@misc{9223791,
  abstract     = {{Medical image segmentation often relies either on traditional image processing methods, which can be limited in their ability to handle complex image characteristics, or on deep learning approaches, which require large amounts of high-quality annotated data. Hybrid methods that combine these two paradigms offer a potential way to reduce annotation requirements while maintaining strong segmentation performance. This thesis investigates such a hybrid approach by combining a gradient-based wavefront propagation method, namely the fast marching method, with a deep learning model. 

The central objective is to analyze how imperfect annotations generated by the fast marching method propagate through the deep learning model, and to assess how much and what type of supervision is sufficient to achieve reliable segmentation performance. To enable a systematic and controlled investigation, CT-like volumes of the myenteric plexus with known two-class ground truth are simulated. The fast marching method is then applied to generate segmentation masks under different conditions, including deliberately undersegmented and oversegmented configurations, which are subsequently used as imperfect supervision to train an Attention Res-UNet with Guided Decoder.

The results show that deep learning models trained on the fast marching–derived masks can substantially reduce false negatives recovering large missing regions, even when the annotations are highly incomplete. However, false positives tend to persist and may be amplified by the deep learning model. Overall, the findings demonstrate that fast marching can serve as an effective pre-processing step in a hybrid segmentation pipeline, provided that systematic oversegmentation is avoided. The work highlights the importance of understanding the error characteristics of supervisory masks and contributes to a more nuanced view of imperfect supervision in medical image segmentation.}},
  author       = {{Parkkola, Sara Lotta Alexandra}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master’s Theses in Mathematical Sciences}},
  title        = {{Learning Beyond Labels: Understanding the Role of Imperfect Supervision in Medical Image Segmentation}},
  year         = {{2026}},
}