GPU accelerated segmentation and centerline extraction of tubular structures from medical images

A Precise Pulmonary Airway Tree Segmentation Method Using Quasi-Spherical Region Constraint and Tracheal Wall Gap Sealing

Accurate segmentation of the pulmonary airway tree is crucial for diagnosing lung diseases. To tackle the issues of low segmentation accuracy and frequent leaks in existing methods, this paper proposes a precise segmentation method using quasi-spherical region-constrained wavefront propagation with tracheal wall gap sealing. Based on the characteristic that the surface formed by seed points approximates the airway cross-section, the width of the unsegmented airway is calculated, determining the initial quasi-spherical constraint region. Using the wavefront propagation method, seed points are continuously propagated and segmented along the tracheal wall within the quasi-spherical constraint region, thus overcoming the need to determine complex segmentation directions. To seal tracheal wall gaps, a morphological closing operation is utilized to extract the characteristics of small holes and locate low-brightness tracheal wall gaps. By filling the CT values at these gaps, the method seals the tracheal wall gaps. Extensive experiments on the EXACT09 dataset demonstrate that our algorithm ranks third in segmentation completeness. Moreover, its performance in preventing airway leaks is significantly better than the top-two algorithms, effectively preventing large-scale leak-induced spread.

Hepatic vessels segmentation using deep learning and preprocessing enhancement

PURPOSE

Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of surgical treatment.

METHODS

Recently, the convolutional neural networks (CNN) have been proved to be efficient for the task of medical image segmentation. The paper proposes an automatic deep learning-based system for liver hepatic vessels segmentation of Computed Tomography (CT) datasets from different sources. The proposed work focuses on the combination of different steps; it starts by a preprocessing step to improve the vessels appearance within the liver region of interest in the CT scans. Coherence enhancing diffusion filtering (CED) and vesselness filtering methods are used to improve vessels contrast and intensity homogeneity. The proposed U-net based network architecture is implemented with modified residual block to include concatenation skip connection. The effect of enhancement using filtering step was studied. Also, the effect of data mismatch used in training and validation is studied.

RESULTS

The proposed method is evaluated using many CT datasets. Dice similarity coefficient (DSC) is used to evaluate the method. The average DSC score achieved a score 79%.

CONCLUSIONS

The proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning.

Performance evaluation of spatial fuzzy C-means clustering algorithm on GPU for image segmentation

Image processing by segmentation technique is an important phase in medical imaging such as MRI. Its objective is to analyze the different tissues in human body. In research area, Fuzzy set is one of the most successful techniques that guarantees a robust classification. Spatial FCM (SFCM); one of the fuzzy c-means variants; considers spatial information to deal with the noisy images. To reduce this iterative algorithm's execution time, a hard SIMD architecture has been planted named the Graphical Processing Unit (GPU). In this work, a great contribution has been done to diagnose, confront and implement three different parallel implementations on GPU. A parallel implementations' extensive study of SFCM entitled PSFCM using 3 × 3 window is presented, and the experiments illustrate a significant decrease in terms of running time of this algorithm known by its high complexity. The experimental results indicate that the parallel version's execution time is about 9.46 times faster than the sequential implementation on image segmentation. This gain in terms of speed-up is achieved on the Nvidia GeForce GT 740 m GPU.

Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters

Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters.

Accelerating Chan-Vese model with cross-modality guided contrast enhancement for liver segmentation

Accurate and fast liver segmentation remains a challenging and important task for clinicians. Segmentation algorithms are slow and inaccurate due to noise and low quality images in computed tomography (CT) abdominal scans. Chan-Vese is an active contour based powerful and flexible method for image segmentation due to superior noise robustness. However, it is quite slow due to time-consuming partial differential equations, especially for large medical datasets. This can pose a problem for a real-time implementation of liver segmentation and hence, an efficient parallel implementation is highly desirable. Another important aspect is the contrast of CT liver images. Liver slices are sometimes very low in contrast which reduces the overall quality of liver segmentation. Hence, we implement cross-modality guided liver contrast enhancement as a pre-processing step to liver segmentation. GPU implementation of Chan-Vese improves average speedup by 99.811 (± 7.65) times and 14.647 (± 1.155) times with and without enhancement respectively in comparison with the CPU. Average dice, sensitivity and accuracy of liver segmentation are 0.656, 0.816 and 0.822 respectively on the original liver images and 0.877, 0.964 and 0.956 respectively on the enhanced liver images improving the overall quality of liver segmentation.

