ArXiv Preprint
Focusing on the complicated pathological features, such as blurred
boundaries, severe scale differences between symptoms, background noise
interference, etc., in the task of retinal edema lesions joint segmentation
from OCT images and enabling the segmentation results more reliable. In this
paper, we propose a novel reliable multi-scale wavelet-enhanced transformer
network, which can provide accurate segmentation results with reliability
assessment. Specifically, aiming at improving the model's ability to learn the
complex pathological features of retinal edema lesions in OCT images, we
develop a novel segmentation backbone that integrates a wavelet-enhanced
feature extractor network and a multi-scale transformer module of our newly
designed. Meanwhile, to make the segmentation results more reliable, a novel
uncertainty segmentation head based on the subjective logical evidential theory
is introduced to generate the final segmentation results with a corresponding
overall uncertainty evaluation score map. We conduct comprehensive experiments
on the public database of AI-Challenge 2018 for retinal edema lesions
segmentation, and the results show that our proposed method achieves better
segmentation accuracy with a high degree of reliability as compared to other
state-of-the-art segmentation approaches. The code will be released on:
https://github.com/LooKing9218/ReliableRESeg.
Meng Wang, Kai Yu, Chun-Mei Feng, Ke Zou, Yanyu Xu, Qingquan Meng, Rick Siow Mong Goh, Yong Liu, Xinxing Xu, Huazhu Fu
2022-12-01
