Deep learning based pectoral muscle segmentation on Mammographic Image Analysis Society (MIAS) mammograms
- Authors
- Kim, Young Jae; Yoo, Eun Young; Kim, Kwang Gi
- Issue Date
- Jun-2021
- Publisher
- SUNGKYUNKWAN UNIV SCH MEDICINE
- Keywords
- Deep learning; Mammography; Pectoralis muscles
- Citation
- PRECISION AND FUTURE MEDICINE, v.5, no.2, pp.77 - 82
- Journal Title
- PRECISION AND FUTURE MEDICINE
- Volume
- 5
- Number
- 2
- Start Page
- 77
- End Page
- 82
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/81782
- DOI
- 10.23838/pfm.2020.00170
- ISSN
- 2508-7940
- 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.
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Collections - 의과대학 > 의학과 > 1. Journal Articles
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