Identification of individual lesions on 18F-NaF PET bone scans is a time-consuming and often subjective process that makes accurate characterization of disease burden challenging. Current automated methods either underestimate disease or struggle with high false positive rates. We developed a statistically optimized regional thresholding (SORT) method that optimizes detection of bone lesions. This study assessed 18F-NaF PET/CT scans of 37 bone metastatic prostate cancer patients. Each PET image was divided into 19 skeletal regions. Areas of disease in each skeletal region were identified by an experienced nuclear medicine physician. A region of interest (ROI) was placed at each disease location and local maxima were extracted for both healthy and diseased ROIs. Secondary physician review was performed after identification of suspicious local maxima. Region-specific SUV thresholds were determined based on receiver operating characteristic (ROC) analysis optimized for detection of malignant disease. The detection performance of the SORT thresholds were compared to commonly used SUV > 10 g ml-1 (SUV10) and SUV > 15 g ml-1 (SUV15) global thresholds. The sensitivity of the SORT thresholds to various factors was evaluated, such as the number of subjects evaluated or image reconstruction settings. 1751 lesions were manually identified by the nuclear medicine physician. SORT identified different thresholds in each skeletal region (SUV range: 3-13 g ml-1). Region-specific SORT thresholding resulted in higher sensitivity (95.8%) than commonly used global thresholds (82.8% for SUV10 and 58.4% for SUV15) while maintaining a high specificity (97.1%, compared to 97.3% for SUV10 and 100.0% for SUV15). Factors, such as reconstruction settings, had minimal impact on threshold optimization, resulting in an average change of 10% (range: 2%-17%) in thresholds for each factor. Region-specific SUV thresholding of NaF PET images for bone lesion detection in metastatic prostate patients was found to be superior to current global thresholding methods.