Abstract
Background
Segmentation of medical image volumes is a time-consuming manual task. Automatic tools are often tailored toward specific patient cohorts, and it is unclear how they behave in other clinical settings.
Purpose
To evaluate the performance of the open-source Multi-Planar U-Net (MPUnet), the validated Knee Imaging Quantification (KIQ) framework, and a state-of-the-art two-dimensional (2D) U-Net architecture on three clinical cohorts without extensive adaptation of the algorithms.
Segmentation of medical image volumes is a time-consuming manual task. Automatic tools are often tailored toward specific patient cohorts, and it is unclear how they behave in other clinical settings.
Purpose
To evaluate the performance of the open-source Multi-Planar U-Net (MPUnet), the validated Knee Imaging Quantification (KIQ) framework, and a state-of-the-art two-dimensional (2D) U-Net architecture on three clinical cohorts without extensive adaptation of the algorithms.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Journal of Magnetic Resonance Imaging |
| Vol/bind | 55 |
| Udgave nummer | 2 |
| Sider (fra-til) | 1650-1663 |
| ISSN | 1053-1807 |
| DOI | |
| Status | Udgivet - 2022 |