, the suppressive effects of interleukine-23 (IL-23) on xenograft tumors formed by both GBM types were detected using bioluminescent imaging.

Conclusion

The current data suggest that the in vivo growth behaviors and therapeutic responses of the primary and recurrent human GBMs were comparable using the reporter gene imaging, and different molecular expressive profiles exist between these two GBM types.     

Functionalized Magnetonanoparticles in Visualization of Intracranial Tumors on MRI

Purpose

The development of nonradioactive and targeted magnetonanoparticles (MNP) capable of crossing the blood–brain barrier (BBB) and of concentrating in and enhancing the contrast of intracranial tumors on magnetic resonance imaging (MRI).

Procedure

Nonradioactive 2-deoxy-d-glucose (2DG) was covalently attached to magnetonanoparticles composed of iron oxide and dextran and prepared for intravenous (tail) injection in the naïve rats and mouse models of glioma. MR images were acquired at 3 and 7 T.

Results

2DG-MNP increased tumor visibility and improved delineation of tumor margins. Histopathology confirmed that 2DG-MNP crossed the BBB and accumulated within brain parenchyma.

Conclusion

Nonradioactive 2DG-MNP can cross an intact BBB on and improve visualization of tumor and tumor margins on MRI.     

Local/non-local regularized image segmentation using graph-cuts

Objective

Multispectral, multichannel, or time series image segmentation is important for image analysis in a wide range of applications. Regularization of the segmentation is commonly performed using local image information causing the segmented image to be locally smooth or piecewise constant. A new spatial regularization method, incorporating non-local information, was developed and tested.

Methods

Our spatial regularization method applies to feature space classification in multichannel images such as color images and MR image sequences. The spatial regularization involves local edge properties, region boundary minimization, as well as non-local similarities. The method is implemented in a discrete graph-cut setting allowing fast computations.

Results

The method was tested on multidimensional MRI recordings from human kidney and brain in addition to simulated MRI volumes.

Conclusion

The proposed method successfully segment regions with both smooth and complex non-smooth shapes with a minimum of user interaction.     

Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson’s disease

Purpose

Template-based segmentation techniques have been developed to facilitate the accurate targeting of deep brain structures in patients with movement disorders. Three template-based brain MRI segmentation techniques were compared to determine the best strategy for segmenting the deep brain structures of patients with Parkinson’s disease.

Methods

T1-weighted and T2-weighted magnetic resonance (MR) image templates were created by averaging MR images of 57 patients with Parkinson’s disease. Twenty-four deep brain structures were manually segmented on the templates. To validate the template-based segmentation, 14 of the 24 deep brain structures from the templates were manually segmented on 10 MR scans of Parkinson’s patients as a gold standard. We compared the manual segmentations with three methods of automated segmentation: two registration-based approaches, automatic nonlinear image matching and anatomical labeling (ANIMAL) and symmetric image normalization (SyN), and one patch-label fusion technique. The automated labels were then compared with the manual labels using a Dice-kappa metric and center of gravity. A Friedman test was used to compare the Dice-kappa values and paired t tests for the center of gravity.

Results

The Friedman test showed a significant difference between the three methods for both thalami (p < 0.05) and not for the subthalamic nuclei. Registration with ANIMAL was better than with SyN for the left thalamus and was better than the patch-based method for the right thalamus.

Conclusion

Although template-based approaches are the most used techniques to segment basal ganglia by warping onto MR images, we found that the patch-based method provided similar results and was less time-consuming. Patch-based method may be preferable for the subthalamic nucleus segmentation in patients with Parkinson’s disease.     

Specific Chemotaxis of Magnetically Labeled Mesenchymal Stem Cells: Implications for MRI of Glioma

Purpose

Glioblastoma multiforme (GBM) is a lethal disease marked by infiltration of cancerous cells into the surrounding normal brain. The dire outcome of GBM patients stems in part from the limitations of current neuroimaging methods. Notably, early cancer detection methodologies are lacking, without the ability to identify aggressive, metastatic tumor cells. We propose a novel approach for tumor detection using magnetic resonance imaging (MRI) based on imaging specific tumor tropism of mesenchymal stem cells (MSCs) labeled with micron-sized iron oxide particles (MPIOs).

