Automated CT segmentation for lower extremity tissues in lymphedema evaluation using deep learning

• Published in: European radiology Vol. 35; no. 11; pp. 6842 - 6852
• Main Authors: Na, Seongwon, Choi, Se Jin, Ko, Yousun, Urooj, Bushra, Huh, Jimi, Cha, Seungwoo, Jung, Chul, Cheon, Hwayeong, Jeon, Jae Yong, Kim, Kyung Won
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2025; Springer Nature B.V
• Subjects: Adult; Aged; Algorithms; Automation; Computed tomography; Datasets; Deep Learning; Diagnostic Radiology; Dissection; Female; Fibrosis; Graphical user interface; Gynecological cancer; Hospitals; Humans; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Lower Extremity - diagnostic imaging; Lymphatic system; Lymphedema; Lymphedema - diagnostic imaging; Machine learning; Mapping; Medical imaging; Medicine; Medicine & Public Health; Middle Aged; Muscles; Neuroradiology; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology; Seeds; Segmentation; Software; Tissues; Tomography, X-Ray Computed - methods; Ultrasound; Volume measurement; Volumetric analysis; Deep learning; Lower extremity; Lymphedema; X-ray computed tomography; Image processing (computer-assisted)
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-025-11673-3

Objectives 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. Materials and methods 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. 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. Conclusion Our deep learning algorithm provides an automated segmentation tool enabling accurate segmentation, volume measurement of tissue component, and lymphedema mapping. Key Points 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 . Graphical Abstract