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Performance evaluation of segmentation methods for assessing the lens of the frog Thoropa miliaris from synchrotron-based phase-contrast micro-CT images

Published:January 05, 2022DOI:https://doi.org/10.1016/j.ejmp.2021.12.013

      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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