Abstract
Semantic segmentation is a crucial task in medical imaging.
Although supervised learning techniques have proven to be effective in
performing this task, they heavily depend on large amounts of annotated
training data. The recently introduced Segment Anything Model (SAM)
enables prompt-based segmentation and offers zero-shot generalization
to unfamiliar objects. In our work, we leverage SAM’s abstract object
understanding for medical image segmentation to provide pseudo labels
for semi-supervised learning, thereby mitigating the need for extensive
annotated training data.
Our approach refines initial segmentations that are derived from a lim-
ited amount of annotated data (comprising up to 43 cases) by extracting
bounding boxes and seed points as prompts forwarded to SAM. Thus,
it enables the generation of dense segmentation masks as pseudo labels
for unlabelled data. The results show that training with our pseudo la-
bels yields an improvement in Dice score from 74.29 % to 84.17 % and
from 66.63 % to 74.87 % for the segmentation of bones of the paedi-
atric wrist and teeth in dental radiographs, respectively. As a result, our
method outperforms intensity-based post-processing methods, state-of-
the-art supervised learning for segmentation (nnU-Net), and the semi-
supervised mean teacher approach. Our Code is available on GitHub.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/ADSMI2024_065.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: https://papers.miccai.org/miccai-2026-sat/supp/ADSMI2024_065_supp.pdf
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{KeuRon_SAM_MICCAISAT2026,
author = { Keuth, Ron AND Hansen, Lasse AND Balks, Maren AND Jager, Ronja AND Schroder, Anne-Nele AND Tushaus, Ludger AND Heinrich, Mattias P.},
title = { { SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17276},
month = {pending},
page = {pending}
}