  Image guidance has been widely used in radiation therapy. Correctly
identifying the bounding box of the anatomical landmarks from limited field of
views is the key to success. In image-guided radiation therapy (IGRT), the
detection of those landmarks like the 12th vertebra (T12) still requires
tedious manual inspections and annotations; and superior-inferior misalignment
to the wrong vertebral body is still relatively common. It is necessary to
develop an automated approach to detect those landmarks from images. The
challenges of training a model to identify T12 vertebrae automatically mainly
are high shape similarity between T12 and neighboring vertebrae, limited
annotated data, and class imbalance. This study proposed a novel 3D detection
network, requiring only a small amount of training data. Our approach has the
following innovations, including 1) the introduction of an auxiliary network to
build constraint feature map for improving the model's generalization,
especially when the constraint structure is easier to be detected than the main
one; 2) an improved detection head and target functions for accurate bounding
box detection; and 3) an improved loss functions to address the high class
imbalance. Our proposed network was trained, validated and tested on anotated
CT images from 55 patients and demonstrated accurate distinguish T12 vertebra
from its neighboring vertebrae of high shape similarity. Our proposed algorithm
yielded the bounding box center and size errors of 3.98\pm2.04mm and
16.83\pm8.34mm, respectively. Our approach significantly outperformed
state-of-the-arts Retina-Net3D in average precision (AP) at IoU thresholds of
0.35 and 0.5, with AP increasing from 0 to 95.4 and 0 to 64.7, respectively. In
summary, our approach has a great potential to be integrated into the clinical
workflow to improve the safety of IGRT.
