  Accurate detection and segmentation of diffuse large B-cell lymphoma (DLBCL)
from PET images has important implications for estimation of total metabolic
tumor volume, radiomics analysis, surgical intervention and radiotherapy.
Manual segmentation of tumors in whole-body PET images is time-consuming,
labor-intensive and operator-dependent. In this work, we develop and validate a
fast and efficient three-step cascaded deep learning model for automated
detection and segmentation of DLBCL tumors from PET images. As compared to a
single end-to-end network for segmentation of tumors in whole-body PET images,
our three-step model is more effective (improves 3D Dice score from 58.9% to
78.1%) since each of its specialized modules, namely the slice classifier, the
tumor detector and the tumor segmentor, can be trained independently to a high
degree of skill to carry out a specific task, rather than a single network with
suboptimal performance on overall segmentation.
