Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications Automatic PET image segmentation for oncology applications

• Published in: International journal of radiation oncology, biology, physics Vol. 77; no. 1; pp. 301 - 8
• Main Authors: Hatt, Mathieu, Cheze Le Rest, Catherine, Descourt, Patrice, Dekker, André, de Ruysscher, Dirk, Oellers, Michel, Lambin, Philippe, Pradier, Olivier, Visvikis, Dimitris
• Format: Journal Article
• Language: English
• Published: Elsevier 01.05.2010
• Subjects: Algorithms; Bayes Theorem; Bioengineering; Fluorodeoxyglucose F18; Fuzzy Logic; Humans; Life Sciences; Markov Chains; Medical Oncology; Neoplasms; Nuclear medicine; Positron-Emission Tomography; Radiopharmaceuticals; Radiotherapy Dosage; Radiotherapy Planning, Computer-Assisted; Tumor Burden; Heterogeneous functional volumes delineation; automatic segmentation; image-guided radiotherapy; dose painting
• ISSN: 0360-3016; 1879-355X
• DOI: 10.1016/j.ijrobp.2009.08.018

PURPOSE: Accurate contouring of positron emission tomography (PET) functional volumes is now considered crucial in image-guided radiotherapy and other oncology applications because the use of functional imaging allows for biological target definition. In addition, the definition of variable uptake regions within the tumor itself may facilitate dose painting for dosimetry optimization. METHODS AND MATERIALS: Current state-of-the-art algorithms for functional volume segmentation use adaptive thresholding. We developed an approach called fuzzy locally adaptive Bayesian (FLAB), validated on homogeneous objects, and then improved it by allowing the use of up to three tumor classes for the delineation of inhomogeneous tumors (3-FLAB). Simulated and real tumors with histology data containing homogeneous and heterogeneous activity distributions were used to assess the algorithm's accuracy. RESULTS: The new 3-FLAB algorithm is able to extract the overall tumor from the background tissues and delineate variable uptake regions within the tumors, with higher accuracy and robustness compared with adaptive threshold (T(bckg)) and fuzzy C-means (FCM). 3-FLAB performed with a mean classification error of less than 9% +/- 8% on the simulated tumors, whereas binary-only implementation led to errors of 15% +/- 11%. T(bckg) and FCM led to mean errors of 20% +/- 12% and 17% +/- 14%, respectively. 3-FLAB also led to more robust estimation of the maximum diameters of tumors with histology measurements, with <6% standard deviation, whereas binary FLAB, T(bckg) and FCM lead to 10%, 12%, and 13%, respectively. CONCLUSION: These encouraging results warrant further investigation in future studies that will investigate the impact of 3-FLAB in radiotherapy treatment planning, diagnosis, and therapy response evaluation.