Automatic Quantification of Breast Density from Mammography Using Deep Learning
-
- Inoue Kenichi
- Shonan Memorial Hospital, Breast Cancer Center
-
- Kawasaki Aika
- Shonan Memorial Hospital, Breast Cancer Center
-
- Koshimizu Kanako
- Shonan Memorial Hospital, Breast Cancer Center
-
- Ariizumi Chigusa
- Shonan Memorial Hospital, Breast Cancer Center
-
- Unno Keiko
- Shonan Memorial Hospital, Breast Cancer Center
-
- Nagashima Miki
- Shonan Memorial Hospital, Breast Cancer Center
-
- Mizuno Kayo
- Shonan Memorial Hospital, Breast Cancer Center
-
- Misumi Misono
- Shonan Memorial Hospital, Breast Cancer Center
-
- Tsutsumi Chizuko
- Shonan Memorial Hospital, Breast Cancer Center
-
- Sasaki Takeshi
- Department of Next-Generation Pathology Information Networking, Faculty of Medicine, The University of Tokyo
-
- Doi Takako
- Shonan Memorial Hospital, Breast Cancer Center
Bibliographic Information
- Other Title
-
- ディープラーニングを用いた dense breast の自動定量化に関する検討
- ディープラーニング オ モチイタ dense breast ノ ジドウ テイリョウカ ニ カンスル ケントウ
Search this article
Abstract
<p>Background : In the field of breast screening using mammography, announcing to the examinees whether they are dense or not has not been deprecated in Japan. One of the reasons is a shortage of objectivity estimating their dense breast. Our aim is to build a system with deep learning algorithm to calculate and quantify objective breast density automatically. Material and Method : Mammography images taken in our institute that were diagnosed as category 1 were collected. Each processed image was transformed into eight-bit grayscale, with the size of 2294 pixels by 1914 pixels. The “base pixel value” was calculated from the fatty area within the breast for each image. The “relative density” was calculated by dividing each pixel value by the base pixel value. Semantic segmentation algorithm was used to automatically segment the area of breast tissue within the mammography image, which was resized to 144 pixels by 120 pixels. By aggregating the relative density within the breast tissue area, the “breast density” was obtained automatically. Result : From each but one mammography image, the breast density was successfully calculated automatically. By defining a dense breast as the breast density being greater than or equal to 30%, the evaluation of the dense breast was consistent with that by a computer and human (76.6%). Conclusion: Deep learning provides an excellent estimation of quantification of breast density. This system could contribute to improve the efficiency of mammography screening system.</p>
Journal
-
- Japanese Journal of Radiological Technology
-
Japanese Journal of Radiological Technology 77 (10), 1165-1172, 2021
Japanese Society of Radiological Technology
- Tweet
Details 詳細情報について
-
- CRID
- 1390008299690235776
-
- NII Article ID
- 130008105929
-
- NII Book ID
- AN00197784
-
- ISSN
- 18814883
- 03694305
-
- NDL BIB ID
- 031795696
-
- Text Lang
- ja
-
- Data Source
-
- JaLC
- NDL
- Crossref
- CiNii Articles
-
- Abstract License Flag
- Disallowed