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Segmentation of histological images and fibrosis identification with a convolutional neural network

机译：用卷积神经网络分割组织学图像和纤维化识别

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摘要

Segmentation of histological images is one of the most crucial tasks for many biomedical analyses involving quantification of certain tissue types, such as fibrosis via Masson's trichrome staining. However, challenges are posed by the high variability and complexity of structural features in such images, in addition to imaging artifacts. Further, the conventional approach of manual thresholding is labor-intensive, and highly sensitive to inter- and intra-image intensity variations. An accurate and robust automated segmentation method is of high interest. We propose and evaluate an elegant convolutional neural network (CNN) designed for segmentation of histological images, particularly those with Masson's trichrome stain. The network comprises 11 successive convolutional rectified linear unit - batch normalization layers. It outperformed state-of-the-art CNNs on a dataset of cardiac histological images (labeling fibrosis, myocytes, and background) with a Dice similarity coefficient of 0.947. With 100 times fewer (only 300,000) trainable parameters than the state-of-the-art, our CNN is less susceptible to overfitting, and is efficient. Additionally, it retains image resolution from input to output, captures fine-grained details, and can be trained end-to-end smoothly. To the best of our knowledge, this is the first deep CNN tailored to the problem of concern, and may potentially be extended to solve similar segmentation tasks to facilitate investigations into pathology and clinical treatment.
机译：组织学图像的分割是许多生物医学分析最重要的任务之一，涉及某些组织类型的定量，例如通过Masson的三色染色的纤维化。然而，除了成像伪影之外，这些图像中的结构特征的高变形和复杂性是挑战。此外，手动阈值的传统方法是劳动密集型，对图像间和图像内强度变化非常敏感。准确且坚固的自动分割方法具有高兴趣。我们提出并评估了优雅的卷积神经网络（CNN），专为组织学图像分割，特别是具有Masson的三色染色的细分。该网络包括11个连续的卷积整流线性单元 - 批量归一化层。它在心脏组织学图像（标记纤维化，肌细胞和背景）的数据集中优于最先进的CNN，其骰子相似度系数为0.947。由于最先进的培训参数（我们的CNN较少100倍，我们的CNN易受过度装备的影响，并且有效。此外，它保留从输入到输出的图像分辨率，捕获细粒细节，可以平滑地训练端到端。据我们所知，这是对令人担忧的问题量身定制的第一个深度CNN，可能会扩展以解决类似的细分任务，以方便调查病理和临床治疗。

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