open access

Vol 25, No 6 (2020)
Original research articles
Published online: 2020-11-01
Submitted: 2020-05-26
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Convolutional neural network-based automatic liver delineation on contrast-enhanced and non-contrast-enhanced CT images for radiotherapy planning

Naohiro Sakashita, Kiyonori Shirai, Yoshihiro Ueda, Ayuka Ono, Teruki Teshima
DOI: 10.1016/j.rpor.2020.09.005
·
Rep Pract Oncol Radiother 2020;25(6):981-986.

open access

Vol 25, No 6 (2020)
Original research articles
Published online: 2020-11-01
Submitted: 2020-05-26

Abstract

Aim

This study evaluated a convolutional neural network (CNN) for automatically delineating the liver on contrast-enhanced or non-contrast-enhanced CT, making comparisons with a commercial automated technique (MIM Maestro®).

Background

Intensity-modulated radiation therapy requires careful labor-intensive planning involving delineation of the target and organs on CT or MR images to ensure delivery of the effective dose to the target while avoiding organs at risk.

Materials and Methods

Contrast-enhanced planning CT images from 101 pancreatic cancer cases and accompanying mask images showing manually-delineated liver contours were used to train the CNN to segment the liver. The trained CNN then performed liver segmentation on a further 20 contrast-enhanced and 15 non-contrastenhanced CT image sets, producing three-dimensional mask images of the liver.

Results

For both contrast-enhanced and non-contrast-enhanced images, the mean Dice similarity coefficients between CNN segmentations and ground-truth manual segmentations were significantly higher than those between ground-truth and MIM Maestro software (p < 0.001). Although mean CT values of the liver were higher on contrast-enhanced than on non-contrast-enhanced CT, there were no significant differences in the Hausdorff distances of the CNN segmentations, indicating that the CNN could successfully segment the liver on both image types, despite being trained only on contrast-enhanced images.

Conclusions

Our results suggest that a CNN can perform highly accurate automated delineation of the liver on CT images, irrespective of whether the CT images are contrast-enhanced or not.

Abstract

Aim

This study evaluated a convolutional neural network (CNN) for automatically delineating the liver on contrast-enhanced or non-contrast-enhanced CT, making comparisons with a commercial automated technique (MIM Maestro®).

Background

Intensity-modulated radiation therapy requires careful labor-intensive planning involving delineation of the target and organs on CT or MR images to ensure delivery of the effective dose to the target while avoiding organs at risk.

Materials and Methods

Contrast-enhanced planning CT images from 101 pancreatic cancer cases and accompanying mask images showing manually-delineated liver contours were used to train the CNN to segment the liver. The trained CNN then performed liver segmentation on a further 20 contrast-enhanced and 15 non-contrastenhanced CT image sets, producing three-dimensional mask images of the liver.

Results

For both contrast-enhanced and non-contrast-enhanced images, the mean Dice similarity coefficients between CNN segmentations and ground-truth manual segmentations were significantly higher than those between ground-truth and MIM Maestro software (p < 0.001). Although mean CT values of the liver were higher on contrast-enhanced than on non-contrast-enhanced CT, there were no significant differences in the Hausdorff distances of the CNN segmentations, indicating that the CNN could successfully segment the liver on both image types, despite being trained only on contrast-enhanced images.

Conclusions

Our results suggest that a CNN can perform highly accurate automated delineation of the liver on CT images, irrespective of whether the CT images are contrast-enhanced or not.

Get Citation

Keywords

CT; Neural network; Radiation therapy planning

About this article
Title

Convolutional neural network-based automatic liver delineation on contrast-enhanced and non-contrast-enhanced CT images for radiotherapy planning

Journal

Reports of Practical Oncology and Radiotherapy

Issue

Vol 25, No 6 (2020)

Pages

981-986

Published online

2020-11-01

DOI

10.1016/j.rpor.2020.09.005

Bibliographic record

Rep Pract Oncol Radiother 2020;25(6):981-986.

Keywords

CT
Neural network
Radiation therapy planning

Authors

Naohiro Sakashita
Kiyonori Shirai
Yoshihiro Ueda
Ayuka Ono
Teruki Teshima

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