Complete fully automatic model-based segmentation of normal and pathological lymph nodes in CT data.

Purpose: Exact and reproducible knowledge regarding the position, size, and type of the lymph nodes is often needed for tumor computer-aided diagnosis, treatment planning, and follow-up. An automatic segmentation method for CT data was developed that can identify and delineate normal as well as pathologically altered lymph nodes to satisfy this requirement.

Methods: A semi-automatic lymph node segmentation method was developed using a 3D Stable Mass-Spring Model (SMSM), based on parallel simulation of the shape model on CT scan images.

[Three-dimensional visualization for preoperative planning and evaluation in head and neck surgery].

There are a lot of diagnostic possibilities for the preoperative planning in head and neck surgery. So far, no study was performed to evaluate if there is an advantage of three-dimensional visualization compared to conventional computed tomography yet. Additionally, there are no specifications for such a visualization prior surgery in head and neck surgery.

Segmentation of neck lymph nodes in CT datasets with stable 3D mass-spring models segmentation of neck lymph nodes.

Rationale And Objectives: The quantitative assessment of neck lymph nodes in the context of malignant tumors requires an efficient segmentation technique for lymph nodes in tomographic three-dimensional (3D) datasets. We present a stable 3D mass-spring model for lymph node segmentation in computed tomography (CT) datasets.

Materials And Methods: For the first time our model concurrently represents the characteristic gray value range, directed contour information, and shape knowledge, which leads to a robust and efficient segmentation process.

Segmentation of neck lymph nodes in CT datasets with stable 3D mass-spring models.

The quantitative assessment of neck lymph nodes in the context of malign tumors requires an efficient segmentation technique for lymph nodes in tomographic 3D datasets. We present a Stable 3D Mass-Spring Model for lymph node segmentation in CT datasets. Our model for the first time represents concurrently the characteristic gray value range, directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process.

[Three-dimensional visualization of CT of the neck for surgical planning].

Background: Surgical planning in ENT profits from computer assisted preoperative visualization and planning. The informative capability is to be improved by three-dimensional illustrating of the preoperative available data. The possibility of a 3-D-visualization of lymph nodes stands in the center of the interest.