  Purpose Automated detection of region of interest (ROI) is a critical step
for many medical image applications such as heart ROIs detection in perfusion
MRI images, lung boundary detection in chest X-rays, and femoral head detection
in pelvic radiographs. Thus, we proposed a practical framework of ROIs
detection in medical images, with a case study for hip detection in
anteroposterior (AP) pelvic radiographs.
  Materials and Methods: We conducted a retrospective study which analyzed hip
joints seen on 7,399 AP pelvic radiographs from three diverse sources,
including 4,290 high resolution radiographs from Chang Gung Memorial Hospital
Osteoarthritis, 3,008 low to medium resolution radiographs from Osteoarthritis
Initiative, and 101 heterogeneous radiographs from Google image search engine.
We presented a deep learning-based ROI detection framework utilizing
single-shot multi-box detector (SSD) with ResNet-101 backbone and customized
head structure based on the characteristics of the obtained datasets, whose
ground truths were labeled by non-medical annotators in a simple graphical
interface.
  Results: Our method achieved average intersection over union (IoU)=0.8115,
average confidence=0.9812, and average precision with threshold IoU=0.5
(AP50)=0.9901 in the independent test set, suggesting that the detected hip
regions have appropriately covered main features of the hip joints.
  Conclusion: The proposed approach featured on low-cost labeling, data-driven
model design, and heterogeneous data testing. We have demonstrated the
feasibility of training a robust hip region detector for AP pelvic radiographs.
This practical framework has a promising potential for a wide range of medical
image applications.