Fast parallel vessel segmentation

BACKGROUND AND OBJECTIVE

Accurate and fast vessel segmentation from liver slices remain challenging and important tasks for clinicians. The algorithms from the literature are slow and less accurate. We propose fast parallel gradient based seeded region growing for vessel segmentation. Seeded region growing is tedious when the inter connectivity between the elements is unavoidable. Parallelizing region growing algorithms are essential towards achieving real time performance for the overall process of accurate vessel segmentation.

METHODS

The parallel implementation of seeded region growing for vessel segmentation is iterative and hence time consuming process. Seeded region growing is implemented as kernel termination and relaunch on GPU due to its iterative mechanism. The iterative or recursive process in region growing is time consuming due to intermediate memory transfers between CPU and GPU. We propose persistent and grid-stride loop based parallel approach for region growing on GPU. We analyze static region of interest of tiles on GPU for the acceleration of seeded region growing.

RESULTS

We aim fast parallel gradient based seeded region growing for vessel segmentation from CT liver slices. The proposed parallel approach is 1.9x faster compared to the state-of-the-art.

CONCLUSION

We discuss gradient based seeded region growing and its parallel implementation on GPU. The proposed parallel seeded region growing is fast compared to kernel termination and relaunch and accurate in comparison to Chan-Vese and Snake model for vessel segmentation.

AVATREE: An open-source computational modelling framework modelling Anatomically Valid Airway TREE conformations

This paper presents AVATREE, a computational modelling framework that generates Anatomically Valid Airway tree conformations and provides capabilities for simulation of broncho-constriction apparent in obstructive pulmonary conditions. Such conformations are obtained from the personalized 3D geometry generated from computed tomography (CT) data through image segmentation. The patient-specific representation of the bronchial tree structure is extended beyond the visible airway generation depth using a knowledge-based technique built from morphometric studies. Additional functionalities of AVATREE include visualization of spatial probability maps for the airway generations projected on the CT imaging data, and visualization of the airway tree based on local structure properties. Furthermore, the proposed toolbox supports the simulation of broncho-constriction apparent in pulmonary diseases, such as chronic obstructive pulmonary disease (COPD) and asthma. AVATREE is provided as an open-source toolbox in C++ and is supported by a graphical user interface integrating the modelling functionalities. It can be exploited in studies of gas flow, gas mixing, ventilation patterns and particle deposition in the pulmonary system, with the aim to improve clinical decision making.

GPU acceleration of liver enhancement for tumor segmentation

BACKGROUND AND OBJECTIVE

Medical image segmentation plays a vital role in medical image analysis. There are many algorithms developed for medical image segmentation which are based on edge or region characteristics. These are dependent on the quality of the image. The contrast of a CT or MRI image plays an important role in identifying region of interest i.e. lesion(s). In order to enhance the contrast of image, clinicians generally use manual histogram adjustment technique which is based on 1D histogram specification. This is time consuming and results in poor distribution of pixels over the image. Cross modality based contrast enhancement is 2D histogram specification technique. This is robust and provides a more uniform distribution of pixels over CT image by exploiting the inner structure information from MRI image. This helps in increasing the sensitivity and accuracy of lesion segmentation from enhanced CT image. The sequential implementation of cross modality based contrast enhancement is slow. Hence we propose GPU acceleration of cross modality based contrast enhancement for tumor segmentation.

METHODS

The aim of this study is fast parallel cross modality based contrast enhancement for CT liver images. This includes pairwise 2D histogram, histogram equalization and histogram matching. The sequential implementation of the cross modality based contrast enhancement is computationally expensive and hence time consuming. We propose persistence and grid-stride loop based fast parallel contrast enhancement for CT liver images. We use enhanced CT liver image for the lesion or tumor segmentation. We implement the fast parallel gradient based dynamic seeded region growing for lesion segmentation.

RESULTS

The proposed parallel approach is 104.416 ( ±  5.166) times faster compared to the sequential implementation and increases the sensitivity and specificity of tumor segmentation.

