Micro-computed tomography (μCT) is a valuable imaging modality for longitudinal quantification of bone volumes to identify disease or treatment effects for a broad range of conditions that affect bone health. Complex structures, such as the hindpaw with up to 31 distinct bones in mice, have considerable analytic potential, but quantification is often limited to a single bone volume metric due to the intensive effort of manual segmentation. Herein, we introduce a high-throughput, user-friendly, and semi-automated method for segmentation of murine hindpaw μCT datasets. In vivo μCT was performed on male (n = 4; 2–8-months) and female (n = 4; 2–5-months) C57BL/6 mice longitudinally each month. Additional 9.5-month-old male C57BL/6 hindpaws (n = 6 hindpaws) were imaged by ex vivo μCT to investigate the effects of resolution and integration time on analysis outcomes. The DICOMs were exported to Amira software for the watershed-based segmentation, and watershed markers were generated automatically at approximately 80% accuracy before user correction. The semi-automated segmentation method utilizes the original data, binary mask, and bone-specific markers that expand to the full volume of the bone using watershed algorithms. Compared to the conventional manual segmentation using Scanco software, the semi-automated approach produced similar raw bone volumes. The semi-automated segmentation also demonstrated a significant reduction in segmentation time for both experienced and novice users compared to standard manual segmentation. ICCs between experienced and novice users were >0.9 (excellent reliability) for all but 4 bones. The described semi-automated segmentation approach provides remarkable reliability and throughput advantages. Adoption of the semi-automated segmentation approach will provide standardization and reliability of bone volume measures across experienced and novice users and between institutions. The application of this model provides a considerable strategic advantage to accelerate various research opportunities in pre-clinical bone and joint analysis towards clinical translation.

The use of local antibiotics to treat bone infections has been questioned due to a lack of clinical efficacy and emerging information about Staphylococcus aureus colonization of the osteocyte-lacuno canalicular network (OLCN). Here we propose bisphosphonate-conjugated antibiotics (BCA) using a “target and release” approach to deliver antibiotics to bone infection sites. A fluorescent bisphosphonate probe was used to demonstrate bone surface labeling adjacent to bacteria in a S. aureus infected mouse tibiae model. Bisphosphonate and hydroxybisphosphonate conjugates of sitafloxacin and tedizolid (BCA) were synthesized using hydroxyphenyl and aminophenyl carbamate linkers, respectively. The conjugates were adequately stable in serum. Their cytolytic activity versus parent drug on MSSA and MRSA static biofilms grown on hydroxyapatite discs was established by scanning electron microscopy. Sitafloxacin O-phenyl carbamate BCA was effective in eradicating static biofilm: no colony formation units (CFU) were recovered following treatment with 800 mg/L of either the bisphosphonate or α-hydroxybisphosphonate conjugated drug (p < 0.001). In contrast, the less labile tedizolid N-phenyl carbamate linked BCA had limited efficacy against MSSA, and MRSA. CFU were recovered from all tedizolid BCA treatments. These results demonstrate the feasibility of BCA eradication of S. aureus biofilm on OLCN bone surfaces and support in vivo drug development of a sitafloxacin BCA.