An abdominal aortic aneurysm segmentation method: level set with region and statistical information

Applying Artificial Intelligence to Predict Complications After Endovascular Aneurysm Repair

Objective: Complications after Endovascular Aneurysm Repair (EVAR) can be fatal. Patient follow-up for surveillance imaging is becoming more challenging as fewer patients are seen, particularly after the first year. The aim of this study was to develop an artificial intelligence model to predict the complication probability of individual patients to better identify those needing more intensive post-operative surveillance. Methods: Pre-operative CTA 3D reconstruction images of AAA from 273 patients who underwent EVAR from 2011-2020 were collected. Of these, 48 patients had post-operative complications including endoleak, AAA rupture, graft limb occlusion, renal artery occlusion, and neck dilation. A deep convolutional neural network model (VascAI©) was developed which utilized pre-operative 3D CT images to predict risk of complications after EVAR. The model was built with TensorFlow software and run on the Google Colab Platform. An initial training subset of 40 randomly selected patients with complications and 189 without were used to train the AI model while the remaining 8 positive and 36 negative cases tested its performance and prediction accuracy. Data down-sampling was used to alleviate data imbalance and data augmentation methodology to further boost model performance. Results: Successful training was completed on the 229 cases in the training set and then applied to predict the complication probability of each individual in the held-out performance testing cases. The model provided a complication sensitivity of 100% and identified all the patients who later developed complications after EVAR. Of 36 patients without complications, 16 (44%) were falsely predicted to develop complications. The results therefore demonstrated excellent sensitivity for identifying patients who would benefit from more stringent surveillance and decrease the frequency of surveillance in 56% of patients unlike to develop complications. Conclusion: AI models can be developed to predict the risk of post-operative complications with high accuracy. Compared to existing methods, the model developed in this study did not require any expert-annotated data but only the AAA CTA images as inputs. This model can play an assistive role in identifying patients at high risk for post-EVAR complications and the need for greater compliance in surveillance.

Future of Artificial Intelligence in Surgery: A Narrative Review

Artificial intelligence (AI) is the capability of a machine to execute cognitive processes that are typically considered to be functions of the human brain. It is the study of algorithms that enable machines to reason and perform mental tasks, including problem-solving, object and word recognition, and decision-making. Once considered science fiction, AI today is a fact and an increasingly prevalent subject in both academic and popular literature. It is expected to reshape medicine, benefiting both healthcare professionals and patients. Machine learning (ML) is a subset of AI that allows machines to learn and make predictions by recognizing patterns, thus empowering the medical team to deliver better care to patients through accurate diagnosis and treatment. ML is expanding its footprint in a variety of surgical specialties, including general surgery, ophthalmology, cardiothoracic surgery, and vascular surgery, to name a few. In recent years, we have seen AI make its way into the operating theatres. Though it has not yet been able to replace the surgeon, it has the potential to become a highly valuable surgical tool. Rest assured that the day is not far off when AI shall play a significant intraoperative role, a projection that is currently marred by safety concerns. This review aims to explore the present application of AI in various surgical disciplines and how it benefits both patients and physicians, as well as the current obstacles and limitations facing its seemingly unstoppable rise.

Artificial Intelligence-Assisted Sac Diameter Assessment for Complex Endovascular Aortic Repair

PURPOSE

Artificial intelligence (AI) using an automated, deep learning-based method, Augmented Radiology for Vascular Aneurysm (ARVA), has been verified as a viable aide in aneurysm morphology assessment. The aim of this study was to evaluate the accuracy of ARVA when analyzing preoperative and postoperative computed tomography angiography (CTA) in patients managed with fenestrated endovascular repair (FEVAR) for complex aortic aneurysms (cAAs).

MATERIALS AND METHODS

Preoperative and postoperative CTAs from 50 patients (n=100 CTAs) who underwent FEVAR for cAAs were extracted from the picture archiving and communication system (PACS) of a single aortic center equipped with ARVA. All studies underwent automated AI aneurysm morphology assessment by ARVA. Appropriate identification of the outer wall of the aorta was verified by manual review of the AI-generated overlays for each patient. Maximum outer-wall aortic diameters were measured by 2 clinicians using multiplanar reconstruction (MPR) and curved planar reformatting (CPR), and among studies where the aortic wall was appropriately identified by ARVA, they were compared with ARVA automated measurements.

RESULTS

Identification of the outer wall of the aorta was accurate in 89% of CTA studies. Among these, diameter measurements by ARVA were comparable to clinician measurements by MPR or CPR, with a median absolute difference of 2.4 mm on the preoperative CTAs and 1.6 mm on the postoperative CTAs. Of note, no significant difference was detected between clinician measurements using MPR or CPR on preoperative and postoperative scans (range 0.5-0.9 mm).

