Optimization of segmentation for true lumen, false lumen, and false lumen thrombus in type B aortic dissection using the 2D fine-tuning U-Net method

Segmentation approaches with 3D representation data face significant challenges, particularly in terms of contrast and resolution, which tend to decrease when the image is enlarged. These challenges can negatively impact segmentation performance, which requires high performance and accuracy for medical applications. To address this, this study proposes the use of image segmentation from 3D representation data to a 2D slice representation approach that offers higher sharpness and resolution because it can capture details at the slice level. Type B aortic dissection is a very dangerous disease of the aorta. In the context of medical image segmentation for cases of type B aortic dissection, previous studies have often only examined two main components: False Lumen (FL) and True Lumen (TL). However, the presence of a False Lumen Thrombus (FLT) is a critical factor that can endanger patients for the treatment and diagnosis of type B aortic dissection. Therefore, this study proposes a more comprehensive segmentation with 2D slice representation, including TL, FL, and FLT in type B aortic dissection. Through 2D slice representation, this study successfully maintained good image resolution and contrast and achieved more optimal segmentation accuracy compared to previous works by using the 2D fine-tuning U-Net method and appropriate activation function settings. The final results of this study show the highest dice score (DSC) with the use of the ReLU activation function, reaching 96% for the false lumen, 91% for the true lumen, and 92% for the false lumen thrombus.