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Connected-UNets: a deep learning architecture for breast mass segmentation
Asma Baccouche1, Begonya Garcia-Zapirain2, Cristian Castillo Olea2
1Department of Computer Science and Engineering, University of Louisville, Louisville, KY, 40292, USA. asma.baccouche@louisville.edu.
NPJ Breast Cancer
|December 3, 2021
Summary
This study introduces Connected-UNets, an AI model for enhanced breast cancer mass segmentation in mammograms. The novel architecture improves diagnostic accuracy for radiologists by achieving high performance on public and private datasets.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Vision
Background:
- Radiologists analyze mammograms for breast cancer detection, identifying suspicious lesions and tumors.
- Artificial intelligence (AI) and deep learning offer automated breast mass segmentation to aid diagnosis.
- UNet and its variants are leading models for medical image segmentation, showing promise in mammography.
Purpose of the Study:
- To propose a novel deep learning architecture, Connected-UNets, for improved automatic breast mass segmentation.
- To integrate Atrous Spatial Pyramid Pooling (ASPP) within UNet architectures to enhance contextual information.
- To evaluate the proposed Connected-UNets architecture on standard and Attention/Residual UNet variations.
Main Methods:
- Developed Connected-UNets by linking two UNets with modified skip connections.
- Integrated ASPP into standard UNets, Attention UNets (AUNet), and Residual UNets (ResUNet).
- Utilized public (CBIS-DDSM, INbreast) and private datasets, augmented with synthetic data from CycleGAN.
Main Results:
- Achieved high Dice scores: 89.52% (CBIS-DDSM), 95.28% (INbreast), and 95.88% (private dataset).
- Obtained high Intersection over Union (IoU) scores: 80.02% (CBIS-DDSM), 91.03% (INbreast), and 92.27% (private dataset).
- Demonstrated superior automatic mass segmentation performance compared to existing methods.
Conclusions:
- The proposed Connected-UNets architecture significantly improves automatic breast mass segmentation accuracy.
- Integrating ASPP enhances the model's ability to capture contextual information for better segmentation.
- The findings support the use of advanced AI models like Connected-UNets in clinical mammography for improved breast cancer detection.
