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arXiv 2401.13472eess.IVcs.CV

使用深度神经网络分割心肌Z盘

Segmenting Cardiac Muscle Z-disks with Deep Neural Networks

University of Leeds（利兹大学）；University College London（伦敦大学学院）

Mihaela Croitor Ibrahim, Nishant Ravikumar, Alistair Curd, Joanna Leng, Oliver Umney, Michelle Peckham

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AI总结：

本文基于Airyscan共聚焦Z盘数据集评估多种深度分割模型，发现UNet++分割精度最高而pix2pix在titin突变数据上泛化更好，证明机器学习可自动量化Z盘形态。

摘要：

Z盘是界定横纹肌中重复肌节的复杂结构。它们在心肌细胞中发挥重要作用，例如为收缩的肌节提供机械稳定性、参与细胞信号传导和自噬。Z盘结构的变化已与心脏功能受损相关联。因此，迫切需要创建能够从显微图像中分割Z盘的工具，以克服图像亮度和染色技术差异等传统局限。在本研究中，我们应用基于深度学习的分割模型来提取横纹肌组织图像中的Z盘。我们利用一个新颖的Airyscan共聚焦数据集，其中包含健康心脏组织Z盘的高分辨率图像，这些图像使用针对特定Z盘蛋白的Affimers进行染色。我们采用交互式标注工具Ilastik获取真值分割掩膜，并使用所得数据集训练和评估若干最先进分割网络的性能。在测试集上，UNet++在心肌细胞Z盘分割中取得最佳性能，平均Dice得分为0.91，优于UNet、FPN、DeepLabv3+和pix2pix等其他成熟分割方法。然而，在一个带有titin突变的心肌细胞附加数据集上测试时，pix2pix表现出更好的泛化能力。这是首个证明基于机器学习的自动化分割方法可有效用于共聚焦显微图像中Z盘分割的研究。自动化分割方法和预测分割掩膜可用于提取Z盘的形态特征（如宽度和方向），并进而量化心脏微结构中与疾病相关的变化。

英文摘要：

Z-disks are complex structures that delineate repeating sarcomeres in striated muscle. They play significant roles in cardiomyocytes such as providing mechanical stability for the contracting sarcomere, cell signalling and autophagy. Changes in Z-disk architecture have been associated with impaired cardiac function. Hence, there is a strong need to create tools to segment Z-disks from microscopy images, that overcome traditional limitations such as variability in image brightness and staining technique. In this study, we apply deep learning based segmentation models to extract Z-disks in images of striated muscle tissue. We leverage a novel Airyscan confocal dataset, which comprises high resolution images of Z-disks of healthy heart tissue, stained with Affimers for specific Z-disk proteins. We employed an interactive labelling tool, Ilastik to obtain ground truth segmentation masks and use the resulting data set to train and evaluate the performance of several state-of-the-art segmentation networks. On the test set, UNet++ achieves best segmentation performance for Z-disks in cardiomyocytes, with an average Dice score of 0.91 and outperforms other established segmentation methods including UNet, FPN, DeepLabv3+ and pix2pix. However, pix2pix demonstrates improved generalisation, when tested on an additional dataset of cardiomyocytes with a titin mutation. This is the first study to demonstrate that automated machine learning-based segmentation approaches may be used effectively to segment Z-disks in confocal microscopy images. Automated segmentation approaches and predicted segmentation masks could be used to derive morphological features of Z-disks (e.g. width and orientation), and subsequently, to quantify disease-related changes to cardiac microstructure.

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