Procedures

MPIO labeled and unlabeled MSCs were compared for viability, multi-lineage differentiation, and migration, where both chemotactic and chemokinetic movement were assessed in the presence of serum-free medium, serum-containing medium, and glioma-conditioned medium. MRI was performed on agarose samples, consisting of MPIO-labeled single MSCs, to confirm the capability to detect single cells.

Results

We determined that MPIO-labeled MSCs exhibit specific and significant chemotactic migration towards glioma-conditioned medium in vitro. Confocal fluorescence microscopy confirmed that MPIOs are internalized and do not impact important cell processes of MSCs. Lastly, MPIO-labeled MSCs appear as single distinct, dark spots on T₂*-weighted MRI, supporting the robustness of this contrast agent for cell tracking.

Conclusions

This is the first study to show that MPIO-labeled MSCs exhibit specific tropism toward tumor-secreted factors in vitro. The potential for detecting single MPIO-labeled MSCs provides rationale for in vivo extension of this methodology to visualize GBM in animal models.     

An in vivo subject-specific 3D functional knee joint model using combined MR imaging

Purpose

We aim to quantitatively characterise the knee joint function in vivo under body-weight-bearing conditions via subject-specific models extracted from magnetic resonance (MR) data, in order to better understand the knee joint kinematic function in 3D.

Methods

Six healthy volunteers without any record of knee abnormality were scanned using a combined MR imaging strategy to record quasi-squatting motion and 3D knee anatomy. After a semi-automatic segmentation to delineate tibio-femoral articulation components, motion data were mapped to the anatomical data using a bi-rigid registration in order to achieve six degrees of freedom. The individual knee joint function was characterised by analysing the tibio-femoral articulation contact mechanism based on the reconstructed models in 3D and MR images in 2D. Contact points were extracted and their trajectory was plotted on the tibia plateau.

Results

The 3D models clearly show the relative rotation and gliding between tibia and femur during global flexion. Within the measured flexion arc, the contact points move less between 30 $^{\circ }$ and 100 $^{\circ }$ on both tibial plateaux as compared to that on the rest of the flexion arc. Four out of the six volunteers showed a global pattern of less moving extent of contact points on the medial tibial plateau than on the lateral tibial plateau in both 3D and 2D.

Conclusion

The proposed subject-specific model is able to characterise knee joint kinematic function. It provides a way to describe knee joint surface kinematics quantitatively, which may help to better understand the knee function and joint derangements.     

Within-brain classification for brain tumor segmentation

Purpose

In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a machine learning problem.

Methods

This method has an advantage over typical machine learning methods for this task where generalization is made across brains. The problem with these methods is that they need to deal with intensity bias correction and other MRI-specific noise. In this paper, we avoid these issues by approaching the problem as one of within brain generalization. Specifically, we propose a semi-automatic method that segments a brain tumor by training and generalizing within that brain only, based on some minimum user interaction.

Conclusion

We investigate how adding spatial feature coordinates (i.e., i, j, k) to the intensity features can significantly improve the performance of different classification methods such as SVM, kNN and random forests. This would only be possible within an interactive framework. We also investigate the use of a more appropriate kernel and the adaptation of hyper-parameters specifically for each brain.

Results

As a result of these experiments, we obtain an interactive method whose results reported on the MICCAI-BRATS 2013 dataset are the second most accurate compared to published methods, while using significantly less memory and processing power than most state-of-the-art methods.

     

4D statistical shape modeling of the left ventricle in cardiac MR images

Purpose

Statistical shape models have shown improved reliability and consistency in cardiac image segmentation. They incorporate a sufficient amount of a priori knowledge from the training datasets and solve some major problems such as noise and image artifacts or partial volume effect. In this paper, we construct a 4D statistical model of the left ventricle using human cardiac short-axis MR images.

Methods

Kernel PCA is utilized to explore the nonlinear variation of a population. The distribution of the landmarks is divided into the inter- and intra-subject subspaces. We compare the result of Kernel PCA with linear PCA and ICA for each of these subspaces. The initial atlas in natural coordinate system is built for the end-diastolic frame. The landmarks extracted from it are propagated to all frames of all datasets. We apply the 4D KPCA-based ASM for segmentation of all phases of a cardiac cycle and compare it with the conventional ASM.