CONCLUSION

For patients with cAAs managed with FEVAR, ARVA provides accurate preoperative and postoperative assessment of aortic diameter in 89% of studies. This technology may provide an opportunity to automate cAA morphology assessment in most cases where time-intensive, manual clinician measurements are currently required.

CLINICAL IMPACT

In this retrospective analysis of preoperative and postoperative imaging from 50 patients managed with FEVAR, AI provided accurate aortic diameter measurements in 89% of the CTAs reviewed, despite the complexity of the aortic anatomies, and in post-operative CTAs despite metal artifact from stent grafts, markers and embolization materials. Outliers with imprecise automated aortic overlays were easily identified by scrolling through the axial AI-generated segmentation MPR cuts of the entire aorta.This study supports the notion that such emerging AI technologies can improve efficiency of routine clinician workflows while maintaining excellent measurement accuracy when analyzing complex aortic anatomies by CTA.

Machine learning and image analysis in vascular surgery

Deep learning, a subset of machine learning within artificial intelligence, has been successful in medical image analysis in vascular surgery. Unlike traditional computer-based segmentation methods that manually extract features from input images, deep learning methods learn image features and classify data without making prior assumptions. Convolutional neural networks, the main type of deep learning for computer vision processing, are neural networks with multilevel architecture and weighted connections between nodes that can "auto-learn" through repeated exposure to training data without manual input or supervision. These networks have numerous applications in vascular surgery imaging analysis, particularly in disease classification, object identification, semantic segmentation, and instance segmentation. The purpose of this review article was to review the relevant concepts of machine learning image analysis and its application to the field of vascular surgery.

Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model

BACKGROUND AND PURPOSE

Identifying the presence and extent of intracranial thrombi is crucial in selecting patients with acute ischemic stroke for treatment. This article aims to develop an automated approach to quantify thrombus on NCCT and CTA in patients with stroke.

MATERIALS AND METHODS

A total of 499 patients with large-vessel occlusion from the Safety and Efficacy of Nerinetide in Subjects Undergoing Endovascular Thrombectomy for Stroke (ESCAPE-NA1) trial were included. All patients had thin-section NCCT and CTA images. Thrombi contoured manually were used as reference standard. A deep learning approach was developed to segment thrombi automatically. Of 499 patients, 263 and 66 patients were randomly selected to train and validate the deep learning model, respectively; the remaining 170 patients were independently used for testing. The deep learning model was quantitatively compared with the reference standard using the Dice coefficient and volumetric error. The proposed deep learning model was externally tested on 83 patients with and without large-vessel occlusion from another independent trial.

RESULTS

The developed deep learning approach obtained a Dice coefficient of 70.7% (interquartile range, 58.0%-77.8%) in the internal cohort. The predicted thrombi length and volume were correlated with those of expert-contoured thrombi (r = 0.88 and 0.87, respectively; P < .001). When the derived deep learning model was applied to the external data set, the model obtained similar results in patients with large-vessel occlusion regarding the Dice coefficient (66.8%; interquartile range, 58.5%-74.6%), thrombus length (r = 0.73), and volume (r = 0.80). The model also obtained a sensitivity of 94.12% (32/34) and a specificity of 97.96% (48/49) in classifying large-vessel occlusion versus non-large-vessel occlusion.

CONCLUSIONS

The proposed deep learning method can reliably detect and measure thrombi on NCCT and CTA in patients with acute ischemic stroke.

Application of cascaded GAN based on CT scan in the diagnosis of aortic dissection

PURPOSE

Currently, Computed Tomography Angiography (CTA) is the most commonly used clinical method for the diagnosis of aortic dissection, which is much better than plain CT. However, CTA examination has some disadvantages such as time-consuming image processing, complicated procedure and injection of developer. CT plain scanning is widely used in the early diagnosis of arterial dissection because of its convenience, speed and popularity. In order not to delay the optimal diagnosis and treatment time of patients, we use deep learning technology and network model to synthesize plain CT images into CTA images. Patients can be timely professional related departments of clinical diagnosis and treatment, and reduce the rate of missed diagnosis. In this paper, we propose a CTA image synthesis technique for cardiac aortic dissection based on the cascaded generative adjunctive network model.

METHOD

Firstly, we registered CT images, and then used nnU-Net segmentation network model to obtain CT and CTA paired images containing only the aorta. Then we proposed a CTA image synthesis method for aortic dissection based on cascaded generative adversarial. The core idea is to build a cascade generator and double discriminator network based on DCT channel attention mechanism to further enhance the synthesis effect of CTA.

