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.

这项研究开发了一个快速高效的三阶段级联深度学习模型，用于从 PET 图像中自动检测和分割 DLBCL 肿瘤，并验证了其可行性和准确性。

一种用于弥漫大 B 细胞淋巴瘤的临床 PET 图像自动肿瘤检测和分割的级联深度网络

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

Background: Diffuse large B-cell lymphoma (DLBCL) segmentation is a challenge
in medical image analysis. Traditional segmentation methods for lymphoma
struggle with the complex patterns and the presence of DLBCL lesions.
Objective: We aim to develop an accurate method for lymphoma segmentation with
18F-Fluorodeoxyglucose positron emission tomography (PET) and computed
tomography (CT) images. Methods: Our lymphoma segmentation approach combines a
vision transformer with dual encoders, adeptly fusing PET and CT data via
multimodal cross-attention fusion (MMCAF) module. In this study, PET and CT
data from 165 DLBCL patients were analyzed. A 5-fold cross-validation was
employed to evaluate the performance and generalization ability of our method.
Ground truths were annotated by experienced nuclear medicine experts. We
calculated the total metabolic tumor volume (TMTV) and performed a statistical
analysis on our results. Results: The proposed method exhibited accurate
performance in DLBCL lesion segmentation, achieving a Dice similarity
coefficient of 0.9173$\pm$0.0071, a Hausdorff distance of 2.71$\pm$0.25mm, a
sensitivity of 0.9462$\pm$0.0223, and a specificity of 0.9986$\pm$0.0008.
Additionally, a Pearson correlation coefficient of 0.9030$\pm$0.0179 and an
R-square of 0.8586$\pm$0.0173 were observed in TMTV when measured on manual
annotation compared to our segmentation results. Conclusion: This study
highlights the advantages of MMCAF and vision transformer for lymphoma
segmentation using PET and CT, offering great promise for computer-aided
lymphoma diagnosis and treatment.

本研究旨在发展一种准确的淋巴瘤分割方法，采用 18F - 氟脱氧葡萄糖正电子发射断层扫描和计算机断层扫描图像结合视觉转换器和双编码器进行淋巴瘤分割，这在计算辅助淋巴瘤诊断和治疗方面具有巨大潜力。

基于视觉变换器的 PET/CT 图像淋巴瘤分割的多模态特征融合网络

Vision Transformer-based Multimodal Feature Fusion Network for Lymphoma  Segmentation on PET/CT Images

Diffuse Large B-cell Lymphoma (DLBCL) is a lymphatic cancer involving one or
more lymph nodes and extranodal sites. Its diagnostic and follow-up rely on
Positron Emission Tomography (PET) and Computed Tomography (CT). After
diagnosis, the number of nonresponding patients to standard front-line therapy
remains significant (30-40%). This work aims to develop a computer-aided
approach to identify high-risk patients requiring adapted treatment by
efficiently exploiting all the information available for each patient,
including both clinical and image data. We propose a method based on recent
graph neural networks that combine imaging information from multiple lesions,
and a cross-attention module to integrate different data modalities
efficiently. The model is trained and evaluated on a private prospective
multicentric dataset of 583 patients. Experimental results show that our
proposed method outperforms classical supervised methods based on either
clinical, imaging or both clinical and imaging data for the 2-year
progression-free survival (PFS) classification accuracy.

通过结合多个病灶的成像信息和交叉注意力模块，利用临床和影像数据，发展了一种基于图神经网络的计算机辅助方法，用于识别需要个体化治疗的高风险患者。在 583 名患者的私人前瞻性多中心数据集上训练和评估，实验结果表明，我们的提出方法在 2 年无进展生存（PFS）分类准确性上优于基于临床、成像或临床和成像数据的传统监督方法。