## 1 Introduction  
Over the last 40 to 50 years, the rate of obesity has tripled [1]. As a global health concern, the prevalence of obesity has risen across race, age, and gender. Most pronounced in women of reproductive age [2], obesity during pregnancy is a major obstetric complication especially in view of the increased risk of a caesarean section birth. Furthermore, with high rates of surgical site infection (SSI) affecting 28-50% of obese and morbidity obese women, respectively [3, 4], thej between obesity, caesarean section, and wound infection has become a major healthcare problem for post-operative recovery. The human and economic consequences of SSI are driving national and international organisations to tackle the problem of rising SSI prevalence across all surgical specialities [5, 6].  
In our recent studies of obese women giving birth by caesarean section, rather than waiting until infective complications present, we have focused on finding new ways to explore a prodromal phase of incipient wound tissue breakdown at the surgical site using long wave infrared thermography (IRT) to map the distribution of heat across the abdomen and wound site [3, 7]. As changes in cutaneous temperature are predominately influenced by local skin blood flow, areas of high (and lower) temperature have been used to assess skin perfusion [8; 9]. Although thermography is recognised for its diagnostic potential in identifying locations of abnormal chemical and blood vessel activity across many medical pathologies [10], there is a gap in knowledge of the relationship between skin temperature and skin perfusion in the wider context of surgical wound complications [11].  
However, with new approaches to wound assessment using IRT in women undergoing caesarean section, we have shown that wound abdomen temperature differences (WATD) predict wound complications, notably surgical site infection (SSI) as early as at day 2 and at day 7 after surgery [3], time-points that precede overt signs of infection, typically around the second week after surgery. Furthermore, low skin temperature in the region of the wound, coupled with discrete 'cold spots' along the incision, have good predictive capability to indicate later SSI [3; 12; 13]. Thus, utilising the postoperative thermal signature to identify skin 'cold' spots, commensurate with low cutaneous temperature and corresponding reduction in blood flow, is a potential clinical tool which may help shed light on the underlying mechanisms leading to infective and non-infective wound complications.  
However, identifying one (or more) surface anatomy regions of interest (ROI) from the heat map is both labour-intensive and subjective, as is the refining and 'fine-tuning' of pixel level features. Problems emerge due to 'crowding' of objects, whereby pixel features are not easily assigned to the 'correct' bounding box or cluster group [14]. When overlap occurs between ROIs (such as that shown in Figure 1) [15], feature extraction from a specific ROI can be difficult and lead to misclassifications. To improve upon the reliability of our existing, manual, image post-processing and wound outcome modelling undertaken by a single, experienced researcher (CC) and reported previously [3], our overarching objective was to use the acquired images to refine and improve scope in image analysis using AI-based segmentation.  
The aim of this study therefore was twofold: (a) to compare instance segmentation and feature extraction agreement between two new and independent users after a single training session and (b) to determine the occurrence of thermal cold regions and'spots' across abdomen and wound, respectively.