CONCLUSION

The cross modality approach is inspired by 2D histogram specification which incorporates spatial information existing in both guidance and input images for remapping the input image intensity values. The cross modality based liver contrast enhancement improves the quality of tumor segmentation.

Ab Externo Imaging of Human Episcleral Vessels Using Fiberoptic Confocal Laser Endomicroscopy

Purpose

There is a growing interest in targeting minimally invasive surgery devices to the aqueous outflow system to optimize treatment outcomes. However, methods to visualize functioning, large-caliber aqueous and episcleral veins in-vivo are lacking. This pilot study establishes an ex-vivo system to evaluate the use of a confocal laser microendoscope to noninvasively image episcleral vessels and quantify regional flow variation along the limbal circumference.

Methods

A fiber-optic confocal laser endomicroscopy (CLE) system with lateral and axial resolution of 3.5 μm and 15 μm, respectively, was used on three porcine and four human eyes. Diluted fluorescein (0.04%) was injected into eyes kept under constant infusion. The microprobe was applied to the sclera 1 mm behind the limbus to acquire real-time video. Image acquisition was performed at 15-degree intervals along the limbal circumference to quantify regional flow variation in human eyes.

Results

Vascular structures were visualized in whole human eyes without processing. Schlemm's canal was visualized only after a scleral flap was created. Fluorescent signal intensity and vessel diameter variation were observed along the limbal circumference, with the inferior quadrant having a statistically higher fluorescein signal compared to the other quadrants in human eyes (P < 0.05).

Conclusion

This study demonstrates for the first time that the fiber-optic CLE platform can visualize the episcleral vasculature with high resolution ex-vivo with minimal tissue manipulation. Intravascular signal intensities and vessel diameters were acquired in real-time; such information can help select target areas for minimally invasive glaucoma surgery (MIGS) to achieve greater intraocular pressure reduction.

Preprocessing of Medical Image Data for Three-Dimensional Bioprinted Customized-Neural-Scaffolds

IMPACT STATEMENT

Nerve damage, which can be devastating, triggers several biological cascades, which result in the insufficiencies of the human nervous system to provide complete nerve repair and regain of function. Since no therapeutic strategy exists to provide immediate attention and intervention to patients with newly acquired nerve damage, we propose a strategy in which accelerated medical image processing through graphical processing unit implementation and three-dimensional printing are combined to produce a time-efficient, patient-specific (custom-neural-scaffold) solution to nerve damage. This work aims to beneficially shorten the time required for medical decision-making so that improved patient outcomes are achieved.

Using the CustusX toolkit to create an image guided bronchoscopy application: Fraxinus

PURPOSE

The aim of this paper is to show how a specialized planning and guidance application called Fraxinus, can be built on top of the CustusX platform (www.custusx.org), which is an open source image-guided intervention software platform. Fraxinus has been customized to meet the clinical needs in navigated bronchoscopy.

METHODS

The application requirements for Fraxinus were defined in close collaboration between research scientists, software developers and clinicians (pulmonologists), and built on top of CustusX. Its superbuild system downloads specific versions of the required libraries and builds them for the application in question, including the selected plugins. New functionality is easily added through the plugin framework. The build process enables the creation of specialized applications, adding additional documentation and custom configurations. The toolkit's libraries offer building blocks for image-guided applications. An iterative development process was applied, where the clinicians would test and provide feedback during the entire process.

RESULTS

Fraxinus has been developed and is released as an open source planning and guidance application built on top of CustusX. It is highly specialized for bronchoscopy. The proposed workflow is adapted to the different steps in this procedure. The user interface of CustusX has been modified to enhance information, quality assurance and user friendliness with the intention to increase the overall yield for the patient. As the workflow of the procedure is relatively constant, some actions are predicted and automatically performed by the application, according to the requirements from the clinicians.

CONCLUSIONS

The CustusX platform facilitates development of new and specialized applications. The toolkit supports the process and makes important extension and injection points available for customization.

Image-Based 3-Dimensional Characterization of Laryngotracheal Stenosis in Children

Objectives

Describe a technique for the description and classification of laryngotracheal stenosis in children using 3-dimensional reconstructions of the airway from computed tomography (CT) scans.

Study Design

Cross-sectional.

Setting

Academic tertiary care children’s hospital.

