| Autor: |
Sylwia Nowakowska, Karol Borkowski, Carlotta M. Ruppert, Anna Landsmann, Magda Marcon, Nicole Berger, Andreas Boss, Alexander Ciritsis, Cristina Rossi |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Insights into Imaging, Vol 14, Iss 1, Pp 1-11 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1869-4101 |
| DOI: |
10.1186/s13244-023-01531-5 |
| Popis: |
Abstract Objectives Development of automated segmentation models enabling standardized volumetric quantification of fibroglandular tissue (FGT) from native volumes and background parenchymal enhancement (BPE) from subtraction volumes of dynamic contrast-enhanced breast MRI. Subsequent assessment of the developed models in the context of FGT and BPE Breast Imaging Reporting and Data System (BI-RADS)-compliant classification. Methods For the training and validation of attention U-Net models, data coming from a single 3.0-T scanner was used. For testing, additional data from 1.5-T scanner and data acquired in a different institution with a 3.0-T scanner was utilized. The developed models were used to quantify the amount of FGT and BPE in 80 DCE-MRI examinations, and a correlation between these volumetric measures and the classes assigned by radiologists was performed. Results To assess the model performance using application-relevant metrics, the correlation between the volumes of breast, FGT, and BPE calculated from ground truth masks and predicted masks was checked. Pearson correlation coefficients ranging from 0.963 ± 0.004 to 0.999 ± 0.001 were achieved. The Spearman correlation coefficient for the quantitative and qualitative assessment, i.e., classification by radiologist, of FGT amounted to 0.70 (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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