  Manually segmenting the hepatic vessels from Computer Tomography (CT) is far
more expertise-demanding and laborious than other structures due to the
low-contrast and complex morphology of vessels, resulting in the extreme lack
of high-quality labeled data. Without sufficient high-quality annotations, the
usual data-driven learning-based approaches struggle with deficient training.
On the other hand, directly introducing additional data with low-quality
annotations may confuse the network, leading to undesirable performance
degradation. To address this issue, we propose a novel mean-teacher-assisted
confident learning framework to robustly exploit the noisy labeled data for the
challenging hepatic vessel segmentation task. Specifically, with the adapted
confident learning assisted by a third party, i.e., the weight-averaged teacher
model, the noisy labels in the additional low-quality dataset can be
transformed from "encumbrance" to "treasure" via progressive pixel-wise
soft-correction, thus providing productive guidance. Extensive experiments
using two public datasets demonstrate the superiority of the proposed framework
as well as the effectiveness of each component.
