A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma
<|reference_start|>A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma: 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.<|reference_end|>
@article{ahamed2024a,
    title={A cascaded deep network for automated tumor detection and segmentation
  in clinical PET imaging of diffuse large B-cell lymphoma},
    author={Shadab Ahamed, Natalia Dubljevic, Ingrid Bloise, Claire Gowdy, Patrick
  Martineau, Don Wilson, Carlos F. Uribe, Arman Rahmim, and Fereshteh
  Yousefirizi},
    journal={Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120323M
  (4 April 2022)},
    year={2024},
    doi={10.1117/12.2612684},
    archivePrefix={arXiv},
    eprint={2403.07092},
    primaryClass={eess.IV cs.CV cs.LG physics.med-ph}
}