Automated whole breast segmentation for hand-held ultrasound with position information: Application to breast density estimation. (December 2020)

• Record Type: Journal Article
• Authors: Chang, Jie-Fan; Huang, Chiun-Sheng; Chang, Ruey-Feng
• Is Part Of: Computer methods and programs in biomedicine. Volume 197(2020)
• Publication Date: 2020-12
• Subjects: Ultrasound -- Breast density -- Deep learning -- U-Net -- CNN -- Convolutional networks -- Neural network -- Fuzzy c-mean clustering -- Ultrasound probe position -- Magnetic tracker -- Image acquisition; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105727
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Hand-held ultrasound density estimation hasn't been developed before this research. The proposed WBUS system can avoid sampling bias or overlapped sample image slices. The proposed method can facilitate reliable density evaluation. The breast region can be segmented by the proposed CNN-based method. High breast density correlations were found by the proposed method, and ground truth. Abstract: Background and objective: Women with higher breast densities have a relatively higher risk to be diagnosed with breast cancer. Hand-held ultrasound (HHUS) can provide precise screening results and detect masses in dense breasts. However, its lack of position information and automatic extraction of breast area hinder the implementation of density estimation. To facilitate reliable breast density evaluation, this study proposed an upgraded version of our whole-breast ultrasound (WBUS) system, which not only can provide precise position information, but also can extract precise breast area automatically based on deep learning method. Methods: WBUS images with probe position information were collected from 117 women. For each case, an automatic breast region segmentation by DeepResUnet was conducted, then fibroglandular tissues were extracted from breast region using fuzzy c-mean (FCM) classifier. Finally, the percentage of breast density and breast area of the DeepResUnet predicted region and the breast region of the ground truth were calculated and compared. Results: The average and standard deviation of each breast case for DeepResUnet predicted breast region of 10-fold in Accuracy ( ACC ) was 0.963±0.054. Sensitivity ( SENS ) was 0.928±0.11. Specificity ( SPEC ) was 0.967±0.054. Dice coefficient ( Dice ) was 0.916±0.98. Region intersection over union ( IoU ) was 0.856±0.134. Significant and very high correlations of breast density, fibroglandular tissue area and breast area ( R = 0.843, R = 0.822 and R = 0.984, all p values < 0.001) were found between the ground truth and the result of the proposed method for ultrasound images. Conclusions: Breast density, fibroglandular tissue, and breast volume evaluated based on the proposed method and WBUS system have significant correlations with ground truth, indicating that the proposed method and WBUS system has the potential to be an alternative modality for breast screening and density estimation in clinical use.