mlsanityData to 7 Oct 2026

A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma

arXiv 2403.07092 ↗First posted 11 Mar 2024eess.IV cs.CV cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 33 references that are in the map. First posted on arXiv 11 Mar 2024.

Builds on

11 of 33 references.

References · 11

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2017Mask R-CNNcited by 7,259
  5. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  6. 2017Deformable Convolutional Networkscited by 1,481
  7. 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 614
  8. 2018An overview of deep learning in medical imaging focusing on MRIcited by 140
  9. 2017Domain-adversarial neural networks to address the appearance variability of histopathology imagescited by 41
  10. 2020Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapycited by 15
  11. 2018Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancercited by 14

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer Studycited by 0