Prostate segmentation by Guerbet
About
Summary
This model is based on nnU-Net for segmenting the prostate gland. The algorithm was trained on the PI-CAI dataset of 1500 patients, on a 5-fold cross-validation manner. It was trained by Guerbet IA team as part of their participation in the PI-CAI challenge.
Mechanism
Input:
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Data type: axial T2w MRI, DWI with high b-value MRI, and ADC map.
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File format: NIfTI or MHA format, containing pixel spacing information.
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Target population: Men without prostactomy or resection.
Output: - Binary mask of the segmented prostate gland.
Model type: Baseline nnU-Net was trained with 5-fold cross-validation. Fold splits were given by the PI-CAI challenge.
Interfaces
This algorithm implements all of the following input-output combinations:
Inputs | Outputs | |
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1 |
Validation and Performance
On cross-validation dataset, nnU-Net performing prostate gland segmentation showed a mean overall 3D patient level dice of:
Fold | 3D Dice |
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Fold 0 | 0.968 |
Fold 1 | 0.968 |
Fold 2 | 0.972 |
Fold 3 | 0.966 |
Fold 4 | 0.970 |
Uses and Directions
This algorithm was developed for research purposes only.