Annotation-efficient learning for OCT segmentation
Haoran Zhang Jianlong Yang Ce Zheng Shiqing Zhao Aili Zhang
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
https://github.com/SJTU-Intelligent-Optics-Lab/Annotation-efficient-learning-for-OCT-segmentation.