Andreas Wimmer

A Generic Probabilistic Active Shape Model for Organ Segmentation

Probabilistic models are extensively used in medical image segmentation. Most of them employ parametric representations of densities and make idealizing assumptions, e.g., normal distribution of data. Often, such assumptions are inadequate and limit a broader application. We propose here a novel probabilistic active shape model for organ segmentation, which is entirely built upon non-parametric density estimates. In particular, a nearest neighbor boundary appearance model is complemented by a cascade of boosted classifiers for region information and combined with a shape model based on Parzen density estimation. Image and shape terms are integrated into a single level set equation. Our approach has been evaluated for 3-D liver segmentation using a public data base originating from a competition (http://sliver07.org). With an average surface distance of 1.0 mm and an average volume overlap error of 6.5%, it outperforms other automatic methods and provides accuracy close to interactive ones. Since no adaptions specific to liver segmentation have been made, our probabilistic active shape model can be applied to other segmentation tasks easily.

A learning based deformable template matching method for automatic rib centerline extraction and labeling in CT images

The automatic extraction and labeling of the rib centerlines is a useful yet challenging task in many clinical applications. In this paper, we propose a new approach integrating rib seed point detection and template matching to detect and identify each rib in chest CT scans. The bottom-up learning based detection exploits local image cues and top-down deformable template matching imposes global shape constraints. To adapt to the shape deformation of different rib cages whereas maintain high computational efficiency, we employ a Markov Random Field (MRF) based articulated rigid transformation method followed by Active Contour Model (ACM) deformation. Compared with traditional methods that each rib is individually detected, traced and labeled, the new approach is not only much more robust due to prior shape constraints of the whole rib cage, but removes tedious post-processing such as rib pairing and ordering steps because each rib is automatically labeled during the template matching. For experimental validation, we create an annotated database of 112 challenging volumes with ribs of various sizes, shapes, and pathologies such as metastases and fractures. The proposed approach shows orders of magnitude higher detection and labeling accuracy than state-of-the-art solutions and runs about 40 seconds for a complete rib cage on the average.

Detection and Visualization of Endoleaks in CT Data for Monitoring of Thoracic and Abdominal Aortic Aneurysm Stents

In this paper we present an efficient algorithm for the segmentation of the inner and outer boundary of thoratic and abdominal aortic aneurysms (TAA & AAA) in computed tomography angiography (CTA) acquisitions. The aneurysm segmentation includes two steps: first, the inner boundary is segmented based on a grey level model with two thresholds; then, an adapted active contour model approach is applied to the more complicated outer boundary segmentation, with its initialization based on the available inner boundary segmentation. An opacity image, which aims at enhancing important features while reducing spurious structures, is calculated from the CTA images and employed to guide the deformation of the model. In addition, the active contour model is extended by a constraint force that prevents intersections of the inner and outer boundary and keeps the outer boundary at a distance, given by the thrombus thickness, to the inner boundary. Based upon the segmentation results, we can measure the aneurysm size at each centerline point on the centerline orthogonal multiplanar reformatting (MPR) plane. Furthermore, a 3D TAA or AAA model is reconstructed from the set of segmented contours, and the presence of endoleaks is detected and highlighted. The implemented method has been evaluated on nine clinical CTA data sets with variations in anatomy and location of the pathology and has shown promising results.

Combined cardiac and respiratory motion compensation for atrial fibrillation ablation procedures

Catheter ablation of atrial fibrillation has become an accepted treatment option if a patient no longer responds to or tolerates drug therapy. A main goal is the electrical isolation of the pulmonary veins attached to the left atrium. Catheter ablation may be performed under fluoroscopic image guidance. Due to the rather low soft-tissue contrast of X-ray imaging, the heart is not visible in these images. To overcome this problem, overlay images from pre-operative 3-D volumetric data can be used to add anatomical detail. Unfortunately, this overlay is compromised by respiratory and cardiac motion. In the past, two methods have been proposed to perform motion compensation. The first approach involves tracking of a circumferential mapping catheter placed at an ostium of a pulmonary vein. The second method relies on a motion estimate obtained by localizing an electrode of the coronary sinus (CS) catheter. We propose a new motion compensation scheme which combines these two methods. The effectiveness of the proposed method is verified using 19 real clinical data sets. The motion in the fluoroscopic images was estimated with an overall average error of 0.55 mm by tracking the circumferential mapping catheter. By applying an algorithm involving both the CS catheter and the circumferential mapping catheter, we were able to detect motion of the mapping catheter from one pulmonary vein to another with a false positive rate of 5.8 %.

