Robust multi-vendor breast region segmentation using deep learning
| Autor: | Dercksen, K., Kallenberg, Michiel, Kroes, Jaap, Bosmans, H. |
|---|---|
| Přispěvatelé: | Bosmans, H. |
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer science business.industry Deep learning Data Science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION CAD Pattern recognition medicine.disease Thresholding Breast cancer Computer-aided diagnosis medicine Preprocessor Segmentation Artificial intelligence business |
| Zdroj: | Bosmans, H. (ed.), IWBI2020, 1+. Washington : SPIE STARTPAGE=1+;ISSN=0277-786X;TITLE=Bosmans, H. (ed.), IWBI2020 Bosmans, H. (ed.), IWBI2020, pp. 1 |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.2573086 |
| Popis: | Semantic segmentation of breast images is typically performed as a preprocessing step for breast cancer detection by Computer Aided Diagnosis (CAD) systems. While most literature on region segmentation is based on conventional techniques like line estimation, thresholding and atlas-based approaches, such methods may have problems with generalisation. This paper investigates a robust multi-vendor breast region segmentation system for full field digital mammograms (FFDM) and digital breast tomography (DBT) using a U-Net neural network. Additionally, the effect of adding attention gates to the U-Net architecture was analysed. The proposed networks were trained and tested in a cross-validation setting on in-house FFDM/DBT data and the public INbreast datasets, comprising over 10,000 FFDM and 3,500 DBT images from five different vendors. Dice scores were obtained in the range 0.978 - 0.985, with slightly higher scores for the architecture that includes attention gates. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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