Boosting Sclera Segmentation through Semi-supervised Learning with Fewer Labels
Journal:
arXiv
Published Date:
Jan 13, 2025
Abstract
Sclera segmentation is crucial for developing automatic eye-related medical
computer-aided diagnostic systems, as well as for personal identification and
verification, because the sclera contains distinct personal features. Deep
learning-based sclera segmentation has achieved significant success compared to
traditional methods that rely on hand-crafted features, primarily because it
can autonomously extract critical output-related features without the need to
consider potential physical constraints. However, achieving accurate sclera
segmentation using these methods is challenging due to the scarcity of
high-quality, fully labeled datasets, which depend on costly, labor-intensive
medical acquisition and expertise. To address this challenge, this paper
introduces a novel sclera segmentation framework that excels with limited
labeled samples. Specifically, we employ a semi-supervised learning method that
integrates domain-specific improvements and image-based spatial transformations
to enhance segmentation performance. Additionally, we have developed a
real-world eye diagnosis dataset to enrich the evaluation process. Extensive
experiments on our dataset and two additional public datasets demonstrate the
effectiveness and superiority of our proposed method, especially with
significantly fewer labeled samples.
