Machine Learning Methods in Computed Tomography Image Analysis
Lymph nodes have high clinical relevance because they are often affected by cancer, and also play an important role in all kinds of infections and inflammations in general. Lymph nodes are commonly examined using computed tomography (CT).
Manually counting and measuring lymph nodes in CT volumes images is not only cumbersome but also introduces the problem of inter-observer variability and even intra-observer variability. Automatic detection is however challenging as lymph nodes are hard to see due to low contrast, irregular shape, and clutter. In this work, a top-down approach for lymph node detection in 3-D CT volume images is proposed. The focus is put on lymph nodes that lie in the region of the mediastinum.
CT volumes that show the mediastinum are typically scans of the thorax or even the whole thoracic and abdominal region. Therefore, the first step of the method proposed in this work is to determine the visible portion of the body from a CT volume. This allows pruning the search space for mediastinal lymph nodes and also other structures of interest. Furthermore, it can tell whether the mediastinum is actually visible. The visible body region of an unknown test volume is determined by 1-D registration along the longitudinal axis with a number of reference volumes whose body regions are known. A similarity measure for axial CT slices is proposed that has its origin in scene classification. An axial slice is described by a spatial pyramid of histograms of visual words, which are code words of a quantized feature space. The similarity of two slices is measured by comparing their histograms. As features, descriptors of the Speeded Up Robust Features are used. This work proposes 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.
The mediastinal body region contains a number of structures that can be confused with lymph nodes. One of them is the esophagus. Its attenuation coefficient is usually similar, and at the same time it is often surrounded by lymph nodes. Therefore, knowing the outline of the esophagus both helps to reduce false alarms in lymph node detection, and to put focus on the neighborhood. In the second part of this work, a fully automatic method for segmenting the esophagus in 3-D CT images is proposed. Esophagus segmentation is a challenging problem due to limited contrast to surrounding structures and a versatile shape and appearance. Here, a multi step method is proposed: 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 and used to approximate the esophagus shape. Finally, the surface of this approximation is non-rigidly deformed to better fit the boundary of the organ.
The third part of this work is a method for automatic detection and segmentation of mediastinal lymph nodes. Having low contrast to neighboring structures, it is vital to incorporate as much anatomical knowledge as possible to achieve good detection rates. Here, a prior of the spatial distribution is proposed to model this knowledge. Different variants of this prior are compared to each other. This is combined with a discriminative model that detects lymph nodes from their appearance. It first generates a set of possible lymph node center positions. Two model variants are compared. Given a detected center point, either the bounding box of the lymph node is directly detected, or a lymph node is segmented. A feature set is introduced that is extracted from this segmentation, and a classifier is trained on this feature set and used to reject false detections.