Using deep learning to segment breast and fibroglandular tissue in MRI volumes

Purpose: Automated segmentation of breast and fibroglandular tissue (FGT) is required for various computer‐aided applications of breast MRI. Traditional image analysis and computer vision techniques, such atlas, template matching, or, edge and surface detection, have been applied to solve this task. However, applicability of these methods is usually limited by the characteristics of the images used in the study datasets, while breast MRI varies with respect to the different MRI protocols used, in addition to the variability in breast shapes. All this variability, in addition to various MRI artifacts, makes it a challenging task to develop a robust breast and FGT segmentation method using traditional approaches. Therefore, in this study, we investigated the use of a deep‐learning approach known as “U‐net.” Materials and methods: We used a dataset of 66 breast MRI's randomly selected from our scientific archive, which includes five different MRI acquisition protocols and breasts from four breast density categories in a balanced distribution. To prepare reference segmentations, we manually segmented breast and FGT for all images using an in‐house developed workstation. We experimented with the application of U‐net in two different ways for breast and FGT segmentation. In the first method, following the same pipeline used in traditional approaches, we trained two consecutive (2C) U‐nets: first for segmenting the breast in the whole MRI volume and the second for segmenting FGT inside the segmented breast. In the second method, we used a single 3‐class (3C) U‐net, which performs both tasks simultaneously by segmenting the volume into three regions: nonbreast, fat inside the breast, and FGT inside the breast. For comparison, we applied two existing and published methods to our dataset: an atlas‐based method and a sheetness‐based method. We used Dice Similarity Coefficient (DSC) to measure the performances of the automated methods, with respect to the manual segmentations. Additionally, we computed Pearson's correlation between the breast density values computed based on manual and automated segmentations. Results: The average DSC values for breast segmentation were 0.933, 0.944, 0.863, and 0.848 obtained from 3C U‐net, 2C U‐nets, atlas‐based method, and sheetness‐based method, respectively. The average DSC values for FGT segmentation obtained from 3C U‐net, 2C U‐nets, and atlas‐based methods were 0.850, 0.811, and 0.671, respectively. The correlation between breast density values based on 3C U‐net and manual segmentations was 0.974. This value was significantly higher than 0.957 as obtained from 2C U‐nets (P < 0.0001, Steiger's Z‐test with Bonferoni correction) and 0.938 as obtained from atlas‐based method (P = 0.0016). Conclusions: In conclusion, we applied a deep‐learning method, U‐net, for segmenting breast and FGT in MRI in a dataset that includes a variety of MRI protocols and breast densities. Our results showed that U‐net‐based methods significantly outperformed the existing algorithms and resulted in significantly more accurate breast density computation.