Semi-automated weak annotation for deep neural network skin thickness measurement.
Ultrason Imaging
; 43(4): 167-174, 2021 07.
Article
en En
| MEDLINE
| ID: mdl-33971769
ABSTRACT
Correctly calculating skin stiffness with ultrasound shear wave elastography techniques requires an accurate measurement of skin thickness. We developed and compared two algorithms, a thresholding method and a deep learning method, to measure skin thickness on ultrasound images. Here, we also present a framework for weakly annotating an unlabeled dataset in a time-effective manner to train the deep neural network. Segmentation labels for training were proposed using the thresholding method and validated with visual inspection by a human expert reader. We reduced decision ambiguity by only inspecting segmentations at the center A-line. This weak annotation approach facilitated validation of over 1000 segmentation labels in 2 hours. A lightweight deep neural network that segments entire 2D images was designed and trained on this weakly-labeled dataset. Averaged over six folds of cross-validation, segmentation accuracy was 57% for the thresholding method and 78% for the neural network. In particular, the network was better at finding the distal skin margin, which is the primary challenge for skin segmentation. Both algorithms have been made publicly available to aid future applications in skin characterization and elastography.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
/
Redes Neurales de la Computación
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Ultrason Imaging
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos