Guidelines for Cerebrovascular Segmentation: Managing Imperfect
  Annotations in the context of Semi-Supervised Learning
Pierre Rougé Pierre-Henri Conze Nicolas Passat Odyssée Merveille
Segmentation in medical imaging is an essential and often preliminary task in
the image processing chain, driving numerous efforts towards the design of
robust segmentation algorithms. Supervised learning methods achieve excellent
performances when fed with a sufficient amount of labeled data. However, such
labels are typically highly time-consuming, error-prone and expensive to
produce. Alternatively, semi-supervised learning approaches leverage both
labeled and unlabeled data, and are very useful when only a small fraction of
the dataset is labeled. They are particularly useful for cerebrovascular
segmentation, given that labeling a single volume requires several hours for an
expert. In addition to the challenge posed by insufficient annotations, there
are concerns regarding annotation consistency. The task of annotating the
cerebrovascular tree is inherently ambiguous. Due to the discrete nature of
images, the borders and extremities of vessels are often unclear. Consequently,
annotations heavily rely on the expert subjectivity and on the underlying
clinical objective. These discrepancies significantly increase the complexity
of the segmentation task for the model and consequently impair the results.
Consequently, it becomes imperative to provide clinicians with precise
guidelines to improve the annotation process and construct more uniform
datasets. In this article, we investigate the data dependency of deep learning
methods within the context of imperfect data and semi-supervised learning, for
cerebrovascular segmentation. Specifically, this study compares various
state-of-the-art semi-supervised methods based on unsupervised regularization
and evaluates their performance in diverse quantity and quality data scenarios.
Based on these experiments, we provide guidelines for the annotation and
training of cerebrovascular segmentation models.