Al Hasan Md Mahfuz, Ghazimoghadam Saba, Tunlayadechanont Padcha, Mostafiz Mohammed Tahsin, Gupta Manas, Roy Antika, Peters Keith, Hochhegger Bruno, Mancuso Anthony, Asadizanjani Navid, Forghani Reza
Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, 1600 SW Archer Road, Gainesville, FL, 32610-0374, USA.
Department of Electrical and Computer Engineering, University of Florida College of Medicine, Gainesville, FL, USA.
J Imaging Inform Med. 2024 Dec;37(6):2955-2966. doi: 10.1007/s10278-024-01114-w. Epub 2024 Jun 27.
Early and accurate detection of cervical lymph nodes is essential for the optimal management and staging of patients with head and neck malignancies. Pilot studies have demonstrated the potential for radiomic and artificial intelligence (AI) approaches in increasing diagnostic accuracy for the detection and classification of lymph nodes, but implementation of many of these approaches in real-world clinical settings would necessitate an automated lymph node segmentation pipeline as a first step. In this study, we aim to develop a non-invasive deep learning (DL) algorithm for detecting and automatically segmenting cervical lymph nodes in 25,119 CT slices from 221 normal neck contrast-enhanced CT scans from patients without head and neck cancer. We focused on the most challenging task of segmentation of small lymph nodes, evaluated multiple architectures, and employed U-Net and our adapted spatial context network to detect and segment small lymph nodes measuring 5-10 mm. The developed algorithm achieved a Dice score of 0.8084, indicating its effectiveness in detecting and segmenting cervical lymph nodes despite their small size. A segmentation framework successful in this task could represent an essential initial block for future algorithms aiming to evaluate small objects such as lymph nodes in different body parts, including small lymph nodes looking normal to the naked human eye but harboring early nodal metastases.
对头颈部恶性肿瘤患者进行优化管理和分期时，早期准确检测颈部淋巴结至关重要。初步研究表明，放射组学和人工智能（AI）方法在提高淋巴结检测和分类的诊断准确性方面具有潜力，但要在实际临床环境中应用这些方法，首先需要一个自动淋巴结分割流程。在本研究中，我们旨在开发一种非侵入性深度学习（DL）算法，用于在来自221例无头颈癌患者的正常颈部增强CT扫描的25119张CT切片中检测和自动分割颈部淋巴结。我们专注于小淋巴结分割这一最具挑战性的任务，评估了多种架构，并采用U-Net和我们改进的空间上下文网络来检测和分割直径为5-10毫米的小淋巴结。所开发的算法取得了0.8084的Dice分数，表明其在检测和分割颈部淋巴结方面的有效性，尽管淋巴结尺寸较小。在此任务中成功的分割框架可能是未来算法的一个重要初始模块，这些算法旨在评估不同身体部位的小物体，如淋巴结，包括肉眼看似正常但存在早期淋巴结转移的小淋巴结。
