Daniel Bystrov

Spine detection and labeling using a parts-based graphical model

The detection and extraction of complex anatomical structures usually involves a trade-off between the complexity of local feature extraction and classification, and the complexity and performance of the subsequent structural inference from the viewpoint of combinatorial optimization. Concerning the latter, computationally efficient methods are of particular interest that return the globally-optimal structure. We present an efficient method for part-based localization of anatomical structures which embeds contextual shape knowledge in a probabilistic graphical model. It allows for robust detection even when some of the part detections are missing. The application scenario for our statistical evaluation is spine detection and labeling in magnetic resonance images.

A new method for robust organ positioning in CT images

Robust initialization is essential for any successful segmentation process of medical images. For CT images, initialization is challenging because quality, appearance, content, and field-of-view of the images are highly variable. Furthermore, high execution speed is desirable, whereas the user tolerance to errors is low in clinical applications. We present a new method for efficient and robust positioning of organs in CT images. It is based on a novel probabilistic atlas that, given a tissue type, describes the probability density of the random variable spatial location. Random sampling is then employed to select the most informative points for matching. We present results on pelvic and abdominal images acquired for radiotherapy planning.

Towards automatic patient positioning and scan planning using continuously moving table MR imaging

A concept is proposed to simplify patient positioning and scan planning to improve ease of use and workflow in MR. After patient preparation in front of the scanner the operator selects the anatomy of interest by a single push‐button action. Subsequently, the patient table is moved automatically into the scanner, while real‐time 3D isotropic low‐resolution continuously moving table scout scanning is performed using patient‐independent MR system settings. With a real‐time organ identification process running in parallel and steering the scanner, the target anatomy can be positioned fully automatically in the scanners sensitive volume. The desired diagnostic examination of the anatomy of interest can be planned and continued immediately using the geometric information derived from the acquired 3D data. The concept was implemented and successfully tested in vivo in 12 healthy volunteers, focusing on the liver as the target anatomy. The positioning accuracy achieved was on the order of several millimeters, which turned out to be sufficient for initial planning purposes. Furthermore, the impact of nonoptimal system settings on the positioning performance, the signal‐to‐noise ratio (SNR), and contrast‐to‐noise ratio (CNR) was investigated. The present work proved the basic concept of the proposed approach as an element of future scan automation. Magn Reson Med, 2009.

Pre- and postoperative MR brain imaging with automatic planning and scanning software in tumor patients: an intraindividual comparative study at 3 Tesla.

To evaluate the feasibility of automatic planning and scanning of brain MR imaging (MRI) protocols on a clinical 3 Tesla system in tumor patients before and after neurosurgical intervention.

Local motion analysis in 4D lung CT using fast groupwise registration

Automatic tools for the propagation of contours and surfaces in time series of CT images (4DCT) are necessary e.g. for dose calculation in radio-therapy-planning. The state of the art method is the registration of 3-D frames of the sequence on a static reference volume or the registration of consecutive volume pairs. Based on the computed vector fields, the contours and surfaces are then propagated through the entire 4-D series. However, the registrations frequently suffer from common image or gating artefacts in the time series. To overcome the problem, we propose a groupwise 4-D registration approach combined with a motion model to perform the registration of a sequence of images.

Motion compensation and plane tracking for kinematic MR-Imaging

The acquisition of time series of 3D MR images is becoming feasible nowadays, which enables the assessment of bone and soft tissue in normal and abnormal joint motion. Fast two-dimensional (2D) scanning of moving joints may also provide high temporal resolution but is limited to a single, predefined slice. Acquiring 3D time series has the advantage that after the acquisition image processing and visualization techniques can be used to reformat the images to any orientation and to reduce the through-plane motion and undesired gross motion superimposed on the relevant joint movement. In this publication, we first review such post-processing techniques for retrospective tracking of viewing planes according to a single moving rigid body (e.g. bone). Then, we present new numerical schemes for optimally tracking viewing planes according to the movement of multiple structures to compensate for their through- as well as in-plane motion. These structures can be specified in an interactive viewing program, and the motion compensated movies can be updated and displayed in real-time. The post-processing algorithms require a 4D motion-field estimation which also can be utilized to interpolate intermediate images to present the final movies in smooth cine-loops and to significantly improve the visual perceptibility of complex joint movement.

Annotation-free probabilistic atlas learning for robust anatomy detection in CT images

A fully automatic method generating a whole body atlas from CT images is presented. The atlas serves as a reference space for annotations. It is based on a large collection of partially overlapping medical images and a registration scheme. The atlas itself consists of probabilistic tissue type maps and can represent anatomical variations. The registration scheme is based on an entropy-like measure of these maps and is robust with respect to field-of-view variations. In contrast to other atlas generation methods, which typically rely on a sufficiently large set of annotations on training cases, the presented method requires only the images. An iterative refinement strategy is used to automatically stitch the images to build the atlas. Affine registration of unseen CT images to the probabilistic atlas can be used to transfer reference annotations, e.g. organ models for segmentation initialization or reference bounding boxes for field-of-view selection. The robustness and generality of the method is shown using a three-fold cross-validation of the registration on a set of 316 CT images of unknown content and large anatomical variability. As an example, 17 organs are annotated in the atlas reference space and their localization in the test images is evaluated. The method yields a recall (sensitivity), specificity and precision of at least 96% and thus performs excellent in comparison to competitors.

Precise anatomy localization in CT data by an improved probabilistic tissue type atlas

Automated interpretation of CT scans is an important, clinically relevant area as the number of such scans is increasing rapidly and the interpretation is time consuming. Anatomy localization is an important prerequisite for any such interpretation task. This can be done by image-to-atlas registration, where the atlas serves as a reference space for annotations such as organ probability maps. Tissue type based atlases allow fast and robust processing of arbitrary CT scans. Here we present two methods which significantly improve organ localization based on tissue types. A first problem is the definition of tissue types, which until now is done heuristically based on experience. We present a method to determine suitable tissue types from sample images automatically. A second problem is the restriction of the transformation space: all prior approaches use global affine maps. We present a hierarchical strategy to refine this global affine map. For each organ or region of interest a localized tissue type atlas is computed and used for a subsequent local affine registration step. A three-fold cross validation on 311 CT images with different fields-of-view demonstrates a reduction of the organ localization error by 33%.

Automatic knee cartilage delineation using inheritable segmentation

We present a fully automatic method for segmentation of knee joint cartilage from fat suppressed MRI. The method first applies 3-D model-based segmentation technology, which allows to reliably segment the femur, patella, and tibia by iterative adaptation of the model according to image gradients. Thin plate spline interpolation is used in the next step to position deformable cartilage models for each of the three bones with reference to the segmented bone models. After initialization, the cartilage models are fine adjusted by automatic iterative adaptation to image data based on gray value gradients. The method has been validated on a collection of 8 (3 left, 5 right) fat suppressed datasets and demonstrated the sensitivity of 83±6% compared to manual segmentation on a per voxel basis as primary endpoint. Gross cartilage volume measurement yielded an average error of 9±7% as secondary endpoint. For cartilage being a thin structure, already small deviations in distance result in large errors on a per voxel basis, rendering the primary endpoint a hard criterion.