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.