RESULTS

The model is trained and tested on CT plain scan and CTA image data set of aortic dissection. The results show that the proposed model achieves good results in CTA image synthesis. In the CT data set, the nnU-Net model improves 8.63% and reduces 10.87mm errors in the key index DSC and HD, respectively, compared with the benchmark model U-Net. In CTA data set, nnU-Net model improves 10.27% and reduces 6.56mm error in key index DSC and HD, respectively, compared with benchmark model U-Net. In the synthesis task, the cascaded generative adm network is superior to Pix2pix and Pix2pixHD network models in both PSNR and SSIM, which proves that our proposed model has significant advantages.

CONCLUSION

This study provides new possibilities for CTA image synthesis of aortic dissection, and improves the accuracy and efficiency of diagnosis, and hopes to provide substantial help for the diagnosis of aortic dissection.

Deep learning-based medical image segmentation of the aorta using XR-MSF-U-Net

PURPOSE

This paper proposes a CT images and MRI segmentation technology of cardiac aorta based on XR-MSF-U-Net model. The purpose of this method is to better analyze the patient's condition, reduce the misdiagnosis and mortality rate of cardiovascular disease in inhabitants, and effectively avoid the subjectivity and unrepeatability of manual segmentation of heart aorta, and reduce the workload of doctors.

METHOD

We implement the X ResNet (XR) convolution module to replace the different convolution kernels of each branch of two-layer convolution XR of common model U-Net, which can make the model extract more useful features more efficiently. Meanwhile, a plug and play attention module integrating multi-scale features Multi-scale features fusion module (MSF) is proposed, which integrates global local and spatial features of different receptive fields to enhance network details to achieve the goal of efficient segmentation of cardiac aorta through CT images and MRI.

RESULTS

The model is trained on common cardiac CT images and MRI data sets and tested on our collected data sets to verify the generalization ability of the model. The results show that the proposed XR-MSF-U-Net model achieves a good segmentation effect on CT images and MRI. In the CT data set, the XR-MSF-U-Net model improves 7.99% in key index DSC and reduces 11.01 mm in HD compared with the benchmark model U-Net, respectively. In the MRI data set, XR-MSF-U-Net model improves 10.19% and reduces 6.86 mm error in key index DSC and HD compared with benchmark model U-Net, respectively. And it is superior to similar models in segmentation effect, proving that this model has significant advantages.

CONCLUSION

This study provides new possibilities for the segmentation of aortic CT images and MRI, improves the accuracy and efficiency of diagnosis, and hopes to provide substantial help for the segmentation of aortic CT images and MRI.

An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology

Imaging in the emergent setting carries high stakes. With increased demand for dedicated on-site service, emergency radiologists face increasingly large image volumes that require rapid turnaround times. However, novel artificial intelligence (AI) algorithms may assist trauma and emergency radiologists with efficient and accurate medical image analysis, providing an opportunity to augment human decision making, including outcome prediction and treatment planning. While traditional radiology practice involves visual assessment of medical images for detection and characterization of pathologies, AI algorithms can automatically identify subtle disease states and provide quantitative characterization of disease severity based on morphologic image details, such as geometry and fluid flow. Taken together, the benefits provided by implementing AI in radiology have the potential to improve workflow efficiency, engender faster turnaround results for complex cases, and reduce heavy workloads. Although analysis of AI applications within abdominopelvic imaging has primarily focused on oncologic detection, localization, and treatment response, several promising algorithms have been developed for use in the emergency setting. This article aims to establish a general understanding of the AI algorithms used in emergent image-based tasks and to discuss the challenges associated with the implementation of AI into the clinical workflow.

Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning

Background: Computed tomography angiography (CTA) is one of the most commonly used imaging technique for the management of vascular diseases. Here, we aimed to develop a hybrid method combining a feature-based expert system with a supervised deep learning (DL) algorithm to enable a fully automatic segmentation of the abdominal vascular tree. Methods: We proposed an algorithm based on the hybridization of a data-driven convolutional neural network and a knowledge-based model dedicated to vascular system segmentation. By using two distinct datasets of CTA from patients to evaluate independence to training dataset, the accuracy of the hybrid method for lumen and thrombus segmentation was evaluated compared to the feature-based expert system alone and to the ground truth provided by a human expert. Results: The hybrid approach demonstrated a better accuracy for lumen segmentation compared to the expert system alone (volume similarity: 0.8128 vs. 0.7912, p = 0.0006 and Dice similarity coefficient: 0.8266 vs. 0.7942, p < 0.0001). The accuracy for thrombus segmentation was also enhanced using the hybrid approach (volume similarity: 0.9404 vs. 0.9185, p = 0.0027 and Dice similarity coefficient: 0.8918 vs. 0.8654, p < 0.0001). Conclusions: By enabling a robust and fully automatic segmentation, the method could be used to develop real-time decision support to help in the management of vascular diseases.

