Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges

Area of Science:

• Oncology
• Radiology
• Artificial Intelligence

Background:

• Lymphoma presents diagnostic challenges due to diverse subtypes and complex imaging features.
• Accurate segmentation of lymphoma in medical imaging is crucial for treatment planning and monitoring.
• Current segmentation methods face limitations in handling the heterogeneity of lymphoma.

Purpose of the Study:

• To review advancements in deep learning for lymphoma segmentation using PET/CT, CT, and MRI.
• To compare deep learning approaches with traditional segmentation methods.
• To explore the clinical applicability and future directions of deep learning in lymphoma segmentation.

Main Methods:

• Comprehensive literature review of deep learning-based lymphoma segmentation studies.
• Analysis of dataset characteristics, network architectures (backbone networks), and performance metrics.
• Comparative evaluation of deep learning models against conventional techniques.

Main Results:

• Deep learning models show promise in improving lymphoma segmentation accuracy and efficiency.
• Key factors influencing model performance include dataset quality, network design, and specific research objectives.
• Significant progress has been made in adapting deep learning for various imaging modalities.

Conclusions:

• Deep learning offers powerful tools for lymphoma segmentation, aiding clinical decision-making.
• Challenges remain in clinical generalizability, workflow integration, and data availability.
• Future research should focus on enhancing model robustness, reducing computational load, and expanding high-quality datasets for broader clinical adoption.