Results

The proposed statistical model is evaluated by calculating the compactness capacity, specificity and generalization ability measures. We investigate the behavior of the nonlinear model for different values of the kernel parameter. The results show that the model built by KPCA is less compact than PCA but more compact than ICA. Although for a constant number of modes the reconstruction error is a little higher for the KPCA-based statistical model, it produces a statistical model with substantially better specificity than PCA- and ICA-based models.

Conclusion

Quantitative analysis of the results demonstrates that our method improves the segmentation accuracy.     

Improved segmentation of low-contrast lesions using sigmoid edge model

Purpose

The intensity profile of an image in the vicinity of a tissue’s boundary is modeled by a step/ramp function. However, this assumption does not hold in cases of low-contrast images, heterogeneous tissue textures, and where partial volume effect exists. We propose a hybrid algorithm for segmentation of CT/MR tumors in low-contrast, noisy images having heterogeneous/homogeneous or hyper-/hypo-intense abnormalities. We also model a smoothed noisy intensity profile by a sigmoid function and employ it to find the true location of boundary more accurately.

Methods

A novel combination of the SVM, watershed, and scattered data approximation algorithms is employed to initially segment a tumor. Small and large abnormalities are treated distinctly. Next, the proposed sigmoid edge model is fitted to the normal profile of the border. The estimated parameters of the model are then utilized to find true boundary of a tissue.

Results

We extensively evaluated our method using synthetic images (contaminated with varying levels of noise) and clinical CT/MR data. Clinical images included 57 CT/MR volumes consisting of small/large tumors, very low-/high-contrast images, liver/brain tumors, and hyper-/hypo-intense abnormalities. We achieved a Dice measure of \(0.83\,(\pm 0.07)\) and average symmetric surface distance of \(2.56\,(\pm 6.31)\) mm. Regarding IBSR dataset, we fulfilled Jaccard index of \(0.85\,(\pm 0.07)\). The average run-time of our code was \(154\,(\pm 71)\) s.

Conclusion

Individual treatment of small and large tumors and boundary correction using the proposed sigmoid edge model can be used to develop a robust tumor segmentation algorithm which deals with any types of tumors.

     

3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set

Purpose

Brain tumor segmentation is a required step before any radiation treatment or surgery. When performed manually, segmentation is time consuming and prone to human errors. Therefore, there have been significant efforts to automate the process. But, automatic tumor segmentation from MRI data is a particularly challenging task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. In our work, we propose an automatic brain tumor segmentation method that addresses these last two difficult problems.

Methods

We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multidimensional feature set. Then, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this work is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned region statistics in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters from the normal brain region to be in the tumor region. This leads to a better disambiguation of the tumor from brain tissue.

Results

We evaluated the performance of our automatic segmentation method on 15 real MRI scans of brain tumor patients, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Validation with the expert segmentation labels yielded encouraging results: Jaccard (58%), Precision (81%), Recall (67%), Hausdorff distance (24 mm).

Conclusions

Using priors on the brain/tumor appearance, our proposed automatic 3D variational segmentation method was able to better disambiguate the tumor from the surrounding tissue.

     

Combination Treatment with Theranostic Nanoparticles for Glioblastoma Sensitization to TMZ

Purpose

Tumor resistance to chemotherapeutic drugs is one of the major obstacles in the treatment of glioblastoma multiforme (GBM). In this study, we attempted to modulate tumor response to chemotherapy by combination treatment that included experimental (small interference RNA (siRNA), chlorotoxin) and conventional (temozolomide, TMZ) therapeutics.

Procedures

siRNA therapy was used to silence O⁶-methylguanine methyltransferase (MGMT), a key factor in brain tumor resistance to TMZ. For targeting of tumor cells, we used chlorotoxin (CTX), a peptide with antitumoral properties. siRNA and CTX were conjugated to iron oxide nanoparticles (NP) that served as the drug carrier and allowed the means to monitor the changes in tumor volume by magnetic resonance imaging (MRI).

