Segmentation agreement and AI-based feature extraction of cutaneous infrared images of obese abdomen after caesarean section: results from a single training session.

Background: Infrared thermography in women undergoing caesarean section has promise to identify a surgical site infection prodrome characterised by changes in cutaneous perfusion with concomitant influences on temperature distribution across the abdomen. This study was designed to compare abdominal and wound regions of interest (ROI) and feature extraction agreement between two independent users after a single training session. Methods: Image analysis performed manually in MATLAB; each rater ‘blind’ to results of the other. Image ROIs were annotated via pixel-level segmentation creating pixel masks at 4 time-points during the first 30 days after surgery. Results: 366 matched image pairs (732 wound and abdomen labels in total) were obtained. Distribution of mask agreement using Jaccard similarity co-efficient ranged from 0.35 to 1. Good segmentation agreement (coefficient ≥ 0.7) (for mask size and shape) was observed for abdomen, but poor for wound (coefficient <0.7). From feature extraction, wound cold spots were observed most in those who later developed wound infections. Conclusions: Rater performance, with respect to the input (image) data in the first stage of algorithm development, reveals a lack of correspondence (agreement) of the ROI indicating the need for further work to refine the characteristics of output labels (masks) before an unsupervised algorithm will work effectively to learn patterns and features of the wound.