Song Yang, Cai Weidong, Eberl Stefan, Fulham Michael J, Feng Dagan
Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia.
Med Image Comput Comput Assist Interv. 2011;14(Pt 3):191-8. doi: 10.1007/978-3-642-23626-6_24.
Positron emission tomography - computed tomography (PET-CT) is now accepted as the best imaging technique to accurately stage lung cancer. The consistent and accurate interpretation of PET-CT images, however, is not a trivial task. We propose a discriminative, multi-level learning and inference method to automatically detect the pathological contexts in the thoracic PET-CT images, i.e. the primary tumor and its spatial relationships within the lung and mediastinum, and disease in regional lymph nodes. The detection results can also be used as features to retrieve similar images with previous diagnosis from an imaging database as a reference set to aid physicians in PET-CT scan interpretation. Our evaluation with clinical data from lung cancer patients suggests our approach is highly accurate.
正电子发射断层扫描-计算机断层扫描（PET-CT）现已被公认为是准确分期肺癌的最佳成像技术。然而，对PET-CT图像进行一致且准确的解读并非易事。我们提出了一种判别式、多层次学习和推理方法，以自动检测胸部PET-CT图像中的病理情况，即原发性肿瘤及其在肺和纵隔内的空间关系，以及区域淋巴结疾病。检测结果还可作为特征，从成像数据库中检索具有先前诊断的相似图像作为参考集，以帮助医生解读PET-CT扫描结果。我们对肺癌患者临床数据的评估表明，我们的方法具有很高的准确性。
