Weakly-Supervised Vessel Detection in Ultra-Widefield Fundus Photography
Via Iterative Multi-Modal Registration and Learning
Abstract
We propose a deep-learning based annotation efficient framework for
vessel detection in ultra-widefield (UWF) fundus photography (FP) that
does not require de novo labeled UWF FP vessel maps. Our approach
utilizes concurrently captured UWF fluorescein angiography (FA) images,
for which effective deep learning approaches have recently become
available, and iterates between a multi-modal registration step and a
weakly-supervised learning step. In the registration step, the UWF FA
vessel maps detected with a pre-trained deep neural network (DNN) are
registered with the UWF FP via parametric chamfer alignment. The warped
vessel maps can be used as the tentative training data but inevitably
contain incorrect (noisy) labels due to the differences between FA and
FP modalities and the errors in the registration. In the learning step,
a robust learning method is proposed to train DNNs with noisy labels.
The detected FP vessel maps are used for the registration in the
following iteration. The registration and the vessel detection benefit
from each other and are progressively improved. Once trained, the UWF FP
vessel detection DNN from the proposed approach allows FP vessel
detection without requiring concurrently captured UWF FA images. We
validate the proposed framework on a new UWF FP dataset, PRIMEFP20, and
on existing narrow field FP datasets. Experimental evaluation, using
both pixel wise metrics and the CAL metrics designed to provide better
agreement with human assessment, shows that the proposed approach
provides accurate vessel detection, without requiring manually labeled
UWF FP training data.