一种用于弥漫大B细胞淋巴瘤临床PET成像中自动化肿瘤检测与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
AI总结：
针对全身PET图像中DLBCL肿瘤手动分割效率低、依赖操作者的问题，提出三步级联深度学习模型，通过三个专用模块独立训练，将3D Dice得分从58.9%提升至78.1%，实现更高效准确的自动化肿瘤检测与分割。
摘要：
从PET图像中准确检测与分割弥漫大B细胞淋巴瘤（DLBCL），对总代谢肿瘤体积估算、影像组学分析、手术干预及放疗具有重要意义。手动分割全身PET图像中的肿瘤耗时、费力且依赖操作者经验。本研究开发并验证了一种快速高效的三步级联深度学习模型，用于从PET图像中自动检测与分割DLBCL肿瘤。与用于全身PET图像肿瘤分割的单一端到端网络相比，本研究的三步模型效果更优——将3D Dice得分从58.9%提升至78.1%，原因在于其包含的切片分类器、肿瘤检测器、肿瘤分割器这三个专用模块可独立训练以高精度完成特定任务，而非单一网络在整体分割上表现次优。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
