Colon cancer is one of the most frequent causes of death. CT colonography is a novel method for the detection of polyps
and early cancer. The general principle of CT colonography includes a cathartic bowel preparation. The resulting
discomfort for patients leads to limited patient acceptance and therefore to limited cancer detection rates.
Reduced bowel preparation, techniques for stool tagging, and electronic cleansing, however, improve the acceptance
rates. Hereby, the high density of oral contrast material highlights residual stool and can be digitally removed.
Known subtraction methods cause artifacts: additional 3D objects are introduced and small bowel folds are perforated.
We propose a new algorithm that is based on the 2nd derivative of the image data using the Hessian matrix and the
following principal axis transform to detect tiny folds which shall not be subtracted together with tagged stool found by a
thresholding method. Since the stool is usually not homogenously tagged with contrast media a detection algorithm for
island-like structures is incorporated. The interfaces of air-stool level and colon wall are detected by a 3-dimensional
difference of Gaussian module. A 3-dimensional filter smoothes the transitions between removed stool and colon tissue.
We evaluated the efficacy of the new algorithm with 10 patient data sets. The results showed no introduced artificial
objects and no perforated folds. The artifacts at the air-stool and colon tissue-stool transitions are considerably reduced
compared to those known from the literature.
The lymphatic system comprises a series of interconnected lymph nodes that are commonly distributed along branching
or linearly oriented anatomic structures. Physicians must evaluate lymph nodes when staging cancer and planning
optimal paths for nodal biopsy. This process requires accurately determining the lymph node's position with respect to
major anatomical landmarks. In an effort to standardize lung cancer staging, The American Joint Committee on Cancer
(AJCC) has classified lymph nodes within the chest into 4 groups and 14 sub groups. We present a method for
automatically labeling lymph nodes according to this classification scheme, in order to improve the speed and accuracy
of staging and biopsy planning. Lymph nodes within the chest are clustered around the major blood vessels and the
airways. Our fully automatic labeling method determines the nodal group and sub-group in chest CT data by use of
computed airway and aorta centerlines to produce features relative to a given node location. A classifier then determines
the label based upon these features. We evaluate the efficacy of the method on 10 chest CT datasets containing 86
labeled lymph nodes. The results are promising with 100% of the nodes assigned to the correct group and 76% to the
correct sub-group. We anticipate that additional features and training data will further improve the results. In addition to
labeling, other applications include automated lymph node localization and visualization. Although we focus on chest
CT data, the method can be generalized to other regions of the body as well as to different imaging modalities.