Results

Theranostic nanoparticles (termed CTX-NP-siMGMT) were internalized by T98G glioblastoma cells in vitro leading to enhancement of TMZ toxicity. Combination treatment of mice bearing orthotopic tumors with CTX-NP-siMGMT and TMZ led to significant retardation of tumor growth, which was monitored by MRI.

Conclusions

While our results demonstrate that siRNA delivery by targeted nanoparticles resulted in modulating tumor response to chemotherapy in GBM, they also point to a significant contribution of CTX to tumor cell death.     

A hybrid approach for segmentation of anatomic structures in medical images

Objective

We propose a hybrid interactive approach for the segmentation of anatomic structures in medical images with higher accuracy at lower user interaction cost.

Materials and methods

Eighteen brain MR scans from the Internet Brain Segmentation Repository are used for brain structure segmentation. A MR scan and a CT scan of an old female are used for orbital structure segmentation. The proposed approach combines shape-based interpolation, radial basis function (RBF)-based warping and model-based segmentation. With this approach, to segment a structure in a 3D image, we first delineate the structure in several slices using interactive methods, and then use shape-based interpolation to automatically generate an initial 3D model of the structure from the segmented slices. To refine the initial model, we specify a set of additional points on the structure boundary in the image, and use a RBF to warp the model so that it passes the specified points. Finally, we adopt a point-anchored active surface approach to further deform the model for a better fitting of the model with its corresponding structure in image.

Results

Two brain structures and 15 orbital structures are segmented. For each structure, it needs only to semi- automatically segment three to five 2D slices and specify two to nine additional points on the structure boundary. The time cost for each structure is about 1–3 min. The overlap ratio of the segmentation results and the ground truth is higher than 96%.

Conclusion

The proposed method for the segmentation of anatomic structure achieved higher accuracy at lower user interaction cost, and therefore promising in many applications such as surgery planning and simulation, atlas construction, and morphometric analysis of anatomic structures.     

Fully automatic segmentation of the Femur from 3D-CT images using primitive shape recognition and statistical shape models

Purpose

Femur segmentation is well established and widely used in computer-assisted orthopedic surgery. However, most of the robust segmentation methods such as statistical shape models (SSM) require human intervention to provide an initial position for the SSM. In this paper, we propose to overcome this problem and provide a fully automatic femur segmentation method for CT images based on primitive shape recognition and SSM.

Method

Femur segmentation in CT scans was performed using primitive shape recognition based on a robust algorithm such as the Hough transform and RANdom SAmple Consensus. The proposed method is divided into 3 steps: (1) detection of the femoral head as sphere and the femoral shaft as cylinder in the SSM and the CT images, (2) rigid registration between primitives of SSM and CT image to initialize the SSM into the CT image, and (3) fitting of the SSM to the CT image edge using an affine transformation followed by a nonlinear fitting.

Results

The automated method provided good results even with a high number of outliers. The difference of segmentation error between the proposed automatic initialization method and a manual initialization method is less than 1 mm.

Conclusion

The proposed method detects primitive shape position to initialize the SSM into the target image. Based on primitive shapes, this method overcomes the problem of inter-patient variability. Moreover, the results demonstrate that our method of primitive shape recognition can be used for 3D SSM initialization to achieve fully automatic segmentation of the femur.     

PNist: interactive volumetric measurements of plexiform neurofibromas in MRI scans

Purpose

Volumetric measurements of plexiform neurofibromas (PNs) are time consuming and error prone, as they require the delineation of the PN boundaries, which is mostly impractical in the daily clinical setup. Accurate volumetric measurements are seldom performed for these tumors mainly due to their great dispersion, size and multiple locations. This paper presents a semiautomatic method for segmentation of PN from STIR MRI scans.

Methods

Plexiform neurofibroma interactive segmentation tool (PNist) is a new tool to segment PNs in STIR MRI scans. The method is based on histogram tumor models computed from a training set.

Results

Experimental results from 28 datasets show an average absolute volume difference of 6.8 % with an average user time of approximately 7 min versus more than 13 min with manual delineation. In complex cases, the PNist user time is less than half in compared to state-of-the-art tools.

