A Backgroundbased Data Enhancement Method for Lymphoma Segmentation in 3D PET Images Due to the poor resolution and low signaltonoise 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 nonself backgroundbased data enhancement method is proposed for the deep learningbased 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 nonself 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.

A Prior Knowledge Intergrated Scheme for Detection and Segmentation of Lymphomas in 3D PET Images based on DBSCAN and GAs Lymphoma detection and segmentation from PET images are critical tasks for cancer staging and treatment monitoring. However, it is still a challenge owing to the complexities of lymphoma PET data themselves, and the huge memory requirements for 3D volume data. In this work, a prior knowledge integrated scheme based on DBSCAN and GAs is proposed to detect and segment lymphomas in 3D PET images. To reduce memory requirements and add more feature information, billions of voxels in 3D volume data are first aggregated into supervoxels. Then, such supervoxels serve as basic data units for further clustering the supervoxels by using DBSCAN algorithm, in which a new similarity measure based on prior knowledge is proposed. Meanwhile, a genetic algorithm is used to search the most appropriate parameters of DBSCAN to obtain the optimal clustering results. Finally, a series of comparison experiments among various feature attributes and various similarity functions are performed, and the results are encouraging, and show that the proposed scheme by intergrating the prior knowledge of organ distributions can achieve better performance than traditional methods.