Applying 3D U-Net Architecture to the Task of Multi-Organ Segmentation in Computed Tomography

2020
Pavlo Radiuk

The achievement of high-precision segmentation in medical image analysis has been an active direction of research over the past decade. Significant success in medical imaging tasks has been feasible due to the employment of deep learning methods, including convolutional neural networks (CNNs). Convolutional architectures have been mostly applied to homogeneous medical datasets with separate organs. Nevertheless, the segmentation of volumetric medical images of several organs remains an open question. In this paper, we investigate fully convolutional neural networks (FCNs) and propose a modified 3D U-Net architecture devoted to the processing of computed tomography (CT) volumetric images in the automatic semantic segmentation tasks. To benchmark the architecture, we utilised the differentiable Sørensen-Dice similarity coefficient (SDSC) as a validation metric and optimised it on the training data by minimising the loss function. Our hand-crafted architecture was trained and tested on the manually compiled dataset of CT scans. The improved 3D UNet architecture achieved the average SDSC score of 84.8 % on testing subset among multiple abdominal organs. We also compared our architecture with recognised state-of-the-art results and demonstrated that 3D U-Net based architectures could achieve competitive performance and efficiency in the multi-organ segmentation task.

Keywords
Computed tomography volumetric images; fully convolutional neural networks; medical image analysis; multi-organ segmentation; Sørensen-Dice similarity coefficient
DOI
10.2478/acss-2020-0005
Hyperlink
https://content.sciendo.com/view/journals/acss/25/1/article-p43.xml

Radiuk, P. Applying 3D U-Net Architecture to the Task of Multi-Organ Segmentation in Computed Tomography. Applied Computer Systems, 2020, Vol. 25, No. 1, pp. 43-50. ISSN 2255-8683. e-ISSN 2255-8691. Available from: doi:10.2478/acss-2020-0005

Publication language
English (en)