The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review

TL;DR

This systematic review examines how deep learning techniques are applied to lymph node segmentation in medical imaging, highlighting current methodologies, challenges, and future research directions to improve cancer diagnosis and treatment.

Contribution

It provides the first comprehensive overview of deep learning applications in lymph node segmentation and discusses potential future research avenues.

Findings

Deep learning improves lymph node segmentation accuracy.

Challenges include shape variability and limited labeled data.

Future directions involve multimodal fusion and transfer learning.

Abstract

Automatic lymph node segmentation is the cornerstone for advances in computer vision tasks for early detection and staging of cancer. Traditional segmentation methods are constrained by manual delineation and variability in operator proficiency, limiting their ability to achieve high accuracy. The introduction of deep learning technologies offers new possibilities for improving the accuracy of lymph node image analysis. This study evaluates the application of deep learning in lymph node segmentation and discusses the methodologies of various deep learning architectures such as convolutional neural networks, encoder-decoder networks, and transformers in analyzing medical imaging data across different modalities. Despite the advancements, it still confronts challenges like the shape diversity of lymph nodes, the scarcity of accurately labeled datasets, and the inadequate development of methods that are robust and generalizable across different imaging modalities. To the best of our knowledge, this is the first study that provides a comprehensive overview of the application of deep learning techniques in lymph node segmentation task. Furthermore, this study also explores potential future research directions, including multimodal fusion techniques, transfer learning, and the use of large-scale pre-trained models to overcome current limitations while enhancing cancer diagnosis and treatment planning strategies.