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首页> 外文会议>Conference on Applications of Machine Learning >Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks
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Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks

机译：通过卷积神经网络在角膜Guttata存在下自动化角膜内皮图像分割

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Automated cell counting in in-vivo specular microscopy images is challenging, especially in situations where single-cell segmentation methods fail due to pathological conditions. This work aims to obtain reliable cell segmentation from specular microscopy images of both healthy and pathological corneas. We cast the problem of cell segmentation as a supervised multi-class segmentation problem. The goal is to learn a mapping relation between an input specular microscopy image and its labeled counterpart, indicating healthy (cells) and pathological regions (e.g., guttae). We trained a U-net model by extracting 96 × 96 pixel patches from corneal endothelial cell images and the corresponding manual segmentation by a physician. Encouraging results show that the proposed method can deliver reliable feature segmentation enabling more accurate cell density estimations for assessing the state of the cornea.
机译：体内镜面显微镜图像中的自动细胞计数是具有挑战性的，特别是在单细胞分段方法由于病理条件而导致的情况下的情况。这项工作旨在从健康和病理角膜的镜面显微镜图像中获得可靠的细胞分段。我们将细胞分割问题作为监督多级分割问题施放。目标是学习输入镜面显微镜图像与其标记的对应物之间的映射关系，表明健康（细胞）和病理区域（例如，Guttae）。通过由医生提取来自角膜内皮细胞图像的96×96像素块和由医生的相应的手动分割来训练U-Net模型。令人鼓舞的结果表明，该方法可以提供可靠的特征分割，从而实现更准确的细胞密度估计来评估角膜的状态。

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