Automatic segmentation of kidneys in computed tomography images using U-Net

Accurate segmentation of target volumes and organs at risk from computed tomography (CT) images is essential for treatment planning in radiation therapy. The segmentation task is often done manually making it time-consuming. Besides, it is biased to the clinician experience and subject to inter-observer variability. Therefore, and due to the development of artificial intelligence tools and particularly deep learning (DL) algorithms, automatic segmentation has been proposed as an alternative. The purpose of this work is to use a DL-based method to segment the kidneys on CT images for radiotherapy treatment planning.

In this contribution, we used the CT scans of 20 patients. Segmentation of the kidneys was performed using the U-Net model. The Dice similarity coefficient (DSC), the Matthews correlation coefficient (MCC), the Hausdorff distance (HD), the sensitivity and the specificity were used to quantitatively evaluate this delineation.

This model was able to segment the organs with a good accuracy. The obtained values of the used metrics for the kidneys segmentation, were presented. Our results were also compared to those obtained recently by other authors.

Fully automated DL-based segmentation of CT images has the potential to improve both the speed and the accuracy of radiotherapy organs contouring.