Enhancing Breast Cancer Segmentation in Mammography with UNet++ - Deep Learning Approach

Kimia  Jalalian; Golnaz  Hosseini; Razieh  Ghiasi; Alireza  Bosaghzadeh

doi:10.18502/fbt.v12i4.19818

Kimia Jalalian Department of Biomedical Engineering, Pooyesh Institute of Higher Education, Qom, Iran
Golnaz Hosseini Department of Biomedical Engineering, Pooyesh Institute of Higher Education, Qom, Iran
Razieh Ghiasi Department of Computer Engineering, Qom University, Qom, Iran
Alireza Bosaghzadeh Department of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

DOI: https://doi.org/10.18502/fbt.v12i4.19818

Keywords: Breast Cancer; Semantic Segmentation; UNet ; Digital Mammography; Deep Learning

Abstract

Purpose: Manually segmenting mammograms is time-consuming and subjective. Therefore, automatic segmentation of breast masses is necessary but poses significant challenges due to factors such as low signal-to-noise ratio, diverse mass shapes and sizes, varying contrast levels, and high false positive rates. To address these challenges, we have developed an automatic image segmentation method based on a comprehensive pre-processing pipeline.

Materials and Methods: Our proposed method consists of two phases: 1) the pre-processing phase, which includes denoising, contrast enhancement, image cropping, resizing, and augmentation of mammograms, and 2) the model design phase, where UNet++ is employed as an encoder-decoder-based network for segmenting breast masses. The encoder captures relevant information from various regions in the input image, while the decoder reconstructs the spatial location of the target region. We conducted extensive experiments on publicly available CBIS-DDSM and INbreast datasets to evaluate the performance of our proposed method. For a comprehensive assessment, we utilized evaluation metrics including Precision, True Positive Rate, Dice Score Coefficient, and Jaccard Index. Additionally, a confusion matrix was employed to evaluate segmentation accuracy, while violin plots depicted the distribution of results across different BI-RADS and ACR categories.

Results: Based on our findings, our proposed method demonstrates promising results with a precision rate of 92.33%, a True Positive Rate of 93.83%, a Dice Score Coefficient measuring 92.92%, and a Jaccard Index of 87.05% in the CBIS-DDSM dataset. Furthermore, to assess the generalizability of our proposed method, the INbreast dataset was used as an unseen test set. The results demonstrate a precision rate of 91.15%, a True positive rate of 91.15%, a Dice Score coefficient of 92.53%, and a Jaccard Index of 87.25%, indicating robust performance on data outside the training distribution.

Conclusion: The integration of UNet++ with a pre-processing pipeline in digital mammography has shown promising results in accurately segmenting breast masses. This method has the potential to significantly improve early breast cancer detection and reduce diagnostic errors in clinical practice while employing a relatively lightweight model.