Computer-aided lymph node detection in abdominal CT images

Jiamin Liu; Jacob M. White; Ronald M. Summers

doi:10.1117/12.844406

Abstract

Many malignant processes cause abdominal lymphadenopathy, and computed tomography (CT) has become the primary modality for its detection. A lymph node is considered enlarged (swollen) if it is more than 1 centimeter in diameter. Which lymph nodes are swollen depends on the type of disease and the body parts involved. Identifying their locations is very important to determine the possible cause. In the current clinical workflow, the detection and diagnosis of enlarged lymph nodes is usually performed manually by examining all slices of CT images, which can be error-prone and time consuming. 3D blob enhancement filter is a usual way for computer-aided node detection. We proposed a new 3D blob detector for automatic lymph node detection in contrast-enhanced abdominal CT images. Since lymph nodes are usually next to blood vessels, abdominal blood vessels were first segmented as a reference to set the search region for lymph nodes. Then a new detection response measure, blobness, is defined based on eigenvalues of the Hessian matrix and the object scale in our new blob detector. Voxels with higher blobness were clustered as lymph node candidates. Finally some prior anatomical knowledge was utilized for false positive reduction. We applied our method to 5 patients and compared the results with the performance of the original blobness definition. Both methods achieved sensitivity of 83.3% but the false positive rates per patient were 14 and 26 for our method and the original method, respectively. Our results indicated that computer-aided lymph node detection with this new blob detector may yield a high sensitivity and a relatively low FP rate in abdominal CT.

Citation Download Citation

Jiamin Liu, Jiamin Liu, Jacob M. White, Jacob M. White, Ronald M. Summers, Ronald M. Summers, } "Computer-aided lymph node detection in abdominal CT images", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76240U (9 March 2010); doi: 10.1117/12.844406; https://doi.org/10.1117/12.844406

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