Grote, S. ter (Sterre) (2021) Semi-Automatic Volumetry of CT images of Polycystic Livers is as accurate as Manual Volumetry. thesis, Medicine.
Full text available on request.Abstract
Background: Polycystic Liver Disease (PLD) causes growth of cysts in the liver, resulting in an increase of the total liver volume (TLV). The current gold standard for measuring TLV is manual segmentation, which can take hours. Semi-automatic segmentation has the potential to perform uniform, and accurate measurements within five to ten minutes. The objective of this study was to validate the contour and density based semi-automatic segmentation programs (Syngo.via VOI and Volume MMWP, respectively). Methods: This study included CT scans of livers of patients diagnosed with PLD. The contour based program was compared to manual segmentation in 20 scans of 11 patients. The density based program was compared to manual segmentation in 17 scans of 10 patients. Manual segmentation by two observers was compared in 10 CT scans. A cross-sectional analysis was performed on measurements of TLV and a longitudinal analysis was performed on measurements of TLV growth. Results: In the cross-sectional analysis, bias and precision were low and comparable between the three types of measurement. The contour based program had a bias and precision of 2.5±3.2% and the density based program had a bias and precision of 0.5±3.4%. This is comparable to the cross-sectional analysis of multiobserver manual segmentation, where bias and precision was -1.1±3.4%. In the longitudinal analysis of TLV growth, bias and precision were -3.4±3.6% for measurements with the contour based program and 0.9±5.0% for measurements with the density based program. Conclusion: The performance of both the contour based program and the density based program is good and comparable to manual segmentation of the cystic liver. Because of satisfactory performance and the time-efficiency, the density based program is recommended for segmentation.
| Item Type: | Thesis (UNSPECIFIED) |
|---|---|
| Supervisor name: | Gansevoort, prof. dr. R. (Ron) and Aapkes, dr. S. (Sophie) |
| Faculty: | Medical Sciences |
| Date Deposited: | 04 Jan 2022 10:25 |
| Last Modified: | 04 Jan 2022 10:25 |
| URI: | https://umcg.studenttheses.ub.rug.nl/id/eprint/2932 |
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