Cargando…
Generating Synthetic Labeled Data From Existing Anatomical Models: An Example With Echocardiography Segmentation
Deep learning can bring time savings and increased reproducibility to medical image analysis. However, acquiring training data is challenging due to the time-intensive nature of labeling and high inter-observer variability in annotations. Rather than labeling images, in this work we propose an alter...
Formato: | Online Artículo Texto |
---|---|
Lenguaje: | English |
Publicado: |
IEEE
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8493532/ https://www.ncbi.nlm.nih.gov/pubmed/33444134 http://dx.doi.org/10.1109/TMI.2021.3051806 |
Ejemplares similares
-
SinGAN-Seg: Synthetic training data generation for medical image segmentation
por: Thambawita, Vajira, et al.
Publicado: (2022) -
Synthetic Data Generation for Automatic Segmentation of X-ray Computed Tomography Reconstructions of Complex Microstructures
por: Tsamos, Athanasios, et al.
Publicado: (2023) -
Left Ventricle Segmentation in Echocardiography with Transformer
por: Liao, Minqi, et al.
Publicado: (2023) -
Practical Synthetic Data Generation
por: Emam, Khaled
Publicado: (2020) -
Anatomic Variants Mimicking Pathology on Echocardiography: Differential Diagnosis
por: Kim, Mi-Jeong, et al.
Publicado: (2013)