Artificial intelligence in abdominal aortic aneurysm

OBJECTIVE

Abdominal aortic aneurysm (AAA) is a life-threatening disease, and the only curative treatment relies on open or endovascular repair. The decision to treat relies on the evaluation of the risk of AAA growth and rupture, which can be difficult to assess in practice. Artificial intelligence (AI) has revealed new insights into the management of cardiovascular diseases, but its application in AAA has so far been poorly described. The aim of this review was to summarize the current knowledge on the potential applications of AI in patients with AAA.

METHODS

A comprehensive literature review was performed. The MEDLINE database was searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search strategy used a combination of keywords and included studies using AI in patients with AAA published between May 2019 and January 2000. Two authors independently screened titles and abstracts and performed data extraction. The search of published literature identified 34 studies with distinct methodologies, aims, and study designs.

RESULTS

AI was used in patients with AAA to improve image segmentation and for quantitative analysis and characterization of AAA morphology, geometry, and fluid dynamics. AI allowed computation of large data sets to identify patterns that may be predictive of AAA growth and rupture. Several predictive and prognostic programs were also developed to assess patients' postoperative outcomes, including mortality and complications after endovascular aneurysm repair.

CONCLUSIONS

AI represents a useful tool in the interpretation and analysis of AAA imaging by enabling automatic quantitative measurements and morphologic characterization. It could be used to help surgeons in preoperative planning. AI-driven data management may lead to the development of computational programs for the prediction of AAA evolution and risk of rupture as well as postoperative outcomes. AI could also be used to better evaluate the indications and types of surgical treatment and to plan the postoperative follow-up. AI represents an attractive tool for decision-making and may facilitate development of personalized therapeutic approaches for patients with AAA.

Fundamentals in Artificial Intelligence for Vascular Surgeons

Artificial intelligence (AI) corresponds to a broad discipline that aims to design systems, which display properties of human intelligence. While it has led to many advances and applications in daily life, its introduction in medicine is still in its infancy. AI has created interesting perspectives for medical research and clinical practice but has been sometimes associated with hype leading to a misunderstanding of its real capabilities. Here, we aim to introduce the fundamental notions of AI and to bring an overview of its potential applications for medical and surgical practice. In the limelight of current knowledge, limits and challenges to face as well as future directions are discussed.

Outer Wall Segmentation of Abdominal Aortic Aneurysm by Variable Neighborhood Search Through Intensity and Gradient Spaces

Aortic aneurysm segmentation remains a challenge. Manual segmentation is a time-consuming process which is not practical for routine use. To address this limitation, several automated segmentation techniques for aortic aneurysm have been developed, such as edge detection-based methods, partial differential equation methods, and graph partitioning methods. However, automatic segmentation of aortic aneurysm is difficult due to high pixel similarity to adjacent tissue and a lack of color information in the medical image, preventing previous work from being applicable to difficult cases. This paper uses uses a variable neighborhood search that alternates between intensity-based and gradient-based segmentation techniques. By alternating between intensity and gradient spaces, the search can escape from local optima of each space. The experimental results demonstrate that the proposed method outperforms the other existing segmentation methods in the literature, based on measurements of dice similarity coefficient and jaccard similarity coefficient at the pixel level. In addition, it is shown to perform well for cases that are difficult to segment.

A fully automated pipeline for mining abdominal aortic aneurysm using image segmentation

Imaging software have become critical tools in the diagnosis and the treatment of abdominal aortic aneurysms (AAA). The aim of this study was to develop a fully automated software system to enable a fast and robust detection of the vascular system and the AAA. The software was designed from a dataset of injected CT-scans images obtained from 40 patients with AAA. Pre-processing steps were performed to reduce the noise of the images using image filters. The border propagation based method was used to localize the aortic lumen. An online error detection was implemented to correct errors due to the propagation in anatomic structures with similar pixel value located close to the aorta. A morphological snake was used to segment 2D or 3D regions. The software allowed an automatic detection of the aortic lumen and the AAA characteristics including the presence of thrombus and calcifications. 2D and 3D reconstructions visualization were available to ease evaluation of both algorithm precision and AAA properties. By enabling a fast and automated detailed analysis of the anatomic characteristics of the AAA, this software could be useful in clinical practice and research and be applied in a large dataset of patients.

Prediction of Abdominal Aortic Aneurysm Growth Using Dynamical Gaussian Process Implicit Surface

OBJECTIVE

We propose a novel approach to predict the Abdominal Aortic Aneurysm (AAA) growth in future time, using longitudinal computer tomography (CT) scans of AAAs that are captured at different times in a patient-specific way.

