Automated Classification for Visual-Only Postmortem Inspection of Porcine Pathology

Abstract

Several advantages would arise from the automated detection of pathologies of pig carcasses, including avoidance of the inherent risks of subjectivity and variability between human observers. Here, we develop a novel automated classification of two porcine offal pathologies at abattoir: a focal, localized pathology of the liver and a diffuse pathology of the heart, as cases in point. We develop a pattern recognition system based on machine learning to identify those organs that exhibit signs of the pathology of interest. Specifically, deep neural networks are trained to produce probability heat maps, highlighting regions on the surface of an organ potentially affected by a given condition. A final classification stage then decides whether a given organ is affected by the condition in question based on statistics computed from the heat map. We compare outcomes of automated classification with classification by expert pathologists. Results show the classification of liver and heart pathologies in agreement with an expert at levels comparable to, or exceeding, interexpert agreement. A system using methods such as those presented here has potential to overcome the limitations of human-based abattoir inspection, especially if this is based on visual-only inspection, and ultimately to provide a new gold standard for pathology. Note to Practitioners —The motivation for this article reflects the current requirement for visual-only inspection of livestock carcasses at slaughter houses and the need to provide a gold standard for recognition of carcass pathologies. Visual-only inspection is motivated by the need to reduce cross contamination between carcasses by manual palpation, but this leads to substantial variability in detection accuracy both within and between inspectors. This has significant public health implications. Here we present a system that comprises hardware to capture images of pig offal and software to analyze those images and identify cases of liver milk spots and hearts affected by pericarditis. It can classify high proportions of offal with accuracy comparable to that of veterinarians with extensive experience in pig pathology, thus demonstrating the potential to overcome the limitations of human-based abattoir inspection (especially if it is visual-only) and ultimately to provide a new gold standard. Our work is the first to address the automation of pig offal inspection, thus shedding light on the challenges associated with both appropriate image capture and successful image analysis, such as the need to cope with wide variations in the appearance of both normal and diseased organs, as well as different types of lesions and their impact on how much effort is required from experts in order to produce data needed to train the system. Future directions of work should include extending the system to identify more pathologies and implementing a real-time system to cope with production line speed.