Abstract
To develop a U-net deep learning method for breast tissue segmentation on fat-sat T1-weighted (T1W) MRI using transfer learning (TL) from a model developed for non-fat-sat images. The training dataset (N = 126) was imaged on a 1.5 T MR scanner, and the independent testing dataset (N = 40) was imaged on a 3 T scanner, both using fat-sat T1W pulse sequence. Pre-contrast images acquired in the dynamic-contrast-enhanced (DCE) MRI sequence were used for analysis. All patients had unilateral cancer, and the segmentation was performed using the contralateral normal breast. The ground truth of breast and fibroglandular tissue (FGT) segmentation was generated using a template-based segmentation method with a clustering algorithm. The deep learning segmentation was performed using U-net models trained with and without TL, by using initial values of trainable parameters taken from the previous model for non-fat-sat images. The ground truth of each case was used to evaluate the segmentation performance of the U-net models by calculating the dice similarity coefficient (DSC) and the overall accuracy based on all pixels. Pearson’s correlation was used to evaluate the correlation of breast volume and FGT volume between the U-net prediction output and the ground truth. In the training dataset, the evaluation was performed using tenfold cross-validation, and the mean DSC with and without TL was 0.97 vs. 0.95 for breast and 0.86 vs. 0.80 for FGT. When the final model developed with and without TL from the training dataset was applied to the testing dataset, the mean DSC was 0.89 vs. 0.83 for breast and 0.81 vs. 0.81 for FGT, respectively. Application of TL not only improved the DSC, but also decreased the required training case number. Lastly, there was a high correlation (R2 > 0.90) for both the training and testing datasets between the U-net prediction output and ground truth for breast volume and FGT volume. U-net can be applied to perform breast tissue segmentation on fat-sat images, and TL is an efficient strategy to develop a specific model for each different dataset.
Highlights
Breast MRI is a well-established clinical imaging modality for management of breast cancer
The fibroglandular tissue (FGT) segmentation results were very similar between the ground truth and U-net
This study applied U-net to segment breast and FGT on fat-sat T1W MRI, which is a more popular imaging sequence used for diagnosis of breast cancer than the nonfat-sat sequence
Summary
Breast MRI is a well-established clinical imaging modality for management of breast cancer. In addition to its use in diagnosis and pre-operative staging, breast MRI is recommended for annual screening in women with a high risk. Many states in the USA have passed the breast density notification law which has raised awareness and led to the increased clinical use of breast MRI [2, 3]. As a result, this has led to the fast accumulation of a large breast MRI database, which can be used for exploring the clinical use of quantitative breast density. There are two potential clinical applications, one for improving the accuracy of risk-prediction models [4, 5], and the other for evaluating the response to different treatments, such as hormonal therapy [6] and neoadjuvant chemotherapy [7]
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