Parameter-efficient Finetuning of Foundational Models for Text-guided X-ray Image Segmentation

Alikarrar M, Syben C, Scheuplein J, Hümmer C, Ritschl L, Kappler S, Maier A (2026)

Publication Type: Conference contribution

Publication year: 2026

Journal

Informatik aktuell Gesellschaft für Informatik (GI)

Publisher: IEEE

City/Town: New York City

Pages Range: 55-62

Conference Proceedings Title: Bildverarbeitung für die Medizin 2026

Event location: Lübeck DE

ISBN: 9783658510992

DOI: 10.1007/978-3-658-51100-5_10

Abstract

Radiographic image segmentation presents unique challenges due to overlapping anatomical structures, projection ambiguity, and the scarcity of high-quality annotations. Recently, segmentation foundation models such as MedSAM have emerged as powerful tools for automated medical image analysis. Trained on large-scale and diverse image-mask pairs, MedSAM has achieved broad generalization across a wide range of medical image segmentation tasks. Despite this, its exposure to X-rays was primarily limited to chest radiographs annotated with lung masks, and the model relied on spatial prompts like bounding boxes, which are labor-intensive to draw precisely during inference and prone to ambiguity. To overcome these limitations, we propose a parameter-efficient adaptation of MedSAM designed for X-ray image segmentation. The approach integrates lightweight low-rank adaptation (LoRA) fine-tuning to enable efficient model updating while incorporating text-based conditioning to guide mask prediction. This design facilitates intuitive, non-expert human interaction without requiring precise geometric prompts. Evaluated on internal chest and lower-limb radiographic datasets, the model achieves a mean Dice (mDice) score of 92.42 and a mean intersection-over-union (mIoU) of 86.46 while unfreezing only a small fraction of parameters. These results demonstrate that parameter-efficient, language-conditioned adaptation offers an effective strategy for enhancing segmentation performance in projection-based medical imaging.

Authors with CRIS profile

Christopher Syben Department of Neuroradiology Joshua Scheuplein Lehrstuhl für Informatik 5 (Mustererkennung) Christian Hümmer Lehrstuhl für Hochfrequenztechnik (LHFT) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Siemens Healthineers (Germany) DE Germany (DE)

How to cite

APA:

Alikarrar, M., Syben, C., Scheuplein, J., Hümmer, C., Ritschl, L., Kappler, S., & Maier, A. (2026). Parameter-efficient Finetuning of Foundational Models for Text-guided X-ray Image Segmentation. In Bildverarbeitung für die Medizin 2026 (pp. 55-62). Lübeck, DE: New York City: IEEE.

MLA:

Alikarrar, Maeen, et al. "Parameter-efficient Finetuning of Foundational Models for Text-guided X-ray Image Segmentation." Proceedings of the German Conference on Medical Image Computing, Lübeck New York City: IEEE, 2026. 55-62.

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