Discriminative pathological context detection in thoracic images based on multi-level inference

Authors:
Yang Song;Weidong Cai;Stefan Eberl;Michael J. Fulham;Dagan Feng
Affiliations:
Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney, Australia;Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney, Australia;Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney and Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, ...;Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney and Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, ...;Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney, Australia and Center for Multimedia Signal Processing, Department of Elec ...
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Year:
2011

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Abstract

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 PETCT 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.