Tanja Kurzendorfer

Fully automatic segmentation of left ventricular anatomy in 3-D LGE-MRI

The current challenge for electrophysiology procedures, targeting the left ventricle, is the localization and qualification of myocardial scar. Late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the current gold standard to visualize regions of myocardial infarction. Commonly, a stack of 2-D images is acquired of the left ventricle in short-axis orientation. Recently, 3-D LGE-MRI methods were proposed that continuously cover the whole heart with a high resolution within a single acquisition. The acquisition promises an accurate quantification of the myocardium to the extent of myocardial scarring. The major challenge arises in the analysis of the resulting images, as the accurate segmentation of the myocardium is a requirement for a precise scar tissue quantification. In this work, we propose a novel approach for fully automatic left ventricle segmentation in 3-D whole-heart LGE-MRI, to address this limitation. First, a two-step registration is performed to initialize the left ventricle. In the next step, the principal components are computed and a pseudo short axis view of the left ventricle is estimated. The refinement of the endocardium and epicardium is performed in polar space. Prior knowledge for shape and inter-slice smoothness is used during segmentation. The proposed method was evaluated on 30 clinical 3-D LGE-MRI datasets from individual patients obtained at two different clinical sites and were compared to gold standard segmentations of two clinical experts. This comparison resulted in a Dice coefficient of 0.83 for the endocardium and 0.80 for the epicardium.

Cryo-balloon catheter localization in fluoroscopic images

Minimally invasive catheter ablation has become the preferred treatment option for atrial fibrillation. Although the standard ablation procedure involves ablation points set by radio-frequency catheters, cryo-balloon catheters have even been reported to be more advantageous in certain cases. As electro-anatomical mapping systems do not support cryo-balloon ablation procedures, X-ray guidance is needed. However, current methods to provide support for cryo-balloon catheters in fluoroscopically guided ablation procedures rely heavily on manual user interaction. To improve this, we propose a first method for automatic cryo-balloon catheter localization in fluoroscopic images based on a blob detection algorithm. Our method is evaluated on 24 clinical images from 17 patients. The method successfully detected the cryoballoon in 22 out of 24 images, yielding a success rate of 91.6 %. The successful localization achieved an accuracy of 1.00 mm ± 0.44 mm. Even though our methods currently fails in 8.4 % of the images available, it still offers a significant improvement over manual methods. Furthermore, detecting a landmark point along the cryo-balloon catheter can be a very important step for additional post-processing operations.

Navigation for fluoroscopy-guided cryo-balloon ablation procedures of atrial fibrillation

Atrial fibrillation (AFib), the most common arrhythmia, has been identified as a major cause of stroke. The current standard in interventional treatment of AFib is the pulmonary vein isolation (PVI). PVI is guided by fluoroscopy or non-fluoroscopic electro-anatomic mapping systems (EAMS). Either classic point-to-point radio-frequency (RF)- catheter ablation or so-called single-shot-devices like cryo-balloons are used to achieve electrically isolation of the pulmonary veins and the left atrium (LA). Fluoroscopy-based systems render overlay images from pre-operative 3-D data sets which are then merged with fluoroscopic imaging, thereby adding detailed 3-D information to conventional fluoroscopy. EAMS provide tracking and visualization of RF catheters by means of electro-magnetic tracking. Unfortunately, current navigation systems, fluoroscopy-based or EAMS, do not provide tools to localize and visualize single shot devices like cryo-balloon catheters in 3-D. We present a prototype software for fluoroscopy-guided ablation procedures that is capable of superimposing 3-D datasets as well as reconstructing cyro-balloon catheters in 3-D. The 3-D cyro-balloon reconstruction was evaluated on 9 clinical data sets, yielded a reprojected 2-D error of 1.72 mm ± 1.02 mm.

Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair

PurposeFusion of preoperative data with intraoperative X-ray images has proven the potential to reduce radiation exposure and contrast agent, especially for complex endovascular aortic repair (EVAR). Due to patient movement and introduced devices that deform the vasculature, the fusion can become inaccurate. This is usually detected by comparing the preoperative information with the contrasted vessel. To avoid repeated use of iodine, comparison with an implanted stent can be used to adjust the fusion. However, detecting the stent automatically without the use of contrast is challenging as only thin stent wires are visible.MethodWe propose a fast, learning-based method to segment aortic stents in single uncontrasted X-ray images. To this end, we employ a fully convolutional network with residual units. Additionally, we investigate whether incorporation of prior knowledge improves the segmentation.ResultsWe use 36 X-ray images acquired during EVAR for training and evaluate the segmentation on 27 additional images. We achieve a Dice coefficient of 0.933 (AUC 0.996) when using X-ray alone, and 0.918 (AUC 0.993) and 0.888 (AUC 0.99) when adding the preoperative model, and information about the expected wire width, respectively.ConclusionThe proposed method is fully automatic, fast and segments aortic stent grafts in fluoroscopic images with high accuracy. The quality and performance of the segmentation will allow for an intraoperative comparison with the preoperative information to assess the accuracy of the fusion.

Automated left ventricle segmentation in 2-D LGE-MRI

For electrophysiology procedures, obtaining the information of scar within the left ventricle is very important for diagnosis, therapy planning and patient prognosis. The clinical gold standard to visualize scar is late-gadolinium-enhanced-MRI (LGE-MRI). The viability assessment of the myocardium often requires the prior segmentation of the left ventricle (LV). To overcome this problem, we propose an approach for fully automatic LV segmentation in 2-D LGE-MRI. First, the LV is automatically detected using circular Hough transforms. Second, the blood pool is approximated by applying a morphological active contours approach. The refinement of the endo- and epicardial contours is performed in polar space, considering the edge information and scar distribution. The proposed method was evaluated on 26 clinical LGE-MRI data sets. This comparison resulted in a Dice coefficient of 0.85 ± 0.06 for the endocardium and 0.84 ± 0.06 for the epicardium.

Cryo-Balloon Catheter Localization Based on a Support-Vector-Machine Approach

Cryo-balloon catheters have attracted an increasing amount of interest in the medical community as they can reduce patient risk during left atrial pulmonary vein ablation procedures. As cryo-balloon catheters are not equipped with electrodes, they cannot be localized automatically by electro-anatomical mapping systems. As a consequence, X-ray fluoroscopy has remained an important means for guidance during the procedure. Most recently, image guidance methods for fluoroscopy-based procedures have been proposed, but they provide only limited support for cryo-balloon catheters and require significant user interaction. To improve this situation, we propose a novel method for automatic cryo-balloon catheter detection in fluoroscopic images by detecting the cryo-balloon catheters built-in X-ray marker. Our approach is based on a blob detection algorithm to find possible X-ray marker candidates. Several of these candidates are then excluded using prior knowledge. For the remaining candidates, several catheter specific features are introduced. They are processed using a machine learning approach to arrive at the final X-ray marker position. Our method was evaluated on 75 biplane fluoroscopy images from 40 patients, from two sites, acquired with a biplane angiography system. The method yielded a success rate of 99.0% in plane A and 90.6% in plane B, respectively. The detection achieved an accuracy of 1.00 mm±0.82 mm in plane A and 1.13 mm±0.24 mm in plane B. The localization in 3-D was associated with an average error of 0.36 mm±0.86 mm.

3D/2D model-to-image registration by imitation learning for cardiac procedures

PurposeIn cardiac interventions, such as cardiac resynchronization therapy (CRT), image guidance can be enhanced by involving preoperative models. Multimodality 3D/2D registration for image guidance, however, remains a significant research challenge for fundamentally different image data, i.e., MR to X-ray. Registration methods must account for differences in intensity, contrast levels, resolution, dimensionality, field of view. Furthermore, same anatomical structures may not be visible in both modalities. Current approaches have focused on developing modality-specific solutions for individual clinical use cases, by introducing constraints, or identifying cross-modality information manually. Machine learning approaches have the potential to create more general registration platforms. However, training image to image methods would require large multimodal datasets and ground truth for each target application.MethodsThis paper proposes a model-to-image registration approach instead, because it is common in image-guided interventions to create anatomical models for diagnosis, planning or guidance prior to procedures. An imitation learning-based method, trained on 702 datasets, is used to register preoperative models to intraoperative X-ray images.ResultsAccuracy is demonstrated on cardiac models and artificial X-rays generated from CTs. The registration error was

Multiple Device Segmentation for Fluoroscopic Imaging Using Multi-task Learning

2.92\pm 2.22\,\hbox { mm}