Conclusions

PNist is a new method for the semiautomatic segmentation of PN lesions. Its simplicity and reliability make it unique among other state-of-the-art methods. It has the potential to become a clinical tool that allows the reliable evaluation of PN burden and progression.     

Design and initial evaluation of a treatment planning software system for MRI-guided laser ablation in the brain

Purpose

   An open-source software system for planning magnetic resonance (MR)-guided laser-induced thermal therapy (MRgLITT) in brain is presented. The system was designed to provide a streamlined and operator-friendly graphical user interface (GUI) for simulating and visualizing potential outcomes of various treatment scenarios to aid in decisions on treatment approach or feasibility.

Methods

   A portable software module was developed on the 3D Slicer platform, an open-source medical imaging and visualization framework. The module introduces an interactive GUI for investigating different laser positions and power settings as well as the influence of patient-specific tissue properties for quickly creating and evaluating custom treatment options. It also provides a common treatment planning interface for use by both open-source and commercial finite element solvers. In this study, an open-source finite element solver for Pennes’ bioheat equation is interfaced to the module to provide rapid 3D estimates of the steady-state temperature distribution and potential tissue damage in the presence of patient-specific tissue boundary conditions identified on segmented MR images.

Results

   The total time to initialize and simulate an MRgLITT procedure using the GUI was \(<\) 5 min. Each independent simulation took \(<\) 30 s, including the time to visualize the results fused with the planning MRI. For demonstration purposes, a simulated steady-state isotherm contour \((57\,^{\circ }\hbox {C})\) was correlated with MR temperature imaging (N = 5). The mean Hausdorff distance between simulated and actual contours was 2.0 mm \((\sigma \,=\,0.4\,\hbox {mm})\) , whereas the mean Dice similarity coefficient was 0.93 \((\sigma =0.026)\) .

Conclusions

   We have designed, implemented, and conducted initial feasibility evaluations of a software tool for intuitive and rapid planning of MRgLITT in brain. The retrospective in vivo dataset presented herein illustrates the feasibility and potential of incorporating fast, image-based bioheat predictions into an interactive virtual planning environment for such procedures.     

Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context

Purpose

To constrain the risk of severe toxicity in radiotherapy and radiosurgery, precise volume delineation of organs at risk is required. This task is still manually performed, which is time-consuming and prone to observer variability. To address these issues, and as alternative to atlas-based segmentation methods, machine learning techniques, such as support vector machines (SVM), have been recently presented to segment subcortical structures on magnetic resonance images (MRI).

Methods

SVM is proposed to segment the brainstem on MRI in multicenter brain cancer context. A dataset composed by 14 adult brain MRI scans is used to evaluate its performance. In addition to spatial and probabilistic information, five different image intensity values (IIVs) configurations are evaluated as features to train the SVM classifier. Segmentation accuracy is evaluated by computing the Dice similarity coefficient (DSC), absolute volumes difference (AVD) and percentage volume difference between automatic and manual contours.

Results

Mean DSC for all proposed IIVs configurations ranged from 0.89 to 0.90. Mean AVD values were below \(1.5\,\hbox {cm}^{3}\), where the value for best performing IIVs configuration was \(0.85\,\hbox {cm}^{3}\), representing an absolute mean difference of \(3.99\,\%\) with respect to the manual segmented volumes.

Conclusion

Results suggest consistent volume estimation and high spatial similarity with respect to expert delineations. The proposed approach outperformed presented methods to segment the brainstem, not only in volume similarity metrics, but also in segmentation time. Preliminary results showed that the approach might be promising for adoption in clinical use.

     

Cranio-Maxillofacial Surgery

Background

Neurosurgical procedures involving tumor resection require surgical planning such that the surgical path to the tumor is determined to minimize the impact on healthy tissue and brain function. This work demonstrates a predictive tool to aid neurosurgeons in planning tumor resection therapies by finding an optimal model-selected patient orientation that minimizes lateral brain shift in the field of view. Such orientations may facilitate tumor access and removal, possibly reduce the need for retraction, and could minimize the impact of brain shift on image-guided procedures.

