Computer-aided abdominal lymph node detection using contrast-enhanced CT images

Jiamin Liu; Jeremy Hua; Jianhua Yao; Jacob M. White; Ronald M. Summers

doi:10.1117/12.878252

4 March 2011 Computer-aided abdominal lymph node detection using contrast-enhanced CT images

Jiamin Liu, Jeremy Hua, Jianhua Yao, Jacob M. White, Ronald M. Summers

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 796313 (2011) https://doi.org/10.1117/12.878252
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

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 an improved blob detection method for automatic lymph node detection in contrast-enhanced abdominal CT images. First, spine was automatically extracted to indicate abdominal region. Since lymph nodes are usually next to blood vessels, abdominal blood vessels were then segmented as a reference to set the search region for lymph nodes. Next, lymph node candidates were generated by object-scale Hessian analysis. Finally the detected candidates were segmented and some prior anatomical knowledge was utilized for false positive reduction. We applied our method to 9 patients with 11 enlarged lymph nodes and compared the results with the performance of the original multi-scale Hessian analysis. The sensitivities were 91% and 82% for our method and multi-scale Hessian analysis, respectively. The false positive rates per patient were 17 and 28 for our method and multi-scale Hessian analysis, respectively. Our results indicated that computer-aided lymph node detection with this blob detector may yield a high sensitivity and a relatively low FP rate in abdominal CT.

Citation Download Citation

Jiamin Liu, Jeremy Hua, Jianhua Yao, Jacob M. White, and Ronald M. Summers "Computer-aided abdominal lymph node detection using contrast-enhanced CT images", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796313 (4 March 2011); https://doi.org/10.1117/12.878252

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