Affinity Propagation for Computer-aided Detection of Lung Cancer in 3D PET/CT Studies

Open Access
Kuhlengel, Trevor Keith
Graduate Program:
Computer Science and Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
July 19, 2017
Committee Members:
  • William Evan Higgins, Thesis Advisor
  • Robert Collins, Committee Member
  • PET/CT
  • PET
  • CT
  • affinity propagation
  • lung cancer
  • automated detection
Bronchoscopic biopsy of suspicious sites is a critical component of lung-cancer staging. Physicians use PET/CT studies to provide structural and functional information important to diagnosis, staging, and bronchoscopy procedure planning. In this thesis, we aim to develop an automatic robust and time-efficient method for detection of PET-avid thoracic regions of interest (ROIs) in 3D PET/CT studies. The method presented in this thesis uses a PET histogram of the thoracic cavity to automatically select thresholds to apply to the PET study. We detect PET ROIs using thresholds from affinity propagation clustering on the PET histogram after smoothing using kernel density estimation and exponential smoothing. Detected ROIs are subsequently filtered using false positive reduction techniques based on physical constraints of the PET image volumes and empirically chosen volume and intensity restrictions. The method achieves a 68.3\% true detection rate, 40.1\% positive predictive value, and an average of 7.6 false positives per case, evaluated over a dataset of 17 cases. The average computation time for the method is 7.1 seconds.