Mitochondrial diseases are currently untreatable due to our limited understanding of their pathology. We study the expression of various mitochondrial proteins in skeletal myofibres (SM) in order to discover processes involved in mitochondrial pathology using Imaging Mass Cytometry (IMC). IMC produces high dimensional multichannel pseudo-images representing spatial variation in the expression of a panel of proteins within a tissue, including subcellular variation. Statistical analysis of these images requires semi-automated annotation of thousands of SMs in IMC images of patient muscle biopsies. In this paper we investigate the use of deep learning (DL) on raw IMC data to analyse it without any manual pre-processing steps, statistical summaries or statistical models. For this we first train state-of-art computer vision DL models on all available image channels, both combined and individually. We observed better than expected accuracy for many of these models. We then apply state-of-the-art explainable techniques relevant to computer vision DL to find the basis of the predictions of these models. Some of the resulting visual explainable maps highlight features in the images that appear consistent with the latest hypotheses about mitochondrial disease progression within myofibres.
翻译:目前,由于我们对其病理学的了解有限,密托昆地里疾病是无法解冻的。我们研究在骨骼肌肌肌肌肌膜短膜(SM)中各种线粒体蛋白的表达方式,以便利用成像大规模细胞测量(IMC)来发现线粒体病理过程。IMC生产高维多通道假象,代表一个组织内蛋白板表达中的空间差异,包括亚细胞变异。对这些图像的统计分析要求以半自动方式在IMC的病人肌肉生物素象像中注明数千个SMSM。在本文中,我们调查如何使用关于原始IMC数据的深度学习(DL)来分析它,而没有人工预处理步骤、统计摘要或统计模型。我们首次在全部可用的图像频道(包括集成和单独)上列程最先进的计算机视觉DL模型。我们观察到许多这些模型比预期的准确性要好。我们随后应用了与计算机图像DL有关的最新可解释技术。我们调查了原始IMC数据,以便找到这些模型的最新预测基础。