Institute of Neuropathology, University Hospitals, Erlangen, Germany.
Department of (Neuro)Pathology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
Epilepsia. 2020 Mar;61(3):421-432. doi: 10.1111/epi.16447. Epub 2020 Feb 20.
The microscopic review of hematoxylin-eosin-stained images of focal cortical dysplasia type IIb and cortical tuber of tuberous sclerosis complex remains challenging. Both entities are distinct subtypes of human malformations of cortical development that share histopathological features consisting of neuronal dyslamination with dysmorphic neurons and balloon cells. We trained a convolutional neural network (CNN) to classify both entities and visualize the results. Additionally, we propose a new Web-based deep learning application as proof of concept of how deep learning could enter the pathologic routine.
A digital processing pipeline was developed for a series of 56 cases of focal cortical dysplasia type IIb and cortical tuber of tuberous sclerosis complex to obtain 4000 regions of interest and 200 000 subsamples with different zoom and rotation angles to train a neural network. Guided gradient-weighted class activation maps (Guided Grad-CAMs) were generated to visualize morphological features used by the CNN to distinguish both entities.
Our best-performing network achieved 91% accuracy and 0.88 area under the receiver operating characteristic curve at the tile level for an unseen test set. Novel histopathologic patterns were found through the visualized Guided Grad-CAMs. These patterns were assembled into a classification score to augment decision-making in routine histopathology workup. This score was successfully validated by 11 expert neuropathologists and 12 nonexperts, boosting nonexperts to expert level performance.
Our newly developed Web application combines the visualization of whole slide images with the possibility of deep learning-aided classification between focal cortical dysplasia IIb and tuberous sclerosis complex. This approach will help to introduce deep learning applications and visualization for the histopathologic diagnosis of rare and difficult-to-classify brain lesions.
对 IIb 型局灶性皮质发育不良和结节性硬化症皮质结节的苏木精-伊红染色图像进行微观检查仍然具有挑战性。这两种病变都是人类皮质发育畸形的不同亚型，具有相似的组织病理学特征，包括神经元分层异常、形态异常神经元和气球样细胞。我们训练了一个卷积神经网络（CNN）来对这两种病变进行分类并可视化结果。此外，我们提出了一个新的基于网络的深度学习应用程序，作为深度学习如何进入病理常规的概念验证。
我们为 56 例 IIb 型局灶性皮质发育不良和结节性硬化症皮质结节的病例开发了一个数字处理流水线，以获得 4000 个感兴趣区域和 200000 个具有不同缩放和旋转角度的子样本，以训练神经网络。生成引导梯度加权类激活图（Guided Grad-CAMs），以可视化 CNN 用于区分这两种病变的形态学特征。
我们表现最好的网络在未见过的测试集中达到了 91%的准确率和 0.88 的接收器工作特征曲线下面积。通过可视化引导梯度加权类激活图发现了新的组织病理学模式。这些模式被组合成一个分类评分，以增强常规组织病理学工作流程中的决策。该评分得到了 11 位专家神经病理学家和 12 位非专家的成功验证，将非专家的表现提升到了专家水平。
我们新开发的网络应用程序将全切片图像的可视化与深度学习辅助分类相结合，用于 IIb 型局灶性皮质发育不良和结节性硬化症。这种方法将有助于引入深度学习应用程序和可视化，用于罕见和难以分类的脑病变的组织病理学诊断。
