Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization.
Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization.
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作者：
Kainz P;Pfeiffer M;Urschler M
组织病理学切片的分割是数字病理学必要的预处理步骤。由于生物组织的巨大变异性，机器学习技术显示出优于传统图像处理方法的性能。在这里，我们提出了基于深度神经网络的方法，用于对良性和恶性结直肠癌组织中的腺体进行分割和分类，该方法是为了参加 GlaS@MICCAI2015 结肠腺分割挑战而开发的。我们使用两个不同的深度卷积神经网络（CNN）对苏木精-伊红染色图像进行像素级分类。第一个分类器将腺体与背景分离，而第二个分类器则识别腺体分离结构。在后续步骤中，基于加权总变差的图形-背景分割通过正则化 CNN 预测来产生最终分割结果。我们展示了最近发布且公开的与 GlaS@MICCAI2015 挑战相关的 Warwick-QU 结肠腺癌数据集的定量和定性分割结果，并将我们的方法与参与同一挑战的同时开发的其他方法进行比较。在两个测试集上，我们展示了我们的分割性能，并表明我们利用我们系统的固有能力来区分良性和恶性组织，实现了 98% 和 95% 的组织分类准确度。我们的结果表明，深度学习方法可以为生物医学图像分析产生高度准确且可重复的结果，并有可能显着提高医疗诊断的质量和速度。
Segmentation of histopathology sections is a necessary preprocessing step for digital pathology. Due to the large variability of biological tissue, machine learning techniques have shown superior performance over conventional image processing methods. Here we present our deep neural network-based approach for segmentation and classification of glands in tissue of benign and malignant colorectal cancer, which was developed to participate in the GlaS@MICCAI2015 colon gland segmentation challenge. We use two distinct deep convolutional neural networks (CNN) for pixel-wise classification of Hematoxylin-Eosin stained images. While the first classifier separates glands from background, the second classifier identifies gland-separating structures. In a subsequent step, a figure-ground segmentation based on weighted total variation produces the final segmentation result by regularizing the CNN predictions. We present both quantitative and qualitative segmentation results on the recently released and publicly available Warwick-QU colon adenocarcinoma dataset associated with the GlaS@MICCAI2015 challenge and compare our approach to the simultaneously developed other approaches that participated in the same challenge. On two test sets, we demonstrate our segmentation performance and show that we achieve a tissue classification accuracy of 98% and 95%, making use of the inherent capability of our system to distinguish between benign and malignant tissue. Our results show that deep learning approaches can yield highly accurate and reproducible results for biomedical image analysis, with the potential to significantly improve the quality and speed of medical diagnoses.