Breast and fibroglandular tissue segmentation
About
Summary
This algorithm produces a volumetric segmentation mask for the fatty tissue and the fibroglandular tissue (FGT) in the breast given as input either a fat (FS) or without fat suppression (WOFS) T1 breast MR acquisition. The output of this algorithm allows to easily calculate breast density from a FS or WOFS T1 breast MR acquisition.
Mechanism
The algorithm is a fully automated deep learning segmentation network. The network architecture is based on the nnUNet framework.
For more details, please refer to our article: Exploiting the Dixon Method for a Robust Breast and Fibro-Glandular Tissue Segmentation in Breast MRI
Interfaces
This algorithm implements all of the following input-output combinations:
Validation and Performance
We compared a network trained with only without fat-suppression (WOFS-only) T1 breast MR acquisitions and a network trained with both WOFS and FS (WOFS + FS) T1 breast MRI acquisitions. Below you can find the Dice Similarity Coefficient (DSC) on 9 held-out test cases from our internal dataset for both networks.
| Network trained on | Test | Breast DSC [95% CI] | FGT DSC [95% CI] |
|---|---|---|---|
| WOFS-only | WOFS | 0.96 [0.94, 0.97] | 0.92 [0.89, 0.95] |
| FS | 0.10 [0.08, 0.12] | 0.15 [0.11, 0.19] | |
| WOFS + FS | WOFS | 0.96 [0.95, 0.97] | 0.91 [0.87, 0.94] |
| FS | 0.95 [0.94, 0.96] | 0.86 [0.82, 0.91] |
Uses and Directions
This algorithm was developed for research purposes only.