Johannes Feulner

Hierarchical parsing and semantic navigation of full body CT data

Whole body CT scanning is a common diagnosis technique for discovering early signs of metastasis or for differential diagnosis. Automatic parsing and segmentation of multiple organs and semantic navigation inside the body can help the clinician in efficiently obtaining accurate diagnosis. However, dealing with the large amount of data of a full body scan is challenging and techniques are needed for the fast detection and segmentation of organs, e.g., heart, liver, kidneys, bladder, prostate, and spleen, and body landmarks, e.g., bronchial bifurcation, coccyx tip, sternum, lung tips. Solving the problem becomes even more challenging if partial body scans are used, where not all organs are present. We propose a new approach to this problem, in which a network of 1D and 3D landmarks is trained to quickly parse the 3D CT data and estimate which organs and landmarks are present as well as their most probable locations and boundaries. Using this approach, the segmentation of seven organs and detection of 19 body landmarks can be obtained in about 20 seconds with state-of-the-art accuracy and has been validated on 80 CT full or partial body scans.

Automatic Localization and Identification of Vertebrae in Arbitrary Field-of-View CT Scans

This paper presents a new method for automatic localization and identification of vertebrae in arbitrary field-of-view CT scans. No assumptions are made about which section of the spine is visible or to which extent. Thus, our approach is more general than previous work while being computationally efficient. Our algorithm is based on regression forests and probabilistic graphical models. The discriminative, regression part aims at roughly detecting the visible part of the spine. Accurate localization and identification of individual vertebrae is achieved through a generative model capturing spinal shape and appearance. The system is evaluated quantitatively on 200 CT scans, the largest dataset reported for this purpose. We obtain an overall median localization error of less than 6mm, with an identification rate of 81%.

Lymph node detection and segmentation in chest CT data using discriminative learning and a spatial prior

Lymph nodes have high clinical relevance and routinely need to be considered in clinical practice. Automatic detection is, however, challenging due to clutter and low contrast. In this paper, a method is presented that fully automatically detects and segments lymph nodes in 3-D computed tomography images of the chest. Lymph nodes can easily be confused with other structures, it is therefore vital to incorporate as much anatomical prior knowledge as possible in order to achieve a good detection performance. Here, a learned prior of the spatial distribution is used to model this knowledge. Different prior types with increasing complexity are proposed and compared to each other. This is combined with a powerful discriminative model that detects lymph nodes from their appearance. It first generates a number of candidates of possible lymph node center positions. Then, a segmentation method is initialized with a detected candidate. The graph cuts method is adapted to the problem of lymph nodes segmentation. We propose a setting that requires only a single positive seed and at the same time solves the small cut problem of graph cuts. Furthermore, we propose a feature set that is extracted from the segmentation. A classifier is trained on this feature set and used to reject false alarms. Cross-validation on 54 CT datasets showed that for a fixed number of four false alarms per volume image, the detection rate is well more than doubled when using the spatial prior. In total, our proposed method detects mediastinal lymph nodes with a true positive rate of 52.0% at the cost of only 3.1 false alarms per volume image and a true positive rate of 60.9% with 6.1 false alarms per volume image, which compares favorably to prior work on mediastinal lymph node detection.

Lymph node detection in 3-D chest CT using a spatial prior probability

Lymph nodes have high clinical relevance but detection is challenging as they are hard to see due to low contrast and irregular shape. In this paper, a method for fully automatic mediastinal lymph node detection in 3-D computed tomography (CT) images of the chest area is proposed. Discriminative learning is used to detect lymph nodes based on their appearance. Because lymph nodes can easily be confused with other structures, it is vital to incorporate as much anatomical knowledge as possible to achieve good detection rates. Here, a learned prior of the spatial distribution is proposed to model this knowledge. As atlas matching is generally inaccurate in the chest area because of anatomical variations, this prior is not learned in the space of a single atlas, but in the space of multiple ones that are attached to anatomical structures. During test, the priors are weighted and merged according to spatial distances. Cross-validation on 54 CT datasets showed that the prior based detector yields a true positive rate of 52.3% for seven false positives per volume image, which is about two times better than without a spatial prior.

Estimating the body portion of CT volumes by matching histograms of visual words

Being able to automatically determine which portion of the human body is shown by a CT volume image offers various possibilities like automatic labeling of images or initializing subsequent image analysis algorithms. This paper presents a method that takes a CT volume as input and outputs the vertical body coordinates of its top and bottom slice in a normalized coordinate system whose origin and unit length are determined by anatomical landmarks. Each slice of a volume is described by a histogram of visual words: Feature vectors consisting of an intensity histogram and a SURF descriptor are first computed on a regular grid and then classified into the closest visual words to form a histogram. The vocabulary of visual words is a quantization of the feature space by offline clustering a large number of feature vectors from prototype volumes into visual words (or cluster centers) via the K-Means algorithm. For a set of prototype volumes whose body coordinates are known the slice descriptions are computed in advance. The body coordinates of a test volume are computed by a 1D rigid registration of the test volume with the prototype volumes in axial direction. The similarity of two slices is measured by comparing their histograms of visual words. Cross validation on a dataset of 44 volumes proved the robustness of the results. Even for test volumes of ca. 20cm height, the average error was 15.8mm.

