A Background-based Data Enhancement Method for Lymphoma Segmentation in 3D PET Images

摘要

Due to the poor resolution and low signal-to-noise ratio in PET images, and especially to the wide variation in size, shape, site and SUV value among different patients or even for the same patient, lymphoma segmentation in 3D PET Images is still a challenging task in the field of medical image processing. In this work, a novel non-self background-based data enhancement method is proposed for the deep learning-based lymphoma segmentation problem. Firstly, a lymphoma pool with 1991 lymphoid lesions is created. Then, some lymphomas from the lymphoma pool are randomly selected and integrated into their non-self images of the patients according to their respective coordinates when training networks. Finally, a series of comparison experiments among various network models and methods are conducted to verify the effectiveness of the proposed method. The results indicated that the proposed method was promising, and could obtain better comprehensive performance than other methods without any data enhancements for the lymphoma segmentation problems.