Subjects and Methods

Three-dimensional models of the subglottic airway lumen were created using CT scans from 54 children undergoing imaging for indications other than airway disease. The base lumen models were deformed in software to simulate subglottic airway segments with 0%, 25%, 50%, and 75% stenoses for each subject. Statistical analysis of the airway geometry was performed using metrics extracted from the lumen centerlines. The centerline analysis was used to develop a system for subglottic stenosis assessment and classification from patient-specific airway imaging.

Results

The scaled hydraulic diameter gradient metric derived from intersectional changes in the lumen can be used to accurately classify and quantitate subglottic stenosis in the airway based on CT scan imaging. Classification is most accurate in the clinically relevant 25% to 75% range of stenosis.

Conclusions

Laryngotracheal stenosis is a complex diagnosis requiring an understanding of the airway lumen configuration, anatomical distortions of the airway framework, and alterations of respiratory aerodynamics. Using image-based airway models, we have developed a metric that accurately captures subglottis patency. While not intended to replace endoscopic evaluation and existing staging systems for laryngotracheal stenosis, further development of these techniques will facilitate future studies of upper airway computational fluid dynamics and the clinical evaluation of airway disease.

The Direct Oblique Method: A New Gold Standard for Bronchoscopic Navigation That is Superior to Automatic Methods

BACKGROUND

The purpose of this study was to identify bronchi on computed tomographic (CT) images, manual analysis is more accurate than automatic methods. Nonetheless, manual bronchoscopic navigation is not preferred as it involves mentally reconstructing a route to a bronchial target by interpreting 2-dimensional CT images. Here, we established the direct oblique method (DOM), a form of manual bronchoscopic navigation that does not necessitate mental reconstruction, and compared it with automatic virtual bronchoscopic navigation (VBN).

METHODS

Routes were calculated to 47 identical targets using 2 automatic VBNs (LungPoint and VINCENT-BFsim) and the DOM, using 3 general application CT viewers (Aquarius, Synapse Vincent, and OsiriX). Results of all analyses were compared.

RESULTS

The DOM drew routes to more targets than the VBNs [94% (the DOM on any viewer) vs. 49% (LungPoint) vs. 62% (VINCENT-BFsim), P<0.0001]. For the 44 targets with the CT-bronchus or CT-artery signs, 100% of the DOM routes led to targets. In the bronchoscopic simulation phase, the DOM covered 100% of the bifurcations identified on CT, whereas some bifurcations were skipped and some bronchial walls appeared partially transparent in the VBNs. Manual analysis identified more bronchi near the targets than the VBNs [32.1±3.4 (manual analysis) vs.18.9±2.1 (LungPoint) vs. 22.9±2.7 (VINCENT-BFsim), mean±SEM, P<0.0001]. The DOM took around 5 minutes on average.

CONCLUSION

On the basis of precise manual CT analysis using general application CT viewers, the DOM drew routes leading to more targets and provided better bronchoscopic simulation than the automatic route calculation of the VBNs.

Automatic liver vessel segmentation using 3D region growing and hybrid active contour model

This paper proposes a new automatic method for liver vessel segmentation by exploiting intensity and shape constraints of 3D vessels. The core of the proposed method is to apply two different strategies: 3D region growing facilitated by bi-Gaussian filter for thin vessel segmentation, and hybrid active contour model combined with K-means clustering for thick vessel segmentation. They are then integrated to generate final segmentation results. The proposed method is validated on abdominal computed tomography angiography (CTA) images, and obtains an average accuracy, sensitivity, specificity, Dice, Jaccard, and RMSD of 98.2%, 68.3%, 99.2%, 73.0%, 66.1%, and 2.56 mm, respectively. Experimental results show that our method is capable of segmenting complex liver vessels with more continuous and complete thin vessel details, and outperforms several existing 3D vessel segmentation algorithms.

Automatic intraoperative estimation of blood flow direction during neurosurgical interventions

PURPOSE

In neurosurgery, reliable information about blood vessel anatomy and flow direction is important to identify, characterize, and avoid damage to the vasculature. Due to ultrasound Doppler angle dependencies and the complexity of the vascular architecture, clinically valuable 3-D flow direction information is currently not available. In this paper, we aim to clinically validate and demonstrate the intraoperative use of a fully automatic method for estimation of 3-D blood flow direction from freehand 2-D Doppler ultrasound.

