Beyond the Pixel-Wise Loss for Topology-Aware Delineation

Agata Mosinska, Pablo Márquez-Neila, Mateusz Koziński, Pascal Fua; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 3136-3145

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 cross-entropy. In this paper we claim that pixel-wise losses alone are unsuitable for this problem because of their inability to reflect the topological importance of prediction errors. Instead, we propose a new loss term that is aware of the higher-order topological features of the linear structures. We also introduce 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 iterative refinement boost the quality of the predicted delineations, in some cases almost doubling the accuracy as compared to the same classifier trained only with the binary cross-entropy. We show that our approach outperforms state-of-the-art methods on a wide range of data, from microscopy to aerial images.