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Purpose Automated detection of region of interest (ROI) is a critical stepfor many medical image applications such as heart ROIs detection in perfusionMRI images, lung boundary detection in chest X-rays, and femoral head detectionin pelvic radiographs. Thus, we proposed a practical framework of ROIsdetection in medical images, with a case study for hip detection inanteroposterior (AP) pelvic radiographs.  Materials and Methods: We conducted a retrospective study which analyzed hipjoints seen on 7,399 AP pelvic radiographs from three diverse sources,including 4,290 high resolution radiographs from Chang Gung Memorial HospitalOsteoarthritis, 3,008 low to medium resolution radiographs from OsteoarthritisInitiative, and 101 heterogeneous radiographs from Google image search engine.We presented a deep learning-based ROI detection framework utilizingsingle-shot multi-box detector (SSD) with ResNet-101 backbone and customizedhead structure based on the characteristics of the obtained datasets, whoseground truths were labeled by non-medical annotators in a simple graphicalinterface.  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 hipregions have appropriately covered main features of the hip joints.  Conclusion: The proposed approach featured on low-cost labeling, data-drivenmodel design, and heterogeneous data testing. We have demonstrated thefeasibility of training a robust hip region detector for AP pelvic radiographs.This practical framework has a promising potential for a wide range of medicalimage applications.

A Practical Framework for ROI Detection in Medical Images -- a case study for hip detection in anteroposterior pelvic radiographs