368-02-013 ISMRM Abstract

Quantifying User Satisfaction: A Weighted Metric Approach for Evaluating Deep Learning-Based MRI Segmentations

Primary: Musculoskeletal - Muscle

Secondary: Analysis Methods - Segmentation and Detection

368-02-013 · Segmentation for Musculoskeletal MRI Applications · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row I

Keywords: Thigh muscles Deep Learning Segmentation Dixon MRI Deep Learning-based quantification

Accepted

Falko Ensle ¹, Ilker Özgür Koska^2,3, Nina Derron⁴, Ulf Bach¹, Cagan Koska⁵, Philipp Maintz¹, Marta Porta-Vilaro⁶, Jonas Kroschke¹, Philipp Gerber⁴, Roman Guggenberger⁷

¹Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland

²Department of Pediatric Radiology, Acıbadem Kent Hospital, Izmir, Turkey

³Biomedical Technologies Department, Dokuz Eylül University Engineering Faculty, Izmir, Turkey

⁴Department of Endocrinology, Diabetology and Clinical Nutrition, University Hospital Zurich, Zurich, Switzerland

⁵Department of Electrical Electronical Engineering Faculty, Yaşar University, Izmir, Turkey

⁶Department of Radiology, hospital clinic de barcelona, Barcelona, Spain

⁷Department of Radiology and Nuclear Medicine, Cantonal Hospital Winterthur, Winterthur, Switzerland

Presenting Author: Falko Ensle

Synopsis

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References

1. Zhang, Y., Shen, Z., & Jiao, R. (2024). Segment anything model for medical image segmentation: Current applications and future directions. Computers in Biology and Medicine, 171, 108238.

2. Müller, D., Soto-Rey, I., & Kramer, F. (2022). Towards a guideline for evaluation metrics in medical image segmentation. BMC Research Notes, 15(1), 210.

3. Nai, Y. H., Teo, B. W., Tan, N. L., O'Doherty, S., Stephenson, M. C., Thian, Y. L., ... & Reilhac, A. (2021). Comparison of metrics for the evaluation of medical segmentations using prostate MRI dataset. Computers in biology and medicine, 134, 104497.

4. Kirimtat, A., & Krejcar, O. (2023, June). A Guide and Mini-Review on the Performance Evaluation Metrics in Binary Segmentation of Magnetic Resonance Images. In International Work-Conference on Bioinformatics and Biomedical Engineering (pp. 428-440). Cham: Springer Nature Switzerland.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/368-02-013