Na Seongwon, Choi Se Jin, Ko Yousun, Urooj Bushra, Huh Jimi, Cha Seungwoo, Jung Chul, Cheon Hwayeong, Jeon Jae Yong, Kim Kyung Won
Biomedical Engineering Research Center, Asan Institute for Life Science, Asan Medical Center, Seoul, Korea.
Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Eur Radiol. 2025 May 16. doi: 10.1007/s00330-025-11673-3.
Clinical assessment of lymphedema, particularly for lymphedema severity and fluid-fibrotic lesions, remains challenging with traditional methods. We aimed to develop and validate a deep learning segmentation tool for automated tissue component analysis in lower extremity CT scans.
For development datasets, lower extremity CT venography scans were collected in 118 patients with gynecologic cancers for algorithm training. Reference standards were created by segmentation of fat, muscle, and fluid-fibrotic tissue components using 3D slicer. A deep learning model based on the Unet++ architecture with an EfficientNet-B7 encoder was developed and trained. Segmentation accuracy of the deep learning model was validated in an internal validation set (n = 10) and an external validation set (n = 10) using Dice similarity coefficient (DSC) and volumetric similarity (VS). A graphical user interface (GUI) tool was developed for the visualization of the segmentation results.
Our deep learning algorithm achieved high segmentation accuracy. Mean DSCs for each component and all components ranged from 0.945 to 0.999 in the internal validation set and 0.946 to 0.999 in the external validation set. Similar performance was observed in the VS, with mean VSs for all components ranging from 0.97 to 0.999. In volumetric analysis, mean volumes of the entire leg and each component did not differ significantly between reference standard and deep learning measurements (p > 0.05). Our GUI displays lymphedema mapping, highlighting segmented fat, muscle, and fluid-fibrotic components in the entire leg.
Our deep learning algorithm provides an automated segmentation tool enabling accurate segmentation, volume measurement of tissue component, and lymphedema mapping.
Question Clinical assessment of lymphedema remains challenging, particularly for tissue segmentation and quantitative severity evaluation. Findings A deep learning algorithm achieved DSCs > 0.95 and VS > 0.97 for fat, muscle, and fluid-fibrotic components in internal and external validation datasets. Clinical relevance The developed deep learning tool accurately segments and quantifies lower extremity tissue components on CT scans, enabling automated lymphedema evaluation and mapping with high segmentation accuracy.
对于淋巴水肿，尤其是淋巴水肿严重程度和液体纤维化病变的临床评估，传统方法仍具有挑战性。我们旨在开发并验证一种深度学习分割工具，用于下肢CT扫描中的自动组织成分分析。
对于开发数据集，收集了118例妇科癌症患者的下肢CT静脉造影扫描图像用于算法训练。通过使用3D Slicer对脂肪、肌肉和液体纤维化组织成分进行分割来创建参考标准。开发并训练了一种基于带有EfficientNet - B7编码器的Unet++架构的深度学习模型。使用骰子相似系数（DSC）和体积相似性（VS）在内部验证集（n = 10）和外部验证集（n = 10）中验证深度学习模型的分割准确性。开发了一个图形用户界面（GUI）工具用于分割结果的可视化。
我们的深度学习算法实现了高分割准确性。内部验证集中每个成分和所有成分的平均DSC范围为0.945至0.999，外部验证集中为0.946至0.999。在VS中观察到类似的性能，所有成分的平均VS范围为0.97至0.999。在体积分析中，整个腿部和每个成分的平均体积在参考标准和深度学习测量之间没有显著差异（p > 0.05）。我们的GUI显示淋巴水肿映射图，突出显示整个腿部分割出的脂肪、肌肉和液体纤维化成分。
我们的深度学习算法提供了一种自动分割工具，能够进行准确分割、组织成分体积测量和淋巴水肿映射。
问题 淋巴水肿的临床评估仍然具有挑战性，特别是对于组织分割和定量严重程度评估。发现 深度学习算法在内部和外部验证数据集中对脂肪、肌肉和液体纤维化成分的DSC > 0.95且VS > 0.97。临床意义 所开发的深度学习工具能够在CT扫描上准确分割和量化下肢组织成分，实现具有高分割准确性的自动淋巴水肿评估和映射。
