SCANED: Siamese collateral assessment network for evaluation of collaterals from ischemic damage.
Comput Med Imaging Graph
; 113: 102346, 2024 04.
Article
en En
| MEDLINE
| ID: mdl-38364600
ABSTRACT
This study conducts collateral evaluation from ischemic damage using a deep learning-based Siamese network, addressing the challenges associated with a small and imbalanced dataset. The collateral network provides an alternative oxygen and nutrient supply pathway in ischemic stroke cases, influencing treatment decisions. Research in this area focuses on automated collateral assessment using deep learning (DL) methods to expedite decision-making processes and enhance accuracy. Our study employed a 3D ResNet-based Siamese network, referred to as SCANED, to classify collaterals as good/intermediate or poor. Utilizing non-contrast computed tomography (NCCT) images, the network automates collateral identification and assessment by analyzing tissue degeneration around the ischemic site. Relevant features from the left/right hemispheres were extracted, and Euclidean Distance (ED) was employed for similarity measurement. Finally, dichotomized classification of good/intermediate or poor collateral is performed by SCANED using an optimal threshold derived from ROC analysis. SCANED provides a sensitivity of 0.88, a specificity of 0.63, and a weighted F1 score of 0.86 in the dichotomized classification.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Isquemia Encefálica
/
Curva ROC
/
Accidente Cerebrovascular Isquémico
Límite:
Humans
Idioma:
En
Revista:
Comput Med Imaging Graph
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Estados Unidos