Active Learning and Proofreading for Delineation of Curvilinear Structures

TL;DR

This paper introduces an active learning and proofreading framework that prioritizes critical regions in delineating curvilinear structures, reducing manual annotation effort and improving accuracy in microscopy image analysis.

Contribution

It presents a novel approach that guides annotation and verification by assessing the impact of potential misclassifications on delineation topology.

Findings

Reduces manual annotation by focusing on critical regions

Improves delineation accuracy in microscopy images

Effective in blood vessel and neuron segmentation

Abstract

Many state-of-the-art delineation methods rely on supervised machine learning algorithms. As a result, they require manually annotated training data, which is tedious to obtain. Furthermore, even minor classification errors may significantly affect the topology of the final result. In this paper we propose a generic approach to addressing both of these problems by taking into account the influence of a potential misclassification on the resulting delineation. In an Active Learning context, we identify parts of linear structures that should be annotated first in order to train a classifier effectively. In a proofreading context, we similarly find regions of the resulting reconstruction that should be verified in priority to obtain a nearly-perfect result. In both cases, by focusing the attention of the human expert on potential classification mistakes which are the most critical parts of the delineation, we reduce the amount of required supervision. We demonstrate the effectiveness of our approach on microscopy images depicting blood vessels and neurons.