Automated segmentation of an intensity calibration phantom in clinical CT images using a convolutional neural network

by   Keisuke Uemura, et al.

Purpose: To apply a convolutional neural network (CNN) to develop a system that segments intensity calibration phantom regions in computed tomography (CT) images, and to test the system in a large cohort to evaluate its robustness. Methods: A total of 1040 cases (520 cases each from two institutions), in which an intensity calibration phantom (B-MAS200, Kyoto Kagaku, Kyoto, Japan) was used, were included herein. A training dataset was created by manually segmenting the regions of the phantom for 40 cases (20 cases each). Segmentation accuracy of the CNN model was assessed with the Dice coefficient and the average symmetric surface distance (ASD) through the 4-fold cross validation. Further, absolute differences of radiodensity values (in Hounsfield units: HU) were compared between manually segmented regions and automatically segmented regions. The system was tested on the remaining 1000 cases. For each institution, linear regression was applied to calculate coefficients for the correlation between radiodensity and the densities of the phantom. Results: After training, the median Dice coefficient was 0.977, and the median ASD was 0.116 mm. When segmented regions were compared between manual segmentation and automated segmentation, the median absolute difference was 0.114 HU. For the test cases, the median correlation coefficient was 0.9998 for one institution and was 0.9999 for the other, with a minimum value of 0.9863. Conclusions: The CNN model successfully segmented the calibration phantom's regions in the CT images with excellent accuracy, and the automated method was found to be at least equivalent to the conventional manual method. Future study should integrate the system by automatically segmenting the region of interest in bones such that the bone mineral density can be fully automatically quantified from CT images.


page 22

page 23

page 24

page 25

page 28


Automated femur segmentation from computed tomography images using a deep neural network

Osteoporosis is a common bone disease that occurs when the creation of n...

Automated segmentation of 3-D body composition on computed tomography

Purpose: To develop and validate a computer tool for automatic and simul...

Convolution Neural Network based Mode Decomposition for Degenerated Modes via Multiple Images from Polarizers

In this paper, a mode decomposition (MD) method for degenerated modes ha...

Deep learning based automatic segmentation of lumbosacral nerves on non-contrast CT for radiographic evaluation: a pilot study

Background and objective: Combined evaluation of lumbosacral structures ...

A deep learning-based method for relative location prediction in CT scan images

Relative location prediction in computed tomography (CT) scan images is ...

Automatic Weight Estimation of Harvested Fish from Images

Approximately 2,500 weights and corresponding images of harvested Lates ...

Code Repositories



view repo