A Probabilistic Model for Automatic Segmentation of the Esophagus in 3-D CT Scans

Being able to segment the esophagus without user interaction from 3-D CT data is of high value to radiologists during oncological examinations of the mediastinum. The segmentation can serve as a guideline and prevent confusion with pathological tissue. However, limited contrast to surrounding structures and versatile shape and appearance make segmentation a challenging problem. This paper presents a multistep method. First, a detector that is trained to learn a discriminative model of the appearance is combined with an explicit model of the distribution of respiratory and esophageal air. In the next step, prior shape knowledge is incorporated using a Markov chain model. We follow a “detect and connect” approach to obtain the maximum a posteriori estimate of the approximate esophagus shape from hypothesis about the esophagus contour in axial image slices. Finally, the surface of this approximation is nonrigidly deformed to better fit the boundary of the organ. The method is compared to an alternative approach that uses a particle filter instead of a Markov chain to infer the approximate esophagus shape, to the performance of a human observer and also to state of the art methods, which are all semiautomatic. Cross-validation on 144 CT scans showed that the Markov chain based approach clearly outperforms the particle filter. It segments the esophagus with a mean error of 1.80 mm in less than 16 s on a standard PC. This is only 1 mm above the interobserver variability and can compete with the results of previously published semiautomatic methods.

Comparing axial CT slices in quantized N-dimensional SURF descriptor space to estimate the visible body region

In this paper, a method is described to automatically estimate the visible body region of a computed tomography (CT) volume image. In order to quantify the body region, a body coordinate (BC) axis is used that runs in longitudinal direction. Its origin and unit length are patient-specific and depend on anatomical landmarks. The body region of a test volume is estimated by registering it only along the longitudinal axis to a set of reference CT volume images with known body coordinates. During these 1D registrations, an axial image slice of the test volume is compared to an axial slice of a reference volume by extracting a descriptor from both slices and measuring the similarity of the descriptors. A slice descriptor consists of histograms of visual words. Visual words are code words of a quantized feature space and can be thought of as classes of image patches with similar appearance. A slice descriptor is formed by sampling a slice on a regular 2D grid and extracting a Speeded Up Robust Features (SURF) descriptor at each sample point. The codebook, or visual vocabulary, is generated in a training step by clustering SURF descriptors. Each SURF descriptor extracted from a slice is classified into the closest visual word (or cluster center) and counted in a histogram. A slice is finally described by a spatial pyramid of such histograms. We introduce an extension of the SURF descriptors to an arbitrary number of dimensions (N-SURF). Here, we make use of 2-SURF and 3-SURF descriptors. Cross-validation on 84 datasets shows the robustness of the results. The body portion can be estimated with an average error of 15.5mm within 9s. Possible applications of this method are automatic labeling of medical image databases and initialization of subsequent image analysis algorithms.

Model-based esophagus segmentation from CT scans using a spatial probability map

Automatic segmentation of the esophagus from CT data is a challenging problem. Its wall consists of muscle tissue, which has low contrast in CT. Sometimes it is filled with air or remains of orally given contrast agent. While air holes are a clear hint to a human when searching for the esophagus, we found that they are rather distracting to discriminative models of the appearance because of their similarity to the trachea and to lung tissue. However, air inside the respiratory organs can be segmented easily. In this paper, we propose to combine a model based segmentation algorithm of the esophagus with a spatial probability map generated from detected air. Threefold cross-validation on 144 datasets showed that this probability map, combined with a technique that puts more focus on hard cases, increases accuracy by 22%. In contrast to prior work, our method is not only automatic on a manually selected region of interest, but on a whole thoracic CT scan, while our mean segmentation error of 1.80mm is even better.

Robust real-time 3D modeling of static scenes using solely a Time-of-Flight sensor

An algorithm is proposed for the 3D modeling of static scenes solely based on the range and intensity data acquired by a time-of-flight camera during an arbitrary movement. No additional scene acquisition devices, like inertia sensor, positioning robots or intensity based cameras are incorporated. The current pose is estimated by maximizing the uncentered correlation coefficient between edges detected in the current and a preceding frame at a minimum frame rate of four fps and an average accuracy of 45 mm. The paper also describes several extensions for robust registration like multiresolution hierarchies and projection Iterative Closest Point algorithm. The basic registration algorithm and its extensions were intensively evaluated against ground truth data to validate the accuracy, robustness and real-time-capability.

Segmentation based features for lymph node detection from 3-D chest CT

Lymph nodes routinely need to be considered in clinical practice in all kinds of oncological examinations. Automatic detection of lymph nodes from chest CT data is however challenging because of low contrast and clutter. Sliding window detectors using traditional features easily get confused by similar structures like muscles and vessels. It recently has been proposed to combine segmentation and detection to improve the detection performance. Features extracted from a segmentation that is initialized with a detection candidate can be used to train a classifier that decides whether the detection is a true or false positive. In this paper, the graph cuts method is adapted to the problem of lymph nodes segmentation. We propose a setting that requires only a single positive seed and at the same time solves the small cut problem of graph cuts. Furthermore, we propose a feature set that is extracted from the candidate segmentation. A classifier is trained on this feature set and used to reject false alarms. Cross validation on 54 CT datasets showed that the proposed system reaches a detection rate of 60.9% with only 6.1 false alarms per volume image, which is better than the current state of the art of mediastinal lymph node detection.