Image- versus histogram-based considerations in semantic segmentation of pulmonary hyperpolarized gas images.
Magn Reson Med
; 86(5): 2822-2836, 2021 11.
Article
en En
| MEDLINE
| ID: mdl-34227163
ABSTRACT
PURPOSE:
To characterize the differences between histogram-based and image-based algorithms for segmentation of hyperpolarized gas lung images.METHODS:
Four previously published histogram-based segmentation algorithms (ie, linear binning, hierarchical k-means, fuzzy spatial c-means, and a Gaussian mixture model with a Markov random field prior) and an image-based convolutional neural network were used to segment 2 simulated data sets derived from a public (n = 29 subjects) and a retrospective collection (n = 51 subjects) of hyperpolarized 129Xe gas lung images transformed by common MRI artifacts (noise and nonlinear intensity distortion). The resulting ventilation-based segmentations were used to assess algorithmic performance and characterize optimization domain differences in terms of measurement bias and precision.RESULTS:
Although facilitating computational processing and providing discriminating clinically relevant measures of interest, histogram-based segmentation methods discard important contextual spatial information and are consequently less robust in terms of measurement precision in the presence of common MRI artifacts relative to the image-based convolutional neural network.CONCLUSIONS:
Direct optimization within the image domain using convolutional neural networks leverages spatial information, which mitigates problematic issues associated with histogram-based approaches and suggests a preferred future research direction. Further, the entire processing and evaluation framework, including the newly reported deep learning functionality, is available as open source through the well-known Advanced Normalization Tools ecosystem.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Semántica
/
Isótopos de Xenón
Tipo de estudio:
Observational_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Magn Reson Med
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos