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首页> 外文会议>IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops >MULTILAYER ENCODER-DECODER NETWORK FOR 3D NUCLEAR SEGMENTATION IN SPHEROID MODELS OF HUMAN MAMMARY EPITHELIAL CELL LINES
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MULTILAYER ENCODER-DECODER NETWORK FOR 3D NUCLEAR SEGMENTATION IN SPHEROID MODELS OF HUMAN MAMMARY EPITHELIAL CELL LINES

机译：用于3D核细分的多层编码器 - 解码网络人类乳腺上皮细胞术中的三维核细分

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摘要

Nuclear segmentation is an important step in quantitative profiling of colony organization in 3D cell culture models. However, complexities arise from technical variations and biological heterogeneities. We proposed a new 3D segmentation model based on convolutional neural networks for 3D nuclear segmentation, which overcomes the complexities associated with non-uniform staining, aberrations in cellular morphologies, and cells being in different states. The uniqueness of the method originates from (i) volumetric operations to capture all the threedimensional features, and (ii) the encoder-decoder architecture, which enables segmentation of the spheroid models in one forward pass. The method is validated with four human mammary epithelial cell (HMEC) lines—each with unique genetic makeup. The performance of the proposed method is compared with the previous methods and is shown that the deep learning model has a superior pixel-based segmentation, and an F1-score of 0.95 is reported.
机译：核细分是3D细胞培养模型中菌落组织定量分析的重要步骤。然而，从技术变异和生物异质性产生复杂性。我们提出了一种基于卷积神经网络的基于3D核细分的新的3D分割模型，其克服了与非均匀染色，细胞形态的畸变相关的复杂性，以及细胞处于不同状态。该方法的唯一性来自（i）体积操作以捕获所有三维功能，（ii）编码器解码器架构，其能够在一个转发通行证中能够分割球体模型。该方法用四种人乳腺上皮细胞（HMEC）线进行验证，每条术线具有独特的遗传构成。将所提出的方法的性能与先前的方法进行比较，并示出了深度学习模型具有优越的基于像素的分段，并且报告了0.95的F1分数。

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