1
|
Paiva K, Meneses AADM, Barcellos R, Moura MSDS, Mendes G, Fidalgo G, Sena G, Colaço G, Silva HR, Braz D, Colaço MV, Barroso RC. Performance evaluation of segmentation methods for assessing the lens of the frog Thoropa miliaris from synchrotron-based phase-contrast micro-CT images. Phys Med 2022; 94:43-52. [PMID: 34995977 DOI: 10.1016/j.ejmp.2021.12.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 11/16/2021] [Accepted: 12/20/2021] [Indexed: 12/15/2022] Open
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.
Collapse
Affiliation(s)
- Katrine Paiva
- Laboratory of Applied Physics to Biomedical and Environmental Sciences, Physics Institute, State University of Rio de Janeiro, Rio de Janeiro, Brazil.
| | | | - Renan Barcellos
- Laboratory of Applied Physics to Biomedical and Environmental Sciences, Physics Institute, State University of Rio de Janeiro, Rio de Janeiro, Brazil; Nuclear Engineering Program/COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Gabriela Mendes
- Laboratory of Applied Physics to Biomedical and Environmental Sciences, Physics Institute, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Gabriel Fidalgo
- Laboratory of Applied Physics to Biomedical and Environmental Sciences, Physics Institute, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Gabriela Sena
- Nuclear Engineering Program/COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Gustavo Colaço
- Laboratory of Herpetology, Institute of Biological and Health Sciences, Federal Rural University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Hélio Ricardo Silva
- Laboratory of Herpetology, Institute of Biological and Health Sciences, Federal Rural University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Delson Braz
- Nuclear Engineering Program/COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Marcos Vinicius Colaço
- Laboratory of Applied Physics to Biomedical and Environmental Sciences, Physics Institute, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Regina Cely Barroso
- Laboratory of Applied Physics to Biomedical and Environmental Sciences, Physics Institute, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| |
Collapse
|
2
|
Li Y, Parkinson DY, Feng J, Xia CH, Gong X. Quantitative X-ray tomographic analysis reveals calcium precipitation in cataractogenesis. Sci Rep 2021; 11:17401. [PMID: 34465795 PMCID: PMC8408149 DOI: 10.1038/s41598-021-96867-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 08/12/2021] [Indexed: 02/07/2023] Open
Abstract
Cataracts, named for pathological light scattering in the lens, are known to be associated with increased large protein aggregates, disrupted protein phase separation, and/or osmotic imbalances in lens cells. We have applied synchrotron phase contrast X-ray micro-computed tomography to directly examine an age-related nuclear cataract model in Cx46 knockout (Cx46KO) mice. High-resolution 3D X-ray tomographic images reveal amorphous spots and strip-like dense matter precipitates in lens cores of all examined Cx46KO mice at different ages. The precipitates are predominantly accumulated in the anterior suture regions of lens cores, and they become longer and dense as mice age. Alizarin red staining data confirms the presence of calcium precipitates in lens cores of all Cx46KO mice. This study indicates that the spatial and temporal calcium precipitation is an age-related event associated with age-related nuclear cataract formation in Cx46KO mice, and further suggests that the loss of Cx46 promotes calcium precipitates in the lens core, which is a new mechanism that likely contributes to the pathological light scattering in this age-related cataract model.
Collapse
Affiliation(s)
- Yuxing Li
- Vision Science Program and School of Optometry, University of California, Berkeley, 693 Minor Hall, Berkeley, CA, 94720-2020, USA
- Tsinghua-Berkeley Shenzhen Institute (TBSI), UC Berkeley, Berkeley, CA, USA
| | - Dilworth Y Parkinson
- Advanced Light Source Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jun Feng
- Advanced Light Source Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Chun-Hong Xia
- Vision Science Program and School of Optometry, University of California, Berkeley, 693 Minor Hall, Berkeley, CA, 94720-2020, USA
| | - Xiaohua Gong
- Vision Science Program and School of Optometry, University of California, Berkeley, 693 Minor Hall, Berkeley, CA, 94720-2020, USA.
| |
Collapse
|
3
|
Tkachev SY, Mitrin BI, Karnaukhov NS, Sadyrin EV, Voloshin MV, Maksimov AY, Goncharova AS, Lukbanova EA, Zaikina EV, Volkova AV, Khodakova DV, Mindar MV, Yengibarian MA, Protasova TP, Kit SO, Ermakov AM, Chapek SV, Tkacheva MS. Visualization of different anatomical parts of the enucleated human eye using X-ray micro-CT imaging. Exp Eye Res 2020; 203:108394. [PMID: 33310058 DOI: 10.1016/j.exer.2020.108394] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Revised: 11/18/2020] [Accepted: 12/07/2020] [Indexed: 12/01/2022]
Abstract
Micro-CT visualization allows reconstruction of eye structures with the resolution of light microscopy and estimation of tissue densities. Moreover, this method excludes damaging procedures and allows further histological staining due to the similar steps in the beginning. We have shown the feasibility of the lab-based micro-CT machine usage for visualization of clinically important compartments of human eye such as trabecular outflow pathway, retina, iris and ciliary body after pre-treatment with iodine in ethanol. We also identified the challenges of applying this contrasting technique to lens, cornea, and retina and proposed alternative staining methods for these tissues. Thereby this work provides a starting point for other studies for imaging of human eyes in normal and pathological conditions using lab-based micro-CT systems.
Collapse
Affiliation(s)
- Sergey Y Tkachev
- National Medical Research Centre for Oncology, Rostov-on-Don, Russia.
| | | | | | | | | | - Alexey Y Maksimov
- National Medical Research Centre for Oncology, Rostov-on-Don, Russia
| | - Anna S Goncharova
- National Medical Research Centre for Oncology, Rostov-on-Don, Russia
| | | | | | | | - Darya V Khodakova
- National Medical Research Centre for Oncology, Rostov-on-Don, Russia
| | - Maria V Mindar
- National Medical Research Centre for Oncology, Rostov-on-Don, Russia
| | | | | | - Sergey O Kit
- National Medical Research Centre for Oncology, Rostov-on-Don, Russia
| | | | | | - Marina S Tkacheva
- National Medical Research Centre for Oncology, Rostov-on-Don, Russia
| |
Collapse
|