Prostate segmentation by Guerbet
About
Interfaces
This algorithm implements all of the following input-output combinations:
| Inputs | Outputs | |
|---|---|---|
| 1 |
|
|
Model Facts
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:
-
Data type: axial T2w MRI, DWI with high b-value MRI, and ADC map.
-
File format: NIfTI or MHA format, containing pixel spacing information.
-
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.
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 |
|---|---|
| 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.
Warnings
Common Error Messages
Information on this algorithm has been provided by the Algorithm Editors, following the Model Facts labels guidelines from Sendak, M.P., Gao, M., Brajer, N. et al. Presenting machine learning model information to clinical end users with model facts labels. npj Digit. Med. 3, 41 (2020). 10.1038/s41746-020-0253-3