METHODS

We adopt a formulation that considers a surface of the AAA as a manifold embedded in a scalar field over the three dimensional (3D) space. For this formulation, we develop our Dynamical Gaussian Process Implicit Surface (DGPIS) model based on observed surfaces of 3D AAAs as visible variables while the scalar fields are hidden. In particular, we use Gaussian process regression to construct the field as an observation model from CT training image data. We then learn a dynamic model to represent the evolution of the field. Finally, we derive the predicted AAA surface from the predicted field along with uncertainty quantified in future time.

RESULTS

A dataset of 7 subjects (4-7 scans) was collected and used to evaluate the proposed method by comparing its prediction Hausdorff distance errors against those of simple extrapolation. In addition, we evaluate the prediction results with respect to a conventional shape analysis technique such as Principal Component Analysis (PCA). All comparative results show the superior prediction performance of the proposed approach.

CONCLUSION

We introduce a novel approach to predict the AAA growth and its predicted uncertainty in future time, using longitudinal CT scans in a patient-specific fashion.

SIGNIFICANCE

The capability to predict the AAA shape and its confidence region by our approach establish the potential for guiding clinicians with informed decision in conducting medical treatment and monitoring of AAAs.

Segmentation of lumen and outer wall of abdominal aortic aneurysms from 3D black-blood MRI with a registration based geodesic active contour model

Segmentation of the geometric morphology of abdominal aortic aneurysm is important for interventional planning. However, the segmentation of both the lumen and the outer wall of aneurysm in magnetic resonance (MR) image remains challenging. This study proposes a registration based segmentation methodology for efficiently segmenting MR images of abdominal aortic aneurysms. The proposed methodology first registers the contrast enhanced MR angiography (CE-MRA) and black-blood MR images, and then uses the Hough transform and geometric active contours to extract the vessel lumen by delineating the inner vessel wall directly from the CE-MRA. The proposed registration based geometric active contour is applied to black-blood MR images to generate the outer wall contour. The inner and outer vessel wall are then fused presenting the complete vessel lumen and wall segmentation. The results obtained from 19 cases showed that the proposed registration based geometric active contour model was efficient and comparable to manual segmentation and provided a high segmentation accuracy with an average Dice value reaching 89.79%.

Generic thrombus segmentation from pre- and post-operative CTA

PURPOSE

Abdominal aortic aneurysm (AAA) is a localized, permanent and irreversible enlargement of the artery, with the formation of thrombus into the inner wall of the aneurysm. A precise patient-specific segmentation of the thrombus is useful for both the pre-operative planning to estimate the rupture risk, and for post-operative assessment to monitor the disease evolution. This paper presents a generic approach for 3D segmentation of thrombus from patients suffering from AAA using computed tomography angiography (CTA) scans.

METHODS

A fast and versatile thrombus segmentation approach has been developed. It is composed of initial centerline detection and aorta lumen segmentation, an optimized pre-processing stage and the use of a 3D deformable model. The approach has been designed to be very generic and requires minimal user interaction. The proposed method was tested on different datasets with 145 patients overall, including pre- and post-operative CTAs, abdominal aorta and iliac artery sections, different calcification degrees, aneurysm sizes and contrast enhancement qualities.

RESULTS

The thrombus segmentation approach showed very accurate results with respect to manual delineations for all datasets ([Formula: see text] and [Formula: see text] for abdominal aorta sections on pre-operative CTA, iliac artery sections on pre-operative CTAs and aorta sections on post-operative CTA, respectively). Experiments on the different patient and image conditions showed that the method was highly versatile, with no significant differences in term of precision. Comparison with the level-set algorithm also demonstrated the superiority of the 3D deformable model. Average processing time was [Formula: see text].

CONCLUSION

We presented a near-automatic and generic thrombus segmentation algorithm applicable to a large variability of patient and imaging conditions. When integrated in an endovascular planning system, our segmentation algorithm shows its compatibility with clinical routine and could be used for pre-operative planning and post-operative assessment of endovascular procedures.

Implementation and use of 3D pairwise geodesic distance fields for seeding abdominal aortic vessels

PURPOSE

Precise extraction of aorta and the vessels departing from it (i.e. coeliac, renal, and iliac) is vital for correct positioning of a graft prior to abdominal aortic surgery. To perform this task, most of the segmentation algorithms rely on seed points, and better-located seed points provide better initial positions for cross-sectional methods. Under non-optimal acquisition characteristics of daily clinical routine and complex morphology of these vessels, inserting seed points to all these small, but critically important vessels is a tedious, time-consuming, and error-prone task. Thus, in this paper, a novel strategy is developed to generate pathways between user-inserted seed points in order to initialize segmentation methods effectively.