METHODS

A 3-D vessel model is reconstructed from 2-D Doppler ultrasound and used to determine the vessel architecture. The blood flow direction is then estimated automatically using the model in combination with Doppler velocity data. To enable testing and validation during surgery, the method was implemented as part of the open-source navigation system CustusX ( www.custusx.org ).

RESULTS

Ten patients were included prospectively. Data from four patients were processed postoperatively, and data from six patients were processed intraoperatively. In total, the blood flow direction was estimated for 48 different blood vessels with a success rate of 98%.

CONCLUSIONS

In this work, we have shown that the proposed method is suitable for fully automatic estimation of the blood flow direction in intracranial vessels during neurosurgical interventions. The method has the potential to make the understanding of the complex vascular anatomy and flow pattern more intuitive for the surgeon. The method is compatible with intraoperative use, and results can be presented within the limited time frame where they still are of clinical interest.

Liver vessel segmentation and identification based on oriented flux symmetry and graph cuts

BACKGROUND AND OBJECTIVE

Accurate segmentation of liver vessels from abdominal computer tomography angiography (CTA) volume is very important for liver-vessel analysis and living-related liver transplants. This paper presents a novel liver-vessel segmentation and identification method.

METHODS

Firstly, an anisotropic diffusion filter is used to smooth noise while preserving vessel boundaries. Then, based on the gradient symmetry and antisymmetry pattern of vessel structures, optimal oriented flux (OOF) and oriented flux antisymmetry (OFA) measures are respectively applied to detect liver vessels and their boundaries, and further to slenderize vessels. Next, according to vessel geometrical structure, a centerline extraction measure based on height ridge traversal and leaf node line-growing (LNLG) is proposed for the extraction of liver-vessel centerlines, and an intensity model based on fast marching is integrated into graph cuts (GCs) for effective segmentation of liver vessels. Finally, a distance voting mechanism is applied to separate the hepatic vein and portal vein.

RESULTS

The experiment results on abdominal CTA images show that the proposed method can effectively segment liver vessels, achieving an average accuracy, sensitivity, and specificity of 97.7%, 79.8%, and 98.6%, respectively, and has a good performance on thin-vessel extraction.

CONCLUSIONS

The proposed method does not require manual selection of the centerlines and vessel seeds, and can effectively segment liver vessels and identify hepatic vein and portal vein.

A new visualization method for navigated bronchoscopy

OBJECTIVE

In flexible endoscopy techniques, such as bronchoscopy, there is often a challenge visualizing the path from start to target based on preoperative data and accessing these during the procedure. An example of this is visualizing only the inside of central airways in bronchoscopy. Virtual bronchoscopy (VB) does not meet the pulmonologist's need to detect, define and sample the frequent targets outside the bronchial wall. Our aim was to develop and study a new visualization technique for navigated bronchoscopy.

MATERIAL AND METHODS

We extracted the shortest possible path from the top of the trachea to the target along the airway centerline and a corresponding auxiliary route in the opposite lung. A surface structure between the centerlines was developed and displayed. The new technique was tested on non-selective CT data from eight patients using artificial lung targets.

RESULTS

The new display technique anchored to centerline curved surface (ACCuSurf) made it easy to detect and interpret anatomical features, targets and neighboring anatomy outside the airways, in all eight patients.

CONCLUSIONS

ACCuSurf can simplify planning and performing navigated bronchoscopy, meets the challenge of improving orientation and register the direction of the moving endoscope, thus creating an optimal visualization for navigated bronchoscopy.

A multimodal image guiding system for Navigated Ultrasound Bronchoscopy (EBUS): A human feasibility study

BACKGROUND

Endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) is the endoscopic method of choice for confirming lung cancer metastasis to mediastinal lymph nodes. Precision is crucial for correct staging and clinical decision-making. Navigation and multimodal imaging can potentially improve EBUS-TBNA efficiency.

AIMS

To demonstrate the feasibility of a multimodal image guiding system using electromagnetic navigation for ultrasound bronchoschopy in humans.

