Precis Future Med.
2021 Jun;5(2):77-82. 10.23838/pfm.2020.00170.
Deep learning based pectoral muscle segmentation on Mammographic Image Analysis Society (MIAS) mammograms
- Affiliations
-
- 1Department of Biomedical Engineering, Gachon University Gil Medical Center, Gachon University College of Medicine, Inchon, Korea
- 2Department of Radiology, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Korea
- KMID: 2517763
- DOI: http://doi.org/10.23838/pfm.2020.00170
Abstract
- Purpose
The purpose of this study was to propose a deep learning-based method for automated detection of the pectoral muscle, in order to reduce misdetection in a computer- aided diagnosis (CAD) system for diagnosing breast cancer in mammography. This study also aimed to assess the performance of the deep learning method for pectoral muscle detection by comparing it to an image processing-based method using the random sample consensus (RANSAC) algorithm.
Methods
Using the 322 images in the Mammographic Image Analysis Society (MIAS) database, the pectoral muscle detection model was trained with the U-Net architecture. Of the total data, 80% was allocated as training data and 20% was allocated as test data, and the performance of the deep learning model was tested by 5-fold cross validation.
Results
The image processing-based method for pectoral muscle detection using RANSAC showed 92% detection accuracy. Using the 5-fold cross validation, the deep learning-based method showed a mean sensitivity of 95.55%, mean specificity of 99.88%, mean accuracy of 99.67%, and mean dice similarity coefficient of 95.88%.
Conclusion
The proposed deep learning-based method of pectoral muscle detection performed better than an existing image processing-based method. In the future, by collecting data from various medical institutions and devices to further train the model and improve its reliability, we expect that this model could greatly reduce misdetection rates by CAD systems for breast cancer diagnosis.
