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

Abstract

Lymphoma is a prevalent malignant tumor within the hematological system, posing significant challenges to clinical practice due to its diverse subtypes, intricate radiological and metabolic manifestations. Lymphoma segmentation studies based on positron emission tomography/computed tomography (PET/CT), CT, and magnetic resonance imaging represent key strategies for addressing these challenges. This article reviews the advancements in lymphoma segmentation research utilizing deep learning methods, offering a comparative analysis with traditional approaches, and conducting an in-depth examination and summary of aspects such as dataset characteristics, backbone networks of models, adjustments to network structures based on research objectives, and model performance. The article also explores the potential and challenges of translating deep learning-based lymphoma segmentation research into clinical scenarios, with a focus on practical clinical applications. The future research priorities in lymphoma segmentation are identified as enhancing the models' clinical generalizability, integrating into clinical workflows, reducing computational demands, and expanding high-quality datasets. These efforts aim to facilitate the broad application of deep learning in the diagnosis and treatment monitoring of lymphoma.

Keywords: Lymphoma; artificial intelligence; clinical application; deep learning; medical image segmentation.

Figure 1.

Lymphoma typing and subtype schematic diagram.

Figure 2.

The proportion of different modal data in lymphoma segmentation studies.

Figure 3.

The main backbone species in lymphoma segmentation studies.

Figure 4.

Adjustments of network architecture based on research objectives.

Figure 5.

The key components of methods aimed at enhancing segmentation accuracy. In edge detection, Luo et al. have integrated a Multi-Atlas Boundary Awareness module into the backbone network. This module operates on gradient maps, uncertainty maps, and level set maps. To address class imbalance issues, Wang et al. introduced the Prior-Shift Regularization module to enhance the model's sensitivity toward minority classes. In the context of time-series problems, Wang et al. incorporated a Recursive Dense Decoder (RDS-Decoder), which emulates the behavior of a Recurrent Neural Network (RNN) within the decoder. This facilitates the dense reuse of feature information between decoder feature maps at the same scale, capturing temporal dependencies between feature maps. In the process of leveraging spatial and multimodal feature fusion, Yuan et al. designed dual encoder branches, each processing a different modality of images (positron emission tomography (PET) or computed tomography (CT)). Each encoder branch consists of multiple convolutional layers to extract features at various scales for each modality. A fusion learning component generates spatial fusion maps from the features obtained by the two encoder branches, quantifying the contribution of information from each modality. In tackling issues of uncertainty, the methods of Huang et al., Diao et al., and Huang et al. showcase the integration strategy and functionality of the Dempster–Shafer (DS) theory within the model framework.

Figure 6.

Workflow of a clinically application-oriented integrated lymphoma segmentation method.