Fraunhofer Institute for Digital Medicine MEVISResearch Interests
- Deep Learning
- Ultrasound Data Analysis
Hannah Strohm
Publications
2025
Perotti L, Stamm O, Strohm H, Jenne J, Fournelle M, Lahmann N, Müller-Werdan U (2025) Learning Transversus Abdominis Activation in Older Adults with Chronic Low Back Pain Using an Ultrasound-Based Wearable: A Randomized Controlled Pilot Study. Jfmk 10(1):14
Speicher D, Grün T, Weber S, Hewener H, Klesy S, Rumanus S, Strohm H, Stamm O, Perotti L, Tretbar SH, Fournelle M (2025) Wearable 256-Element MUX-Based Linear Array Transducer for Monitoring of Deep Abdominal Muscles. Applied Sciences 15(7):3600
2024
Strohm H, Rothluebbers S, Perotti L, Stamm O, Fournelle M, Jenne J, Guenther M (2024) Contraction assessment of abdominal muscles using automated segmentation designed for wearable ultrasound applications. IJCARS 19:1607–1614
2023
Walluscheck S, Canalini L, Strohm H, Diekmann S, Klein J, Heldmann S (2023) MR-CT multi-atlas registration guided by fully automated brain structure segmentation with CNNs. Int J CARS 18:483–491
2022
Strohm H, Kuhlen V, Jenne J, Günther M, Rothlübbers S (2022) Effect of Geometric and Transmit Corrections on Global Speed of Sound Estimation. Proceedings of the IEEE International Ultrasonics Symposium (IUS). pp 1–4
2021
Hyun D, Wiacek A, Goudarzi S, Rothluebbers S, Asif A, Eickel K, Eldar Y, Huang J, Mischi M Rivaz H, Sinden D, van Sloun RJG, Strohm H, Bell MAL (2021) Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework & Open Datasets. IEEE Trans Ultrason Ferroelectr Freq Control 68(12):3466–3483
2020
Rothluebbers S, Strohm H, Eickel K, Jenne J, Kuhlen V, Sinden D, Günther M (2020) Improving Image Quality of Single Plane Wave Ultrasound via Deep Learning Based Channel Compounding. IEEE International Ultrasonics Symposium
Strohm H, Rothlübbers S, Eickel K, Günther M (2020) Deep learning-based reconstruction of ultrasound images from raw channel data. Int J CARS 15:1487–1490
Strohm H, Rothluebbers S, Eickel K, Günther M (2020) Beyond Classical Ultrasound Contrast via Deep Neural Networks. IEEE International Ultrasonics Symposium