  Deep learning techniques for 3D brain vessel image segmentation have not been
as successful as in the segmentation of other organs and tissues. This can be
explained by two factors. First, deep learning techniques tend to show poor
performances at the segmentation of relatively small objects compared to the
size of the full image. Second, due to the complexity of vascular trees and the
small size of vessels, it is challenging to obtain the amount of annotated
training data typically needed by deep learning methods. To address these
problems, we propose a novel annotation-efficient deep learning vessel
segmentation framework. The framework avoids pixel-wise annotations, only
requiring weak patch-level labels to discriminate between vessel and non-vessel
2D patches in the training set, in a setup similar to the CAPTCHAs used to
differentiate humans from bots in web applications. The user-provided weak
annotations are used for two tasks: 1) to synthesize pixel-wise pseudo-labels
for vessels and background in each patch, which are used to train a
segmentation network, and 2) to train a classifier network. The classifier
network allows to generate additional weak patch labels, further reducing the
annotation burden, and it acts as a noise filter for poor quality images. We
use this framework for the segmentation of the cerebrovascular tree in
Time-of-Flight angiography (TOF) and Susceptibility-Weighted Images (SWI). The
results show that the framework achieves state-of-the-art accuracy, while
reducing the annotation time by ~77% w.r.t. learning-based segmentation methods
using pixel-wise labels for training.