METHOD

The proposed method requires only a single user-inserted seed for each vessel of interest for initializations. Starting from these initial seeds, it automatically generates pathways that span all vessels in between. To accomplish this, first, a geodesic mask is generated by adaptive thresholding, which reinforces the initial seeds to be kept in the vascular tree. Then, a novel implementation of 3D pairwise geodesic distance field (3D-PGDF) is utilized. It is shown that the minimal-valued geodesic of 3D-PGDF successfully defines a path linking the initial seeds as being the shortest geodesic. Moreover, the robustness of the minimum level set of the 3D-PGDF to local variations and regions of high curvature is increased by a region classification strategy, which adds partial geodesics to these critical regions.

RESULTS

The proposed method was applied to 19 challenging CT data sets obtained from four different scanners and compared to two benchmark methods. The first method is a high-precision technique with very long processing time (subvoxel precise multi-stencil fast marching-MSFM), while the second is a very fast method with lower accuracy (3D fast marching). The results, which are obtained using various measures, show that the pathways generated by the developed technique enable significantly higher segmentation performance than 3D fast marching and require much less computational power and time than MSFM.

CONCLUSION

The developed technique offers a useful tool for generating pathways between seed points with minimal user interaction. It guarantees to include all important vessels in a computationally effective manner and thus, it can be used to initialize segmentation methods for abdominal aortic tree.

Skeleton Graph Matching vs. Maximum Weight Cliques aorta registration techniques

Vascular diseases are one of the most challenging health problems in developed countries. Past as well as ongoing research activities often focus on efficient, robust and fast aorta segmentation, and registration techniques. According to this needs our study targets an abdominal aorta registration method. The investigated algorithms make it possible to efficiently segment and register abdominal aorta in pre- and post-operative Computed Tomography (CT) data. In more detail, a registration technique using the Path Similarity Skeleton Graph Matching (PSSGM), as well as Maximum Weight Cliques (MWCs) are employed to realise the matching based on Computed Tomography data. The presented approaches make it possible to match characteristic voxels belonging to the aorta from different Computed Tomography (CT) series. It is particularly useful in the assessment of the abdominal aortic aneurysm treatment by visualising the correspondence between the pre- and post-operative CT data. The registration results have been tested on the database of 18 contrast-enhanced CT series, where the cross-registration analysis has been performed producing 153 matching examples. All the registration results achieved with our system have been verified by an expert. The carried out analysis has highlighted the advantage of the MWCs technique over the PSSGM method. The verification phase proves the efficiency of the MWCs approach and encourages to further develop this methods.

Skeleton-based abdominal aorta registration technique

Vascular diseases are the most challenging health problems in developed countries. The vascular segmentation as well as registration techniques are the topics of past and ongoing research activities. In this work we target an abdominal aorta registration technique. The developed methodology is useful in the assessment of abdominal aortic aneurysm treatment by visualizing the correspondence between pre- and postoperative Computed Tomography (CT) data. The presented approach makes it possible to match all voxels belonging to the aorta from different CT series. It is based on aorta lumen segmentation and graph matching method. To segment the lumen area a hybrid level-set active contour approach is used. The matching step is performed based on a path similarity skeleton graph matching procedure. The registration results have been tested on the database of 8 patients, for which two different contrast-enhanced CT series were acquired. All registration results achieved with our system and verified by an expert prove the efficiency of the approach and encourage to further develop this method.

Estimation of wall properties and wall strength of aortic aneurysms using modern imaging techniques. One more step towards a patient-specific assessment of aneurysm rupture risk

Abdominal aortic aneurysmal disease is a major health problem with rupture representing its main complication accompanied by great mortality. Elective repair is currently performed with mortality rates <3%, based upon size or expansion rate, with a recommended threshold of 5.5 cm maximum diameter or >1cm/year enlargement. It is well established that even small AAAs without indication for surgical repair can experience rupture with catastrophic outcomes whereas larger aneurysms often remain intact for a long period. It is recognized, therefore, that the currently used, maximum diameter criterion can not accurately predict AAAs evolution. There is increasing interest in the role of patient-specific biomechanical profiling of AAA development and rupture. Biomechanically, rupture of a vessel occurs when intravascular forces exceed vessel wall structural endurance. Peak Wall Stress (PWS) has been previously shown to better identify AAAs prone to rupture than maximum diameter, but currently stress analysis takes into account several assumptions that influence results to a large extent and limit their use. Moreover stress represents only one of two determinants of rupture risk according to the biomechanical perspective. Wall strength and mechanical properties on the other hand cannot be assessed in vivo but only ex vivo through mechanical studies with mean values of these parameters taken into account for rupture risk estimations. New possibilities in the field of aortic imaging offer promising tools for the validation and advancement of stress analysis and the in vivo evaluation of AAAs' wall properties and wall strength. Documentation of aortic wall motion during cardiac cycle is now feasible through ECG-gated multi-detector CT imaging offering new possibilities towards an individualized method for rupture risk and expansion-rate predictions based on data acquired in vivo.

