Automated Delineation of Lung Tumors in PET Images Based on Monotonicity and a Tumor-Customized Criterion

Reliable automated or semiautomated lung tumor delineation methods in positron emission tomography should provide accurate tumor boundary definition and separation of the lung tumor from surrounding tissue or hot spots that have similar intensities to the lung tumor. We propose a tumor-customized downhill (TCD) method to achieve these objectives. Our approach includes: 1) automatic formulation of a tumor-customized criterion to improve tumor boundary definition, 2) a monotonic property of the standardized uptake value (SUV) of tumors to separate the tumor from adjacent regions of increased metabolism (hot spot), and 3) accounts for tumor heterogeneity. Three simulated lesions and 30 PET-CT studies, grouped into simple and complex groups, were used for evaluation. Our main findings are that TCD, when compared to the threshold based on 40% and 50% maximum SUV, adaptive threshold, Fuzzy c-means, and watershed techniques achieved the highest Dice's similarity coefficient average for simulation data (0.73) and complex group (0.71); the least volumetric error in the simple (1.76 mL) and the complex group (14.59 mL); and TCD solves the problem of leakage into adjacent tissues when many other techniques fail.