@inproceedings{1ec4037b2e0f4c69953e76bb45591906,
title = "Thoracic abnormality detection with data adaptive structure estimation",
abstract = "Automatic detection of lung tumors and abnormal lymph nodes are useful in assisting lung cancer staging. This paper presents a novel detection method, by first identifying all abnormalities, then differentiating between lung tumors and abnormal lymph nodes based on their degree of overlap with the lung field and mediastinum. Regression-based appearance model and graph-based structure labeling are designed to estimate the actual lung field and mediastinum from the pathology-affected thoracic images adaptively. The proposed method is simple, effective and generalizable, and can be potentially applicable to other medical imaging domains as well. Promising results are demonstrated based on our evaluations on clinical PET-CT data sets from lung cancer patients.",
author = "Yang Song and Weidong Cai and Yun Zhou and Dagan Feng",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2012.; 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 ; Conference date: 01-10-2012 Through 05-10-2012",
year = "2012",
doi = "10.1007/978-3-642-33415-3_10",
language = "English",
isbn = "9783642334146",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "74--81",
editor = "Nicholas Ayache and Herve Delingette and Polina Golland and Kensaku Mori",
booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings",
}