Coronary Centerline Extraction from CCTA Using 3D-UNet

Future Internet

Remus Brad¹  Alexandru Dorobanțiu¹  Valentin Ogrean¹ 

[1] Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania;

关键词: CCTA;    centerline;    coronary artery segmentation;    deep learning;    U-NET;   

DOI  :  10.3390/fi13040101

来源: DOAJ

【 摘 要 】

The mesh-type coronary model, obtained from three-dimensional reconstruction using the sequence of images produced by computed tomography (CT), can be used to obtain useful diagnostic information, such as extracting the projection of the lumen (planar development along an artery). In this paper, we have focused on automated coronary centerline extraction from cardiac computed tomography angiography (CCTA) proposing a 3D version of U-Net architecture, trained with a novel loss function and with augmented patches. We have obtained promising results for accuracy (between 90–95%) and overlap (between 90–94%) with various network training configurations on the data from the Rotterdam Coronary Artery Centerline Extraction benchmark. We have also demonstrated the ability of the proposed network to learn despite the huge class imbalance and sparse annotation present in the training data.

【 授权许可】

Unknown