Methods

In this study, preoperative magnetic resonance images were utilized in conjunction with pre- and post-resection laser range scans of the craniotomy and cortical surface to produce patient-specific finite element models of intraoperative shift for 6 cases. These cases were used to calibrate a model (i.e., provide general rules for the application of patient positioning parameters) as well as determine the current model-based framework predictive capabilities. Finally, an objective function is proposed that minimizes shift subject to patient position parameters. Patient positioning parameters were then optimized and compared to our neurosurgeon as a preliminary study.

Results

The proposed model-driven brain shift minimization objective function suggests an overall reduction of brain shift by 23 % over experiential methods.

Conclusions

This work recasts surgical simulation from a trial-and-error process to one where options are presented to the surgeon arising from an optimization of surgical goals. To our knowledge, this is the first realization of an evaluative tool for surgical planning that attempts to optimize surgical approach by means of shift minimization in this manner.     

Cerebrovascular segmentation and planning of depth electrode insertion for epilepsy surgery

Purpose

Depth electrodes are inserted in the brain to locate the epileptogenic zone without craniotomy, but there is risk of surgical hemorrhage. Preoperative planning is required to mitigate this risk. A preoperative imaging, segmentation and three dimensional (3D) visualization procedure was developed to provide neurosurgeons with cortical and vascular anatomy information for surgical planning and neuronavigation.

Methods

Cerebral vascular imaging was performed with phase-contrast magnetic resonance angiography (PC-MRA). Fuzzy c-means was performed to extract brain parenchyma from the PC-MRA images. A multi-scale vessel enhancement filter and thresholding process were combined to segment the vasculature and suppress background noise in the PC-MRA images. Finally, 3D visualization of the vasculature and cortical structures was implemented using volume rendering.

Results

Quantitative and qualitative validation of the vascular segmentation method were done. Using manual vascular segmentation as the gold standard, our method produced a satisfactory result: sensitivity was as high as 90 % at a specificity level of 95 %. Moreover, comparing the 3D visualizations of the vasculature and cortical structure for 4 patients with their respective intraoperative craniotomy photographs showed high levels of similarity.

Conclusion

A new automated segmentation and visualization procedure provides sufficient and accurate cortical and vascular anatomy information compared to intraoperative photographs. This method has potential to assist neurosurgeons with planning and neuronavigation for depth electrode insertion with avoidance of cerebral hemorrhage.     

Expert-driven label fusion in multi-atlas-based segmentation of the prostate using weighted atlases

Purpose

Automated segmentation is required for radiotherapy treatment planning, and multi-atlas methods are frequently used for this purpose. The combination of multiple intermediate results from multi-atlas segmentation into a single segmentation map can be achieved by label fusion. A method that includes expert knowledge in the label fusion phase of multi-atlas-based segmentation was developed. The method was tested by application to prostate segmentation, and the accuracy was compared to standard techniques.

Methods

The selective and iterative method for performance level estimation (SIMPLE) algorithm for label fusion was modified with a weight map given by an expert that indicates the importance of each region in the evaluation of segmentation results. Voxel-based weights specified by an expert when performing the label fusion step in atlas-based segmentation were introduced into the modified SIMPLE algorithm. These weights incorporate expert knowledge on accuracy requirements in different regions of a segmentation. Using this knowledge, segmentation accuracy in regions known to be important can be improved by sacrificing segmentation accuracy in less important regions. Contextual information such as the presence of vulnerable tissue is then used in the segmentation process. This method using weight maps to fine-tune the result of multi-atlas-based segmentation was tested using a set of 146 atlas images consisting of an MR image of the lower abdomen and a prostate segmentation. Each image served as a target in a set of leave-one-out experiments. These experiments were repeated for a weight map derived from the clinical practice in our hospital.

Results

The segmentation accuracy increased 6 % in regions that border vulnerable tissue using expert-specified voxel-based weight maps. This was achieved at the cost of a 4 % decrease in accuracy in less clinically relevant regions.

Conclusion

The inclusion of expert knowledge in a multi-atlas-based segmentation procedure was shown to be feasible for prostate segmentation. This method allows an expert to ensure that automatic segmentation is most accurate in critical regions. This improved local accuracy can increase the practical value of automatic segmentation.