Geometrical methods for level set based abdominal aortic aneurysm thrombus and outer wall 2D image segmentation

Abdominal aortic aneurysm (AAA) is a localized dilatation of the aortic wall. Accurate measurements of its geometric characteristics are critical for a reliable estimate of AAA rupture risk. However, current imaging modalities do not provide sufficient contrast to distinguish thrombus from surrounding tissue thus making the task of segmentation quite challenging. The main objective of this paper is to address this problem and accurately extract the thrombus and outer wall boundaries from cross sections of a 3D AAA image data set (CTA). This is achieved by new geometrical methods applied to the boundary curves obtained by a Level Set Method (LSM). Such methods address the problem of leakage of a moving front into sectors of similar intensity and that of the presence of calcifications. The versatility of the methods is tested by creating artificial images which simulate the real cases. Segmentation quality is quantified by comparing the results with a manual segmentation of the slices of ten patient data sets. Sensitivity to the parameter settings and reproducibility are analyzed. This is the first work to our knowledge that utilizes the level set framework to extract both the thrombus and external AAA wall boundaries.

Robust shape regression for supervised vessel segmentation and its application to coronary segmentation in CTA

This paper presents a vessel segmentation method which learns the geometry and appearance of vessels in medical images from annotated data and uses this knowledge to segment vessels in unseen images. Vessels are segmented in a coarse-to-fine fashion. First, the vessel boundaries are estimated with multivariate linear regression using image intensities sampled in a region of interest around an initialization curve. Subsequently, the position of the vessel boundary is refined with a robust nonlinear regression technique using intensity profiles sampled across the boundary of the rough segmentation and using information about plausible cross-sectional vessel shapes. The method was evaluated by quantitatively comparing segmentation results to manual annotations of 229 coronary arteries. On average the difference between the automatically obtained segmentations and manual contours was smaller than the inter-observer variability, which is an indicator that the method outperforms manual annotation. The method was also evaluated by using it for centerline refinement on 24 publicly available datasets of the Rotterdam Coronary Artery Evaluation Framework. Centerlines are extracted with an existing method and refined with the proposed method. This combination is currently ranked second out of 10 evaluated interactive centerline extraction methods. An additional qualitative expert evaluation in which 250 automatic segmentations were compared to manual segmentations showed that the automatically obtained contours were rated on average better than manual contours.

Semiautomatic vessel wall detection and quantification of wall thickness in computed tomography images of human abdominal aortic aneurysms

PURPOSE

Quantitative measurements of wall thickness in human abdominal aortic aneurysms (AAAs) may lead to more accurate methods for the evaluation of their biomechanical environment.

METHODS

The authors describe an algorithm for estimating wall thickness in AAAs based on intensity histograms and neural networks involving segmentation of contrast enhanced abdominal computed tomography images. The algorithm was applied to ten ruptured and ten unruptured AAA image data sets. Two vascular surgeons manually segmented the lumen, inner wall, and outer wall of each data set and a reference standard was defined as the average of their segmentations. Reproducibility was determined by comparing the reference standard to lumen contours generated automatically by the algorithm and a commercially available software package. Repeatability was assessed by comparing the lumen, outer wall, and inner wall contours, as well as wall thickness, made by the two surgeons using the algorithm.

RESULTS

There was high correspondence between automatic and manual measurements for the lumen area (r = 0.978 and r = 0.996 for ruptured and unruptured aneurysms, respectively) and between vascular surgeons (r = 0.987 and r = 0.992 for ruptured and unruptured aneurysms, respectively). The authors' automatic algorithm showed better results when compared to the reference with an average lumen error of 3.69%, which is less than half the error between the commercially available application Simpleware and the reference (7.53%). Wall thickness measurements also showed good agreement between vascular surgeons with average coefficients of variation of 10.59% (ruptured aneurysms) and 13.02% (unruptured aneurysms). Ruptured aneurysms exhibit significantly thicker walls (1.78 +/- 0.39 mm) than unruptured ones (1.48 +/- 0.22 mm), p = 0.044.

CONCLUSIONS

While further refinement is needed to fully automate the outer wall segmentation algorithm, these preliminary results demonstrate the method's adequate reproducibility and low interobserver variability.

Clinical validation of a software for quantitative follow-up of abdominal aortic aneurysm maximal diameter and growth by CT angiography

PURPOSE

To compare the reproducibility and accuracy of abdominal aortic aneurysm (AAA) maximal diameter (D-max) measurements using segmentation software, with manual measurement on double-oblique MPR as a reference standard.

