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首页> 外文会议>International conference on artificial neural networks >Quantifying Structural Heterogeneity of Healthy and Cancerous Mitochondria Using a Combined Segmentation and Classification USK-Net
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Quantifying Structural Heterogeneity of Healthy and Cancerous Mitochondria Using a Combined Segmentation and Classification USK-Net

机译：使用组合的分段和分类USK-Net量化健康和癌性线粒体的结构异质性

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Mitochondria are the main source of cellular energy and thus essential for cell survival. Pathological conditions like cancer, can cause functional alterations and lead to mitochondrial dysfunction. Indeed, electron micrographs of mitochondria that are isolated from cancer cells show a different morphology as compared to mitochondria from healthy cells. However, the description of mitochondrial morphology and the classification of the respective samples are so far qualitative. Furthermore, large intra-class variability and impurities such as mitochondrial fragments and other organelles in the micrographs make a clear separation between healthy and cancerous samples challenging. In this study, we propose a deep-learning based model to quantitatively assess the status of each intact mitochondrion with a continuous score, which measures its closeness to the healthy/tumor classes based on its morphology. This allows us to describe the structural transition from healthy to cancerous mitochondria. Methodologically, we train two USK networks, one to segment individual mitochondria from an electron micrograph, and the other to softly classify each image pixel as belonging to (ⅰ) healthy mitochondrial, (ⅱ) cancerous mitochondrial and (ⅲ) non-mitochondrial (image background & impurities) tissue. Our combined model outperforms each network alone in both pixel classification and object segmentation. Moreover, our model can quantitatively assess the mitochondrial heterogeneity within and between healthy samples and different tumor types, hence providing insightful information of mitochondrial alterations in cancer development.
机译：线粒体是细胞能量的主要来源，因此对细胞存活至关重要。像癌症这样的病理状况，可能导致功能改变并导致线粒体功能障碍。实际上，从癌细胞分离的线粒体的电子显微照片与健康细胞的线粒体相比显示出不同的形态。然而，到目前为止，线粒体形态的描述和各个样品的分类是定性的。此外，显微照片中较大的类内变异性和杂质（例如线粒体片段和其他细胞器）使健康样品和癌性样品之间的清晰分离变得困难。在这项研究中，我们提出了一种基于深度学习的模型，以连续得分定量评估每个完整线粒体的状态，该得分根据其形态来衡量其与健康/肿瘤类别的接近程度。这使我们能够描述从健康线粒体到癌性线粒体的结构转变。从方法上讲，我们训练了两个USK网络，一个从电子显微照片上分割单个线粒体，另一个将每个图像像素柔和地分类为（ⅰ）健康线粒体，（ⅱ）癌性线粒体和（ⅲ）非线粒体（图像背景和杂质）组织。我们的组合模型在像素分类和对象分割方面均胜过每个网络。此外，我们的模型可以定量评估健康样品与不同肿瘤类型之间以及之间的线粒体异质性，从而为癌症发展中的线粒体变化提供了有见地的信息。

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