The ability of Segmenting Anything Model (SAM) to segment ultrasound images.
Biosci Trends
; 17(3): 211-218, 2023 Jul 11.
Article
en En
| MEDLINE
| ID: mdl-37344392
ABSTRACT
Accurate ultrasound (US) image segmentation is important for disease screening, diagnosis, and prognosis assessment. However, US images typically have shadow artifacts and ambiguous boundaries that affect US segmentation. Recently, Segmenting Anything Model (SAM) from Meta AI has demonstrated remarkable potential in a wide range of applications. The purpose of this paper was to conduct an initial evaluation of the ability for SAM to segment US images, particularly in the event of shadow artifacts and ambiguous boundaries. We evaluated SAM's performance on three US datasets of different tissues, including multi-structure cardiac tissue, thyroid nodules, and the fetal head. Results indicated that SAM generally performs well with US images with clear tissue structures, but it has limited performance in the event of shadow artifacts and ambiguous boundaries. Thus, creating an improved SAM that considers the characteristics of US images is significant for automatic and accurate US segmentation.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
Biosci Trends
Asunto de la revista:
BIOLOGIA
Año:
2023
Tipo del documento:
Article
País de afiliación:
China