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Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks.

08:00 EDT 10th March 2020 | BioPortfolio

Summary of "Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks."

Fibroglandular tissue (FGT) segmentation is a crucial step for quantitative analysis of background parenchymal enhancement (BPE) in MRI, which is useful for breast cancer risk assessment. In this study, we develop an automated deep learning method based on a generative adversarial network (GAN) to identify the FGT region in MRI volumes and evaluate its impact on a specific clinical application. The GAN consists of an improved U-Net as a generator to generate FGT candidate areas and a patch deep convolutional neural network (DCNN) as discriminator to evaluate the authenticity of the synthetic FGT region. The proposed method has two improvements compared to the classical U-Net: 1) the improved U-Net is designed to extract more features of the FGT region for a more accurate description of the FGT region; 2) a patch DCNN is designed for discriminating the authenticity of the FGT region generated by the improved U-Net, which makes the segmentation result more stable and accurate. A dataset of 100 three-dimensional bilateral breast MRI scans from 100 patients (aged 22-78 years) was used in this study with the Institutional Review Board (IRB) approval. Three-dimensional hand segmented FGT areas for all breasts were provided as a reference standard. Five-fold cross-validation was used in training and testing of the models. The Dice similarity coefficient (DSC) and Jaccard index (JI) values were evaluated to measure the segmentation accuracy. Previous method using classical U-Net was used as a baseline in this study. In the five partitions of the cross-validation set, the GAN achieved DSC and JI values of 87.0±7.0% and 77.6±10.1% respectively, while the corresponding values obtained through by the baseline method were 81.1±8.7% and 69.0±11.3% respectively. The proposed method is significantly superior to the previous method using U-Net. The FGT segmentation impacted the BPE quantification application in the following manner: the correlation coefficients between quantified BPE value and BI-RADS BPE categories provided by the radiologist were 0.46±0.15 (best: 0.63) based on GAN segmented FGT areas, while the corresponding correlation coefficients were 0.41±0.16 (best: 0.60) based on baseline U-Net segmented FGT areas. BPE can be quantified better using the FGT areas segmented by the proposed GAN model than using the FGT areas segmented by the baseline U-Net.

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This article was published in the following journal.

Name: Physics in medicine and biology
ISSN: 1361-6560
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Medical and Biotech [MESH] Definitions

A common and benign breast disease characterized by varying degree of fibrocystic changes in the breast tissue. There are three major patterns of morphological changes, including FIBROSIS, formation of CYSTS, and proliferation of glandular tissue (adenosis). The fibrocystic breast has a dense irregular, lumpy, bumpy consistency.

Measurement of relative composition of different BREAST tissue types often determined from MAMMOGRAPHY; ULTRASONOGRAPHY; or MRI.

Excision of breast tissue with preservation of overlying skin, nipple, and areola so that breast form may be reconstructed.

A fluid-filled closed cavity or sac that is lined by an EPITHELIUM and found in the BREAST. It may appear as a single large cyst in one breast, multifocal, or bilateral in FIBROCYSTIC BREAST DISEASE.

Neoplasms, usually carcinoma, located within the center of an organ or within small lobes, and in the case of the breast, intraductally. The emphasis of the name is on the location of the neoplastic tissue rather than on its histological type. Most cancers of this type are located in the breast.

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