论文标题
基于单个标记图像的胎盘组织学图像的自动分割和形态表征
Automated segmentation and morphological characterization of placental histology images based on a single labeled image
论文作者
论文摘要
在这项研究中，当标记的数据稀缺时，已经提出了一种新型的数据增强方法来分割胎盘组织学图像。该方法在保持一般纹理和方向的同时，产生了胎盘间隔形态的新实现。结果，生成了多元化的图像数据集，可用于训练深度学习分割模型。我们已经观察到，与文献相比，验证数据集的二进制跨透明镜丢失的二进制跨透镜损失平均会导致42％的降低。此外，在提出的图像重建技术的效果下研究了间隔空间的形态，并量化了人工产生的种群的多样性。由于生成的图像与真实图像的相似度很高，因此所提出的方法的应用可能不限于胎盘组织学图像，建议在未来的研究中研究其他类型的组织。
In this study, a novel method of data augmentation has been presented for the segmentation of placental histological images when the labeled data are scarce. This method generates new realizations of the placenta intervillous morphology while maintaining the general textures and orientations. As a result, a diversified artificial dataset of images is generated that can be used for training deep learning segmentation models. We have observed that on average the presented method of data augmentation led to a 42% decrease in the binary cross-entropy loss of the validation dataset compared to the common approach in the literature. Additionally, the morphology of the intervillous space is studied under the effect of the proposed image reconstruction technique, and the diversity of the artificially generated population is quantified. Due to the high resemblance of the generated images to the real ones, the applications of the proposed method may not be limited to placental histological images, and it is recommended that other types of tissues be investigated in future studies.
