<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

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

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 Public License.

The achievement of high-precision segmentation in medical image analysis has been an active direction of research over the past decade. Significant success in...

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

Published Online: Jun 05, 2020

Page range: 43 - 50

DOI: https://doi.org/10.2478/acss-2020-0005

Keywords
Computed tomography volumetric images, fully convolutional neural networks, medical image analysis, multi-organ segmentation, Sørensen-Dice similarity coefficient

© 2020 Pavlo Radiuk, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

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

Published Online: Jun 05, 2020

Page range: 43 - 50

DOI: https://doi.org/10.2478/acss-2020-0005

KeywordsComputed tomography volumetric images, fully convolutional neural networks, medical image analysis, multi-organ segmentation, Sørensen-Dice similarity coefficient

© 2020 Pavlo Radiuk, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Keywords
Computed tomography volumetric images, fully convolutional neural networks, medical image analysis, multi-organ segmentation, Sørensen-Dice similarity coefficient