Towards fully automated segmentation of rat cardiac MRI by leveraging deep learning frameworks

Abstract

Automated segmentation of human cardiac magnetic resonance datasets has been steadily improving during recent years. Similar applications would be highly useful to improve and speed up the studies of cardiac function in rodents in the preclinical context. However, the transfer of such segmentation methods to the preclinical research is compounded by the limited number of datasets and lower image resolution. In this paper we present a successful application of deep architectures 3D cardiac segmentation for rats in preclinical contexts which to our knowledge has not yet been reported. We developed segmentation models that expand on the standard U-Net architecture and evaluated models separately trained for systole and diastole phases (2MSA) and a single model trained for all phases (1MSA). Furthermore, we calibrated model outputs using a Gaussian process (GP)-based prior to improve phase selection. The resulting models approach human performance in terms of left ventricular segmentation quality and ejection fraction (EF) estimation in both 1MSA and 2MSA settings (Sørensen-Dice score 0.91 ± 0.072 and 0.93 ± 0.032, respectively). 2MSA achieved a mean absolute difference between estimated and reference EF of 3.5 ± 2.5%, while 1MSA resulted in 4.1 ± 3.0%. Applying GPs to 1MSA enabled automating systole and diastole phase selection. Both segmentation approaches (1MSA and 2MSA) were statistically equivalent. Combined with a proposed cardiac phase selection strategy, our work presents an important first step towards a fully automated segmentation pipeline in the context of rat cardiac analysis.

Automatic brain extraction for rat magnetic resonance imaging data using U2-Net

“…Recently, with the introduction of deep learning, this artificial intelligence-based technology has been widely used in various fields (Shattuck and Leahy 2002, Chou et al 2011, Oguz et al 2011. Compared with traditional machine learning technology, deep learning is based on convolutional neural network, and does not require manual extraction of features (Chen et al 2022, Fernández-Llaneza et al 2022. The neural network used in deep learning automatically extracts the features in the data and is widely used in the field of image segmentation (Alam et al 2022, Ruan et al 2022.…”