Deep learning has been successfully applied to OCT segmentation. However, for
data from different manufacturers and imaging protocols, and for different
regions of interest (ROIs), it requires laborious and time-consuming data
annotation and training, which is undesirable in many scenarios, such as
surgical navigation and multi-center clinical trials. Here we propose an
annotation-efficient learning method for OCT segmentation that could
significantly reduce annotation costs. Leveraging self-supervised generative
learning, we train a Transformer-based model to learn the OCT imagery. Then we
connect the trained Transformer-based encoder to a CNN-based decoder, to learn
the dense pixel-wise prediction in OCT segmentation. These training phases use
open-access data and thus incur no annotation costs, and the pre-trained model
can be adapted to different data and ROIs without re-training. Based on the
greedy approximation for the k-center problem, we also introduce an algorithm
for the selective annotation of the target data. We verified our method on
publicly-available and private OCT datasets. Compared to the widely-used U-Net
model with 100% training data, our method only requires ~10% of the data for
achieving the same segmentation accuracy, and it speeds the training up to ~3.5
times. Furthermore, our proposed method outperforms other potential strategies
that could improve annotation efficiency. We think this emphasis on learning
efficiency may help improve the intelligence and application penetration of
OCT-based technologies. Our code and pre-trained model are publicly available
at
this https URL

本文提出了一种基于自监督生成学习的、注释高效的 OCT 图像细分方法，该方法可以显著降低注释成本并加速训练，相比于 U-Net 模型，只需要约 10% 的数据就能达到相同的分割精度。