A comparative study of CNN and U-Net performance for automatic segmentation of medical images: application to cardiac MRI

Abstract

Medical image segmentation is one of the most challenging tasks in medical image analysis which aims to extract effective information and improve the level of clinical diagnosis. In last decades, automatic segmentation based on Deep Learning (DL) models such as U-Net and CNN architectures have been widely used to automatically extract organs or lesions contours in order to overcome manual segmentation limitations. In this paper, we performed a comparative study between CNN and U-Net performance for medical image segmentation applied to cardiac-MRI segmentation using U-Net from short-axis MRI images of ACDC database. The adopted architecture was trained and tested with and without data augmentation. The obtained results show a strong agreement between the labelled masks and the predicted ones with a mean DSC that reached 97,9% and a mean Hausdorrf Distance (HD) that reached 5.318 mm. A quantitative comparison was made on two levels. The first one is an intra-comparison made between the adopted model and methods based on the same architecture and having been trained and tested using the same database which proved that our method reached the highest performance and can be considered as a promising tool for medical image segmentation. The second one is an inter-comparison made between U-Net and CNN performance which proved that U-Net is more suitable for carrying out this task since it takes less time for training as it does not have a fully connected layer and offers a fairly significant similarity to ground truth compared to CNN.