Abstract
Delineation of curvilinear structures is an important
problem in Computer Vision with multiple practical applications. With the advent of Deep Learning, many current
approaches on automatic delineation have focused on finding more powerful deep architectures, but have continued
using the habitual pixel-wise losses such as binary crossentropy. In this paper we claim that pixel-wise losses alone
are unsuitable for this problem because of their inability to
reflect the topological impact of mistakes in the final prediction. We propose a new loss term that is aware of the higherorder topological features of linear structures. We also exploit a refinement pipeline that iteratively applies the same
model over the previous delineation to refine the predictions
at each step, while keeping the number of parameters and
the complexity of the model constant.
When combined with the standard pixel-wise loss, both
our new loss term and an iterative refinement boost the
quality of the predicted delineations, in some cases almost
doubling the accuracy as compared to the same classifier
trained with the binary cross-entropy alone. We show that
our approach outperforms state-of-the-art methods on a
wide range of data, from microscopy to aerial images