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Deep learning for malignant lymph node segmentation and detection: a review.

Wenxia Wu1, Adrien Laville1, Eric Deutsch1

  • 1Unité Mixte de Recherche (UMR) 1030, Gustave Roussy, Department of Radiation Oncology, Université Paris-Saclay, Villejuif, France.

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Summary
This summary is machine-generated.

This review explores deep learning for segmenting malignant lymph nodes, a crucial step in cancer treatment planning. It highlights advancements and challenges, aiming to improve precision and efficiency in oncology.

Keywords:
deep learningdelineationdetectionlymph nodesegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Oncology
  • Radiotherapy Planning

Background:

  • Manual segmentation of OARs, tumors, and lymph nodes is vital but time-consuming and prone to errors.
  • Deep learning shows promise for automatic segmentation, with many studies on OARs and tumors.
  • Comprehensive reviews on deep learning for malignant lymph node segmentation are limited.

Purpose of the Study:

  • To provide an in-depth review of deep learning advancements for malignant lymph node segmentation and detection.
  • To analyze models and outcomes across five clinical sites.
  • To identify current challenges and future trends in the field.

Main Methods:

  • Overview of deep learning methodologies.
  • Examination of specific deep learning models for lymph node segmentation and detection.
  • Analysis of outcomes across head and neck, upper extremity, chest, abdomen, and pelvis.

Main Results:

  • Deep learning models show potential for automating malignant lymph node segmentation.
  • The review synthesizes current research, identifying key models and their performance.
  • Challenges and future directions for clinical application are discussed.

Conclusions:

  • Deep learning offers a path to enhance precision and efficiency in cancer treatment planning through improved lymph node segmentation.
  • Addressing current challenges is crucial for integrating these AI tools into clinical practice.
  • This review bridges a literature gap, focusing specifically on AI for malignant lymph nodes.