Paper Info
Title
Multi-stage osteolytic spinal bone lesion detection from CT data with internal sensitivity control
Abstract
Spinal bone lesion detection is a challenging and important task in cancer diagnosis and treatment monitoring. In this paper we present a method for fully-automatic osteolytic spinal bone lesion detection from 3D CT data. It is a multi-stage approach subsequently applying multiple discriminative models, i.e., multiple random forests, for lesion candidate detection and rejection to an input volume. For each detection stage an internal control mechanism ensures maintaining sensitivity on unseen true positive lesion candidates during training. This way a pre-defined target sensitivity score of the overall system can be taken into account at the time of model generation. For a lesion not only the center is detected but also, during post-processing, its spatial extension along the three spatial axes defined by the surrounding vertebral body's local coordinate system. Our method achieves a cross-validated sensitivity score of 75% and a mean false positive rate of 3.0 per volume on a data collection consisting of 34 patients with 105 osteolytic spinal bone lesions. The median sensitivity score is 86% at 2.0 false positives per volume.
Year
DOI
Venue
2012
10.1117/12.911169
Proceedings of SPIE
Keywords
Field
DocType
Computer-aided detection,osteolytic bone lesions,metastases,computed tomography
Computer vision,False positive rate,Treatment monitoring,Lesion,Computer aided detection,Computed tomography,Artificial intelligence,Radiology,Random forest,Discriminative model,False positive paradox,Physics
Conference
Volume
ISSN
Citations 
8315
0277-786X
5
PageRank 
References 
Authors
0.75
11
8
Name
Order
Citations
PageRank
Michael Wels112910.56
B. M. Kelm2151.99
Alexey Tsymbal385887.84
Matthias Hammon46712.70
Grzegorz Soza538624.12
Michael Sühling610917.77
andrea cavallaro750.75
Dorin Comaniciu88389601.83