A novel open-source ultrasound dataset with deep learning benchmarks for spinal cord injury localization and anatomical segmentation

Abstract While deep learning has catalyzed breakthroughs across numerous domains, its broader adoption in clinical settings is inhibited by the costly and time-intensive nature of data acquisition and annotation. To further facilitate medical machine learning, we present an ultrasound dataset of 10,...

Descripció completa

Dades bibliogràfiques

Autors principals:	Avisha Kumar, Kunal Kotkar, Kelly Jiang, Meghana Bhimreddy, Daniel Davidar, Carly Weber-Levine, Siddharth Krishnan, Max J. Kerensky, Ruixing Liang, Kelley K. Leadingham, Denis Routkevitch, Andrew M. Hersh, Kimberly Ashayeri, Betty Tyler, Ian Suk, Jennifer Son, Nicholas Theodore, Nitish Thakor, Amir Manbachi
Format:	Artigo
Idioma:	Inglês
Publicat:	Nature Portfolio 2025-09-01
Col·lecció:	Scientific Reports
Matèries:	Object detection Semantic segmentation Medical dataset Ultrasound Spinal cord injury
Accés en línia:	https://doi.org/10.1038/s41598-025-16275-z
Etiquetes:	Afegir etiqueta Sense etiquetes, Sigues el primer a etiquetar aquest registre!