METHODS

Four patients referred for lung cancer diagnosis and staging with EBUS-TBNA were enrolled in the study. Target lymph nodes were predefined from the preoperative computed tomography (CT) images. A prototype convex probe ultrasound bronchoscope with an attached sensor for position tracking was used for EBUS-TBNA. Electromagnetic tracking of the ultrasound bronchoscope and ultrasound images allowed fusion of preoperative CT and intraoperative ultrasound in the navigation software. Navigated EBUS-TBNA was used to guide target lymph node localization and sampling. Navigation system accuracy was calculated, measured by the deviation between lymph node position in ultrasound and CT in three planes. Procedure time, diagnostic yield and adverse events were recorded.

RESULTS

Preoperative CT and real-time ultrasound images were successfully fused and displayed in the navigation software during the procedures. Overall navigation accuracy (11 measurements) was 10.0 ± 3.8 mm, maximum 17.6 mm, minimum 4.5 mm. An adequate sample was obtained in 6/6 (100%) of targeted lymph nodes. No adverse events were registered.

CONCLUSIONS

Electromagnetic navigated EBUS-TBNA was feasible, safe and easy in this human pilot study. The clinical usefulness was clearly demonstrated. Fusion of real-time ultrasound, preoperative CT and electromagnetic navigational bronchoscopy provided a controlled guiding to level of target, intraoperative overview and procedure documentation.

Pulmonary Fissure Detection in CT Images Using a Derivative of Stick Filter

Pulmonary fissures are important landmarks for recognition of lung anatomy. In CT images, automatic detection of fissures is complicated by factors like intensity variability, pathological deformation and imaging noise. To circumvent this problem, we propose a derivative of stick (DoS) filter for fissure enhancement and a post-processing pipeline for subsequent segmentation. Considering a typical thin curvilinear shape of fissure profiles inside 2D cross-sections, the DoS filter is presented by first defining nonlinear derivatives along a triple stick kernel in varying directions. Then, to accommodate pathological abnormality and orientational deviation, a [Formula: see text] cascading and multiple plane integration scheme is adopted to form a shape-tuned likelihood for 3D surface patches discrimination. During the post-processing stage, our main contribution is to isolate the fissure patches from adhering clutters by introducing a branch-point removal algorithm, and a multi-threshold merging framework is employed to compensate for local intensity inhomogeneity. The performance of our method was validated in experiments with two clinical CT data sets including 55 publicly available LOLA11 scans as well as separate left and right lung images from 23 GLUCOLD scans of COPD patients. Compared with manually delineating interlobar boundary references, our method obtained a high segmentation accuracy with median F1-scores of 0.833, 0.885, and 0.856 for the LOLA11, left and right lung images respectively, whereas the corresponding indices for a conventional Wiemker filtering method were 0.687, 0.853, and 0.841. The good performance of our proposed method was also verified by visual inspection and demonstration on abnormal and pathological cases, where typical deformations were robustly detected together with normal fissures.

Liver vessel segmentation based on extreme learning machine

Liver-vessel segmentation plays an important role in vessel structure analysis for liver surgical planning. This paper presents a liver-vessel segmentation method based on extreme learning machine (ELM). Firstly, an anisotropic filter is used to remove noise while preserving vessel boundaries from the original computer tomography (CT) images. Then, based on the knowledge of prior shapes and geometrical structures, three classical vessel filters including Sato, Frangi and offset medialness filters together with the strain energy filter are used to extract vessel structure features. Finally, the ELM is applied to segment liver vessels from background voxels. Experimental results show that the proposed method can effectively segment liver vessels from abdominal CT images, and achieves good accuracy, sensitivity and specificity.

Real-Time Automatic Artery Segmentation, Reconstruction and Registration for Ultrasound-Guided Regional Anaesthesia of the Femoral Nerve

The goal is to create an assistant for ultrasound- guided femoral nerve block. By segmenting and visualizing the important structures such as the femoral artery, we hope to improve the success of these procedures. This article is the first step towards this goal and presents novel real-time methods for identifying and reconstructing the femoral artery, and registering a model of the surrounding anatomy to the ultrasound images. The femoral artery is modelled as an ellipse. The artery is first detected by a novel algorithm which initializes the artery tracking. This algorithm is completely automatic and requires no user interaction. Artery tracking is achieved with a Kalman filter. The 3D artery is reconstructed in real-time with a novel algorithm and a tracked ultrasound probe. A mesh model of the surrounding anatomy was created from a CT dataset. Registration of this model is achieved by landmark registration using the centerpoints from the artery tracking and the femoral artery centerline of the model. The artery detection method was able to automatically detect the femoral artery and initialize the tracking in all 48 ultrasound sequences. The tracking algorithm achieved an average dice similarity coefficient of 0.91, absolute distance of 0.33 mm, and Hausdorff distance 1.05 mm. The mean registration error was 2.7 mm, while the average maximum error was 12.4 mm. The average runtime was measured to be 38, 8, 46 and 0.2 milliseconds for the artery detection, tracking, reconstruction and registration methods respectively.

