Synchrotron X-ray biosample imaging: opportunities and challenges.

Synchrotron radiation phase-contrast microtomography is sensitive to low attenuating tissues, giving an alternative visualisation of the sample and being useful for investigating microstructure inside biological specimens without staining them with a contrast medium. The phase-contrast technique has been widely used in the scientific community, as it is a technique associated with radiography and microscopy and able to enhance contrast in soft tissues, specifically at the edges, showing details that could not be seen by the absorption technique. This work aims to show the ability of synchrotron-based phase-contrast microtomography for the visualisation of soft tissues and hard internal structures of millimetre-sized biological organisms. Case studies of the anatomy of Rhodnius prolixus head and Thoropa miliaris tadpole are presented to illustrate the imaging technique.

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

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.