Highlights
- •X-ray synchrotron phase-contrast microtomography to obtain 3D images of soft tissues.
- •Phase-contrast effects to improve the visualization of Thoropa miliaris frog lenses.
- •Lenses segmentation methods: Interpolation, Watershed and U-Net architecture.
- •Dice similarity coefficient and volume quantification to assess segmentation methods.
Abstract
Purpose
In the context of synchrotron microtomography using propagation-based phase-contrast
imaging (XSPCT), we evaluated the performance of semiautomatic and automatic image
segmentation of soft biological structures by means of Dice Similarity Coefficient
(DSC) and volume quantification.
Methods
We took advantage of the phase-contrast effects of XSPCT to provide enhanced object
boundaries and improved visualization of the lenses of the frog Thoropa miliaris. Then, we applied semiautomatic segmentation methods 1 and 2 (Interpolation and Watershed,
respectively) and method 3, an automatic segmentation algorithm using the U-Net architecture,
to the reconstructed images. DSC and volume quantification of the lenses were used
to quantify the performance of image segmentation methods.
Results
Comparing the lenses segmented by the three methods, the most pronounced difference
in volume quantification was between methods 1 and 3: a reduction of 4.24%. Method
1, 2 and 3 obtained the global average DSC of 97.02%, 95.41% and 89.29%, respectively.
Although it obtained the lowest DSC, method 3 performed the segmentation in a matter
of seconds, while the semiautomatic methods had the average time to segment the lenses
around 1 h and 30 min.
Conclusions
Our results suggest that the performance of U-Net was impaired due to the irregularities
of the ROI edges mainly in its lower and upper regions, but it still showed high accuracy
(DSC = 89.29%) with significantly reduced segmentation time compared to the semiautomatic
methods. Besides, with the present work we have established a baseline for future
assessments of Deep Neural Networks applied to XSPCT volumes.
Keywords
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Article info
Publication history
Published online: January 05, 2022
Accepted:
December 20,
2021
Received in revised form:
November 16,
2021
Received:
August 12,
2021
Identification
Copyright
© 2021 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.
