Automatic detection of axillary lymphadenopathy on CT scans of untreated chronic lymphocytic leukemia patients

Abstract

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.

A Novel Hybrid Classifier Model for Leukemia Prediction Using Machine Learning Techniques

“…Recently,fuzzy C-means has been used for diagnosing ALL to obtain a collection of images from the available database and got 90% accuracy in processing time [33]. Neural Network was developed on the pre-processed images for histogram equalization and to detect the canny edge that originated from the clinical slides and obtained 97.8% accuracy in detecting the lymphoblastic cancers [34].…”

SPIE Computer-Aided Diagnosis conference anniversary review

“…11,[86][87][88][89][90][91] Occasional presentations have focused on CAD in other oncologic diseases including assessment of lymphadenopathy, cervical cancer, esophageal cancer, pancreatic tumors, and multiple myeloma. [92][93][94][95][96][97][98] CAD of cardiac and vascular imaging included coronary artery calcium scoring with deep learning, coronary artery detection, and stenosis analysis on angiography and CT, intravascular OCT, cardiomegaly assessment, and cardiac wall and chamber assessment. [99][100][101][102][103][104] Atherosclerotic disease outside the heart was also assessed.…”

“… 11 , 86 – 91 Occasional presentations have focused on CAD in other oncologic diseases including assessment of lymphadenopathy, cervical cancer, esophageal cancer, pancreatic tumors, and multiple myeloma. 92 – 98 …”

Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest

“…In addition to mediastinal lymph node detection, there have been several preceding works which have investigated lymph node detection in the abdomen, pelvic, and axillary. [13][14][15][16][17][18] For example, Kitasaka et al 16 presented a 3D minimum directional difference filter for abdominal lymph nodes detection and applied Hessian-based vesselness to reduce the false positives. Barbu et al…”