Faster R-CNN for Vertebral Body Localization

The faster region-based CNN (R-CNN) [19 (link)] was developed from the R-CNN [22 (link)] and the fast R-CNN [23 (link)], which unifies the target detection process (including candidate region generation, feature extraction, classification, and position refinement) into 1 deep network framework and greatly improves operational speed. In step 1, the faster R-CNN was used to locate the vertebral bodies in sagittal MR images.
First, the six vertebral bodies (L1-S1) in 200 midsagittal images were manually located under the guidance of a radiologist. Second, the faster R-CNN was trained to detect and locate each vertebral body. We detected vertebral bodies instead of disks because they were easier to manually locate. Finally, the middle point coordinate of each vertebral body was calculated based on bounding box coordinates, as the precise location of the vertebral bodies would be used to locate the vertebrae in axial MR images, as shown in Figure 1 (step 1).
The faster R-CNN was implemented with Caffe [24 (link)] (Berkeley Vision and Learning Center deep learning framework) and trained in parallel on 4 Nvidia Titan X graphics processing units. Accuracy, sensitivity, and specificity [25 (link),26 (link)] were analyzed to comprehensively evaluate the performance of this system.