Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images.
Int J Comput Assist Radiol Surg
; 19(5): 971-981, 2024 May.
Article
en En
| MEDLINE
| ID: mdl-38478204
ABSTRACT
PURPOSE:
The assessment of vulnerable plaque characteristics and distribution is important to stratify cardiovascular risk in a patient. Computed tomography angiography (CTA) offers a promising alternative to invasive imaging but is limited by the fact that the range of Hounsfield units (HU) in lipid-rich areas overlaps with the HU range in fibrotic tissue and that the HU range of calcified plaques overlaps with the contrast within the contrast-filled lumen. This paper is to investigate whether lipid-rich and calcified plaques can be detected more accurately on cross-sectional CTA images using deep learning methodology.METHODS:
Two deep learning (DL) approaches are proposed, a 2.5D Dense U-Net and 2.5D Mask-RCNN, which separately perform the cross-sectional plaque detection in the Cartesian and polar domain. The spread-out view is used to evaluate and show the prediction result of the plaque regions. The accuracy and F1-score are calculated on a lesion level for the DL and conventional plaque detection methods.RESULTS:
For the lipid-rich plaques, the median and mean values of the F1-score calculated by the two proposed DL methods on 91 lesions were approximately 6 and 3 times higher than those of the conventional method. For the calcified plaques, the F1-score of the proposed methods was comparable to those of the conventional method. The median F1-score of the Dense U-Net-based method was 3% higher than that of the conventional method.CONCLUSION:
The two methods proposed in this paper contribute to finer cross-sectional predictions of lipid-rich and calcified plaques compared to studies focusing only on longitudinal prediction. The angular prediction performance of the proposed methods outperforms the convincing conventional method for lipid-rich plaque and is comparable for calcified plaque.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Placa Aterosclerótica
/
Angiografía por Tomografía Computarizada
/
Aprendizaje Profundo
Límite:
Humans
/
Male
Idioma:
En
Revista:
Int J Comput Assist Radiol Surg
Asunto de la revista:
RADIOLOGIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Países Bajos