Airway Segmentation and Centerline Extraction from Thoracic CT - Comparison of a New Method to State of the Art Commercialized Methods

INTRODUCTION

Our motivation is increased bronchoscopic diagnostic yield and optimized preparation, for navigated bronchoscopy. In navigated bronchoscopy, virtual 3D airway visualization is often used to guide a bronchoscopic tool to peripheral lesions, synchronized with the real time video bronchoscopy. Visualization during navigated bronchoscopy, the segmentation time and methods, differs. Time consumption and logistics are two essential aspects that need to be optimized when integrating such technologies in the interventional room. We compared three different approaches to obtain airway centerlines and surface.

METHOD

CT lung dataset of 17 patients were processed in Mimics (Materialize, Leuven, Belgium), which provides a Basic module and a Pulmonology module (beta version) (MPM), OsiriX (Pixmeo, Geneva, Switzerland) and our Tube Segmentation Framework (TSF) method. Both MPM and TSF were evaluated with reference segmentation. Automatic and manual settings allowed us to segment the airways and obtain 3D models as well as the centrelines in all datasets. We compared the different procedures by user interactions such as number of clicks needed to process the data and quantitative measures concerning the quality of the segmentation and centrelines such as total length of the branches, number of branches, number of generations, and volume of the 3D model.

RESULTS

The TSF method was the most automatic, while the Mimics Pulmonology Module (MPM) and the Mimics Basic Module (MBM) resulted in the highest number of branches. MPM is the software which demands the least number of clicks to process the data. We found that the freely available OsiriX was less accurate compared to the other methods regarding segmentation results. However, the TSF method provided results fastest regarding number of clicks. The MPM was able to find the highest number of branches and generations. On the other hand, the TSF is fully automatic and it provides the user with both segmentation of the airways and the centerlines. Reference segmentation comparison averages and standard deviations for MPM and TSF correspond to literature.

CONCLUSION

The TSF is able to segment the airways and extract the centerlines in one single step. The number of branches found is lower for the TSF method than in Mimics. OsiriX demands the highest number of clicks to process the data, the segmentation is often sparse and extracting the centerline requires the use of another software system. Two of the software systems performed satisfactory with respect to be used in preprocessing CT images for navigated bronchoscopy, i.e. the TSF method and the MPM. According to reference segmentation both TSF and MPM are comparable with other segmentation methods. The level of automaticity and the resulting high number of branches plus the fact that both centerline and the surface of the airways were extracted, are requirements we considered particularly important. The in house method has the advantage of being an integrated part of a navigation platform for bronchoscopy, whilst the other methods can be considered preprocessing tools to a navigation system.

A novel platform for electromagnetic navigated ultrasound bronchoscopy (EBUS)

Purpose

Endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) of mediastinal lymph nodes is essential for lung cancer staging and distinction between curative and palliative treatment. Precise sampling is crucial. Navigation and multimodal imaging may improve the efficiency of EBUS-TBNA. We demonstrate a novel EBUS-TBNA navigation system in a dedicated airway phantom.

Methods

Using a convex probe EBUS bronchoscope (CP-EBUS) with an integrated sensor for electromagnetic (EM) position tracking, we performed navigated CP-EBUS in a phantom. Preoperative computed tomography (CT) and real-time ultrasound (US) images were integrated into a navigation platform for EM navigated bronchoscopy. The coordinates of targets in CT and US volumes were registered in the navigation system, and the position deviation was calculated.

Results

The system visualized all tumor models and displayed their fused CT and US images in correct positions in the navigation system. Navigating the EBUS bronchoscope was fast and easy. Mean error observed between US and CT positions for 11 target lesions (37 measurements) was \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.8\pm 1.0$$\end{document}2.8±1.0 mm, maximum error was 5.9 mm.

Conclusion

The feasibility of our novel navigated CP-EBUS system was successfully demonstrated. An EBUS navigation system is needed to meet future requirements of precise mediastinal lymph node mapping, and provides new opportunities for procedure documentation in EBUS-TBNA.

CustusX: an open-source research platform for image-guided therapy

Purpose

CustusX is an image-guided therapy (IGT) research platform dedicated to intraoperative navigation and ultrasound imaging. In this paper, we present CustusX as a robust, accurate, and extensible platform with full access to data and algorithms and show examples of application in technological and clinical IGT research.

Methods

CustusX has been developed continuously for more than 15 years based on requirements from clinical and technological researchers within the framework of a well-defined software quality process. The platform was designed as a layered architecture with plugins based on the CTK/OSGi framework, a superbuild that manages dependencies and features supporting the IGT workflow. We describe the use of the system in several different clinical settings and characterize major aspects of the system such as accuracy, frame rate, and latency.

Results

The validation experiments show a navigation system accuracy of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<$$\end{document}<1.1 mm, a frame rate of 20 fps, and latency of 285 ms for a typical setup. The current platform is extensible, user-friendly and has a streamlined architecture and quality process. CustusX has successfully been used for IGT research in neurosurgery, laparoscopic surgery, vascular surgery, and bronchoscopy.

Conclusions

CustusX is now a mature research platform for intraoperative navigation and ultrasound imaging and is ready for use by the IGT research community. CustusX is open-source and freely available at http://www.custusx.org.

Online tracking of interventional devices for endovascular aortic repair

PURPOSE

The continuous integration of innovative imaging modalities into conventional vascular surgery rooms has led to an urgent need for computer assistance solutions that support the smooth integration of imaging within the surgical workflow. In particular, endovascular interventions performed under 2D fluoroscopic or angiographic imaging only, require reliable and fast navigation support for complex treatment procedures such as endovascular aortic repair. Despite the vast variety of image-based guide wire and catheter tracking methods, an adoption of these for detecting and tracking the stent graft delivery device is not possible due to its special geometry and intensity appearance.

METHODS

In this paper, we present, for the first time, the automatic detection and tracking of the stent graft delivery device in 2D fluoroscopic sequences on the fly. The proposed approach is based on the robust principal component analysis and extends the conventional batch processing towards an online tracking system that is able to detect and track medical devices on the fly.

RESULTS

The proposed method has been tested on interventional sequences of four different clinical cases. In the lack of publicly available ground truth data, we have further initiated a crowd sourcing strategy that has resulted in 200 annotations by unexperienced users, 120 of which were used to establish a ground truth dataset for quantitatively evaluating our algorithm. In addition, we have performed a user study amongst our clinical partners for qualitative evaluation of the results.

CONCLUSIONS

Although we calculated an average error in the range of nine pixels, the fact that our tracking method functions on the fly and is able to detect stent grafts in all unfolding stages without fine-tuning of parameters has convinced our clinical partners and they all agreed on the very high clinical relevance of our method.

FAST: framework for heterogeneous medical image computing and visualization

PURPOSE

Computer systems are becoming increasingly heterogeneous in the sense that they consist of different processors, such as multi-core CPUs and graphic processing units. As the amount of medical image data increases, it is crucial to exploit the computational power of these processors. However, this is currently difficult due to several factors, such as driver errors, processor differences, and the need for low-level memory handling. This paper presents a novel FrAmework for heterogeneouS medical image compuTing and visualization (FAST). The framework aims to make it easier to simultaneously process and visualize medical images efficiently on heterogeneous systems.

METHODS

FAST uses common image processing programming paradigms and hides the details of memory handling from the user, while enabling the use of all processors and cores on a system. The framework is open-source, cross-platform and available online.

RESULTS

Code examples and performance measurements are presented to show the simplicity and efficiency of FAST. The results are compared to the insight toolkit (ITK) and the visualization toolkit (VTK) and show that the presented framework is faster with up to 20 times speedup on several common medical imaging algorithms.

CONCLUSIONS

FAST enables efficient medical image computing and visualization on heterogeneous systems. Code examples and performance evaluations have demonstrated that the toolkit is both easy to use and performs better than existing frameworks, such as ITK and VTK.