For the computer-aided diagnosis of tumor diseases knowledge about the position, size and type of the lymph
nodes is needed to compute the tumor classification (TNM). For the computer-aided planning of subsequent
surgeries like the Neck Dissection spatial information about the lymph nodes is also important. Thus, an
efficient and exact segmentation method for lymph nodes in CT data is necessary, especially pathological altered
lymph nodes play an important role here.
Based on prior work, in this paper we present a noticeably enhanced model-based segmentation method for
lymph nodes in CT data, which now can be used also for enlarged and mostly well separated necrotic lymph
nodes. Furthermore, the kind of pathological variation can be determined automatically during segmentation,
which is important for the automatic TNM classification.
Our technique was tested on 21 lymph nodes from 5 CT datasets, among several enlarged and necrotic ones.
The results lie in the range of the inter-personal variance of human experts and improve the results of former
work again. Bigger problems were only noticed for pathological lymph nodes with vague boundaries due to
infiltrated neighbor tissue.
Models of geometry or appearance of three-dimensional objects may be used for locating and specifying object
instances in 3D image data. Such models are necessary for segmentation if the object to be segmented is not
separable based on image information only. They provide a-priori knowledge about the expected shape of the
target structure. The success of such a segmentation task depends on the incorporated model knowledge. We present an automatic method to generate such a model for a given target structure. This knowledge
is created in the form of a 3D Stable Mass-Spring Model (SMSM) and can be computed from a single sample
segmentation. The model is built from different image features using a bottom-up strategy, which allows for
different levels of model abstraction. We show the adequacy of the generated models in two practical medical applications: the anatomical segmentation
of the left ventricle in myocardial perfusion SPECT, and the segmentation of the thyroid cartilage of
the larynx in CT datasets. In both cases, the model generation was performed in a few seconds.