Automated Classification for Visual-Only Postmortem Inspection of Porcine Pathology
Automated Classification for Visual-Only Postmortem Inspection of Porcine Pathology
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DOI：
10.1109/tase.2019.2960106
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发表时间：
2020-04-01
影响因子：
5.6
通讯作者：
Kyriazakis, Ilias
中科院分区：
文献类型：
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作者：
McKenna, Stephen;Amaral, Telmo;Kyriazakis, Ilias
猪尸体病理学的自动化检测将产生几个优点，包括避免人类观察者之间的主观性和可变性的固有风险。在这里，我们开发了一种新的自动化分类的两个猪内脏病理屠宰场：一个局灶性的，本地化的肝脏病理和心脏的弥漫性病理，作为例子。我们开发了一个基于机器学习的模式识别系统，以识别那些表现出感兴趣的病理迹象的器官。具体来说，深度神经网络被训练来生成概率热图，突出显示可能受给定条件影响的器官表面区域。最后的分类阶段，然后根据从热图计算的统计数据来决定给定器官是否受到所讨论的条件的影响。我们将自动分类的结果与专家病理学家的分类结果进行了比较。结果显示，肝脏和心脏病理的分类与专家一致，其水平相当于或超过专家间一致性。使用本文所述方法的系统有可能克服基于人类的屠宰场检查的局限性，特别是如果这是基于仅视觉检查，并最终为病理学提供新的黄金标准。从业员注意-本文的动机反映了目前对屠宰场牲畜尸体的目视检查的要求，以及需要提供识别尸体病理的金标准。仅目视检查的动机是需要通过人工触诊减少胴体之间的交叉污染，但这导致检查员内部和检查员之间的检测准确性存在很大差异。这对公共卫生有重大影响。在这里，我们提出了一个系统，包括硬件捕获图像的猪内脏和软件来分析这些图像，并确定案件的肝乳斑和心脏心包炎。它可以对高比例的内脏进行分类，其准确性与在猪病理学方面具有丰富经验的兽医相当，从而证明了克服基于人类的屠宰场检查的局限性（特别是如果它仅是视觉检查）并最终提供新的黄金标准的潜力。我们的工作是第一个解决猪内脏检查自动化的工作，从而揭示了与适当的图像捕获和成功的图像分析相关的挑战，例如需要科普正常和患病器官外观的广泛变化，以及不同类型的病变及其对专家需要付出多少努力才能产生训练系统所需的数据的影响。未来的工作方向应该包括扩展系统以识别更多的病理，并实施实时系统以科普生产线速度。
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.