Automatic Liver Segmentation Using the Random Walker Algorithm

In this paper we present a new method for fully automatic liver segmentation in computed tomography images. First, an initial set of seed points for the random walker algorithm is created. In this context, voxels belonging to air, fat tissue and ribcage are labeled as background. Furthermore, depending on the shape of the ribcage and voxel intensities, several seed points inside the liver are automatically selected as foreground. This seed mask is then used to initialize the segmentation algorithm. Our method was successfully tested on data of 22 patients.

Implicit active shape model employing boundary classifier

We present an algorithm for the segmentation of the liver in 2-D computed tomography slice images. The basis for our algorithm is an implicit active shape model. In order to detect the liver boundary and guide the shape model deformation, a boundary classifier has been integrated into the implicit framework in a novel manner. The accuracy of the algorithm has been evaluated for 20 test cases including both normal and abnormal livers.

Constrained 2-D/3-D registration for motion compensation in AFib ablation procedures

Fluoroscopic overlay images rendered from pre-operative volumetric data can provide additional guidance for physicians during catheter ablation procedures for treatment of atrial fibrillation (AFib). As these overlay images are compromised by cardiac and respiratory motion, motion compensation methods have been proposed. The approaches so far either require simultaneous biplane imaging for 3-D motion compensation or, in case of mono-plane X-ray imaging, provide only a limited 2-D functionality. To overcome the downsides of the previously suggested methods, we propose a new approach that facilitates full 3-D motion compensation even if only mono-plane X-ray views are available. To this end, we use constrained model-based 2-D/3-D registration to track a circumferential mapping catheter which is commonly used during AFib catheter ablation procedures. Our approach yields an average 2-D tracking error of 0.6 mm and an average 3-D tracking error of 2.1 mm.

DARWIN: Deformable Patient Avatar Representation With Deep Image Network

In this paper, we present a technical approach to robustly estimate the detailed patient body surface mesh under clothing cover from a single snapshot of a range sensor. Existing methods either lack level of detail of the estimated patient body model, fail to estimate the body model robustly under clothing cover, or lack sufficient evaluation over real patient datasets. In this work, we overcome these limitations by learning deep convolutional networks over real clinical dataset with large variation and augmentation. Our approach is validated with experiments conducted over 1063 human subjects from 3 different hospitals and surface errors are measured against groundtruth from CT data.

Multimodal Image Registration with Deep Context Reinforcement Learning

Automatic and robust registration between real-time patient imaging and pre-operative data (e.g. CT and MRI) is crucial for computer-aided interventions and AR-based navigation guidance. In this paper, we present a novel approach to automatically align range image of the patient with pre-operative CT images. Unlike existing approaches based on the surface similarity optimization process, our algorithm leverages the contextual information of medical images to resolve data ambiguities and improve robustness. The proposed algorithm is derived from deep reinforcement learning algorithm that automatically learns to extract optimal feature representation to reduce the appearance discrepancy between these two modalities. Quantitative evaluations on 1788 pairs of CT and depth images from real clinical setting demonstrate that the proposed method achieves the state-of-the-art performance.

Data driven editing of RIB centerlines

Rib Unfolding refers to the flattening of the rib cage into a two dimensional image that enables the radiologist to quickly examine all ribs for metastases and fractures without having to scroll through every single image in the CT scans. The rib centerline extraction is the core component of the rib unfolding technology. If the computed centerlines deviate from the true centerlines of the ribs then the unfolding shows the bone cortex or even surrounding tissues, making it impossible to spot lesions inside the ribs. Therefore, this paper presents an interactive system for data driven editing of rib centerlines. The user simply has to place a click point at the center of a rib. From there, the centerline is automatically corrected in both directions until the distance to the old one is below a threshold. We formulate the interactive data driven editing as an energy minimization problem where the new centerline point is calculated as the center of mass of the segmentation mask in the plane orthogonal to the rib centerline, this segmentation mask represent the rib cross section. An automatic tracing scheme is calculated based on the refined centerline point and along the tangent vector to the refined centerlines to get the next orthogonal plane to be segmented. The novelty of this paper is three fold: 1. It defines an intuitive minimal user interaction workflow for the correction of rib centerlines. 2. It uses the data in the neighborhood of the centerline point to formulate the energy minimization problem and obtain a more accurate segmentation. 3. We present a novel re-initialization component to prevent the rib tracing from deviating from the correct solution. The initial point to be corrected is re-initialized based on the neighboring ribs. Quantitative assessment of our method shows that in 85% percent of the cases the rib correction can be perfomed using one or two points.