Generalizable attention U-Net for segmentation of fibroglandular tissue and background parenchymal enhancement in breast DCE-MRI

ConclusionsGeneralizable algorithms for FGT and BPE segmentation were developed and tested. Our results suggest that when assessing FGT, it is sufficient to use volumetric measures alone. However, for the evaluation of BPE, additional models considering voxels ’ intensity distribution and morphology are required.Critical relevance statementA standardized assessment of FGT density can rely on volumetric measures, whereas in the case of BPE, the volumetric measures constitute, along with voxels ’ intensity distribution and morphology, an important factor.Key points• Our work contributes to the standardization of FGT and BPE assessment.• Attention U-Net can reliably segment intricately shaped FGT and BPE structures.• The developed models were robust to domain shift.Graphical Abstract
Source: Insights into Imaging - Category: Radiology Source Type: research