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23 February 2012 Automatic detection of axillary lymphadenopathy on CT scans of untreated chronic lymphocytic leukemia patients
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Patients with chronic lymphocytic leukemia (CLL) have an increased frequency of axillary lymphadenopathy. Pretreatment CT scans can be used to upstage patients at the time of presentation and post-treatment CT scans can reduce the number of complete responses. In the current clinical workflow, the detection and diagnosis of lymph nodes is usually performed manually by examining all slices of CT images, which can be time consuming and highly dependent on the observer's experience. A system for automatic lymph node detection and measurement is desired. We propose a computer aided detection (CAD) system for axillary lymph nodes on CT scans in CLL patients. The lung is first automatically segmented and the patient's body in lung region is extracted to set the search region for lymph nodes. Multi-scale Hessian based blob detection is then applied to detect potential lymph nodes within the search region. Next, the detected potential candidates are segmented by fast level set method. Finally, features are calculated from the segmented candidates and support vector machine (SVM) classification is utilized for false positive reduction. Two blobness features, Frangi's and Li's, are tested and their free-response receiver operating characteristic (FROC) curves are generated to assess system performance. We applied our detection system to 12 patients with 168 axillary lymph nodes measuring greater than 10 mm. All lymph nodes are manually labeled as ground truth. The system achieved sensitivities of 81% and 85% at 2 false positives per patient for Frangi's and Li's blobness, respectively.
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Jiamin Liu, Jeremy Hua, Vivek Chellappa, Nicholas Petrick, Berkman Sahiner, Mohammed Farooqui, Gerald Marti, Adrian Wiestner, and Ronald M. Summers "Automatic detection of axillary lymphadenopathy on CT scans of untreated chronic lymphocytic leukemia patients", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83150B (23 February 2012);

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