Sadik May, Jakobsson David, Olofsson Fredrik, Ohlsson Mattias, Suurkula Madis, Edenbrandt Lars
Department of Clinical Physiology, Sahlgrenska University Hospital, Göteborg, Sweden.
Nucl Med Commun. 2006 May;27(5):417-23. doi: 10.1097/00006231-200605000-00002.
To develop a completely automated method, based on image processing techniques and artificial neural networks, for the interpretation of bone scans regarding the presence or absence of metastases.
A total of 200 patients, all of whom had the diagnosis of breast or prostate cancer and had undergone bone scintigraphy, were studied retrospectively. Whole-body images, anterior and posterior, were obtained after injection of 99mTc-methylene diphosphonate. The study material was randomly divided into a training group and a test group, with 100 patients in each group. The training group was used in the process of developing the image analysis techniques and to train the artificial neural networks. The test group was used to evaluate the automated method. The image processing techniques included algorithms for segmentation of the head, chest, spine, pelvis and bladder, automatic thresholding and detection of hot spots. Fourteen features from each examination were used as input to artificial neural networks trained to classify the images. The interpretations by an experienced physician were used as the 'gold standard'.
The automated method correctly identified 28 of the 31 patients with metastases in the test group, i.e., a sensitivity of 90%. A false positive classification of metastases was made in 18 of the 69 patients not classified as having metastases by the experienced physician, resulting in a specificity of 74%.
A completely automated method can be used to detect metastases in bone scans. Future developments in this field may lead to clinically valuable decision-support tools.
基于图像处理技术和人工神经网络开发一种完全自动化的方法，用于解读骨扫描中是否存在转移灶。
回顾性研究了200例均被诊断为乳腺癌或前列腺癌且已接受骨闪烁显像的患者。注射99mTc-亚甲基二膦酸盐后获取全身前后位图像。研究材料被随机分为训练组和测试组，每组100例患者。训练组用于开发图像分析技术和训练人工神经网络的过程。测试组用于评估该自动化方法。图像处理技术包括头部、胸部、脊柱、骨盆和膀胱的分割算法、自动阈值处理和热点检测。每次检查的14个特征被用作训练以对图像进行分类的人工神经网络的输入。由经验丰富的医生做出的解读被用作“金标准”。
该自动化方法在测试组中正确识别出31例有转移灶患者中的28例，即灵敏度为90%。在经验丰富的医生未判定为有转移灶的69例患者中，有18例被错误分类为有转移灶，特异性为74%。
一种完全自动化的方法可用于检测骨扫描中的转移灶。该领域未来的发展可能会产生具有临床价值的决策支持工具。
