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Automatic segmentation of eyeball structures from micro-CT images based on sparse annotation

机译：基于稀疏注释的微型CT图像自动分割眼球结构

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A surgical simulator with elaborate artificial eyeball models has been developed for ophthalmic surgeries, in which sophisticated skills are required. To create the elaborate eyeball models with microstructures included in an eyeball, a database of eyeball models should be compiled by segmenting eye structures based on high-resolution medical images. Therefore, this paper presents an automated segmentation of eye structures from micro-CT images by using Fully Convolutional Networks (FCNs). In particular, we aim to construct a method for accurately segmenting eye structures from sparse annotation data. This method performs end-to-end segmentation of eye structures, including a workflow from training the FCN based on sparse annotation to obtaining the segmentation of the entire eyeball. We use the FCN trained on the slices sparsely annotated in a micro-CT volume to segment the remaining slices in the same volume. To achieve accurate segmentation from less annotated images, the multi-class segmentation is performed by using the network trained on the preprocessed and augmented micro-CT images; in the preprocessing, we apply filters for removing ring artifacts and random noises to the images, while in the data augmentation process, rotation and elastic deformation operations are performed on the sparsely-annotated training data. From the results of experiments for evaluating segmentation performances based on sparse annotation, we found that the FCN trained with data augmentation could achieve high segmentation accuracy of more than 90% even from a sparse training subset of only 2.5% of all slices.
机译：具有精心制造的人工眼球模型的手术模拟器已经为眼科手术开发，其中需要精致的技能。为了创建具有在眼球中的微观结构的细节模型，应通过基于高分辨率医学图像进行分割眼结构来编制眼球模型的数据库。因此，本文通过使用完全卷积网络（FCN）呈现来自微CT图像的眼结构的自动分割。特别是，我们的目的是构造一种用于从稀疏注释数据准确地分割眼结构的方法。该方法执行眼结构的端到端分割，包括基于稀疏注释来训练FCN的工作流程，以获得整个眼球的分割。我们使用培训的FCN在微型CT卷中稀疏地注释的切片上，以在相同的体积中段。为了从较少的注释图像实现精确的分割，通过使用在预处理和增强的微CT图像上培训的网络来执行多类分段;在预处理中，我们应用用于去除环形伪影和对图像的随机噪声的滤波器，而在数据增强过程中，对稀疏注释的训练数据执行旋转和弹性变形操作。根据基于稀疏注释评估分割性能的实验结果，我们发现，即使从所有切片中只有2.5％的稀疏训练子集，也可以获得具有数据增强的FCN超过90％的高分分割精度。

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