MATERIALS AND METHODS

The local Ethics Committee approved this study and waived informed consent. Forty patients (33 men, 7 women; mean age, 72 years, range, 49-86 years) had previously undergone two CT angiography (CTA) studies within 16 ± 8 months for follow-up of AAA ≥ 35 mm without previous treatment. The 80 studies were segmented twice using the software to calculate reproducibility of automatic D-max calculation on 3D models. Three radiologists reviewed the 80 studies and manually measured D-max on double-oblique MPR projections. Intra-observer and inter-observer reproducibility were calculated by intraclass correlation coefficient (ICC). Systematic errors were evaluated by linear regression and Bland-Altman analyses. Differences in D-max growth were analyzed with a paired Student's t-test.

RESULTS

The ICC for intra-observer reproducibility of D-max measurement was 0.992 (≥ 0.987) for the software and 0.985 (≥ 0.974) and 0.969 (≥ 0.948) for two radiologists. Inter-observer reproducibility was 0.979 (0.954-0.984) for the three radiologists. Mean absolute difference between semi-automated and manual D-max measurements was estimated at 1.1 ± 0.9 mm and never exceeded 5mm.

CONCLUSION

Semi-automated software measurement of AAA D-max is reproducible, accurate, and requires minimal operator intervention.

Three-dimensional blood vessel quantification via centerline deformation

It is clinically important to quantify the geometric parameters of an abnormal vessel, as this information can aid radiologists in choosing appropriate treatments or apparatuses. Centerline and cross-sectional diameters are commonly used to characterize the morphology of vessel in various clinical applications. Due to the existence of stenosis or aneurysm, the associated vessel centerline is unable to truly portray the original, healthy vessel shape and may result in inaccurate quantitative measurement. To remedy such a problem, a novel method using an active tube model is proposed. In the method, a smoothened centerline is determined as the axis of a deformable tube model that is registered onto the vessel lumen. Three types of regions, normal, stenotic, and aneurysmal regions, are defined to classify the vessel segment under-analyzed by use of the algorithm of a cross-sectional-based distance field. The registration process used on the tube model is governed by different region-adaptive energy functionals associated with the classified vessel regions. The proposed algorithm is validated on the 3-D computer-generated phantoms and 3-D rotational digital subtraction angiography (DSA) datasets. Experimental results show that the deformed centerline provides better vessel quantification results compared with the original centerline. It is also shown that the registered model is useful for measuring the volume of aneurysmal regions.

Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning

Multimodality imaging information is regularly used now in radiotherapy treatment planning for cancer patients. The authors are investigating methods to take advantage of all the imaging information available for joint target registration and segmentation, including multimodality images or multiple image sets from the same modality. In particular, the authors have developed variational methods based on multivalued level set deformable models for simultaneous 2D or 3D segmentation of multimodality images consisting of combinations of coregistered PET, CT, or MR data sets. The combined information is integrated to define the overall biophysical structure volume. The authors demonstrate the methods on three patient data sets, including a nonsmall cell lung cancer case with PET/CT, a cervix cancer case with PET/CT, and a prostate patient case with CT and MRI. CT, PET, and MR phantom data were also used for quantitative validation of the proposed multimodality segmentation approach. The corresponding Dice similarity coefficient (DSC) was 0.90 +/- 0.02 (p < 0.0001) with an estimated target volume error of 1.28 +/- 1.23% volume. Preliminary results indicate that concurrent multimodality segmentation methods can provide a feasible and accurate framework for combining imaging data from different modalities and are potentially useful tools for the delineation of biophysical structure volumes in radiotherapy treatment planning.

3D geometric reconstruction of thoracic aortic aneurysms

Background

The thoracic aortic aneurysm (TAA) is a pathology that involves an expansion of the aortic diameter in the thoracic aorta, leading to risk of rupture. Recent studies have suggested that internal wall stress, which is affected by TAA geometry and the presence or absence of thrombus, is a more reliable predictor of rupture than the maximum diameter, the current clinical criterion. Accurate reconstruction of TAA geometry is a crucial step in patient-specific stress calculations.

Methods

In this work, a novel methodology was developed, which combines data from several sets of magnetic resonance (MR) images with different levels of detail and different resolutions. Two sets of images were employed to create the final model, which has the highest level of detail for each component of the aneurysm (lumen, thrombus, and wall). A reference model was built by using a single set of images for comparison. This approach was applied to two patient-specific TAAs in the descending thoracic aorta.

Results

The results of finite element simulations showed differences in stress pattern between the coarse and fine models: higher stress values were found with the coarse model and the differences in predicted maximum wall stress were 30% for patient A and 11% for patient B.

Conclusion

This paper presents a new approach to the reconstruction of an aneurysm model based on the use of several sets of MR images. This enables more accurate representation of not only the lumen but also the wall surface of a TAA taking account of intraluminal thrombus.