Comparison of 2D and 3D U-Net breast lesion segmentations on DCE-MRI

Abstract

Computer-aided diagnosis based on features extracted from medical images relies heavily on accurate lesion segmentation before feature extraction. Using 994 unique breast lesions imaged with dynamic contrast-enhanced (DCE) MRI, several segmentation algorithms were investigated. The first method is fuzzy c-means (FCM), a well-established unsupervised clustering algorithm used on breast MRIs. The second and third methods are based on the convolutional neural network U-Net, a widely-used deep learning method for image segmentation—for two- or three-dimensional MRI data, respectively. The purpose of this study was twofold—1) to assess the performances of 2D (slice-by-slice) and 3D U-Nets in breast lesion segmentation on DCE-MRI trained with FCM segmentations, and 2) compare their performance to that of FCM. Center slice segmentations produced by FCM, 2D U-Net, and 3D U-Net were evaluated using radiologist segmentations as truth, and volumetric segmentations produced by 2D U-Net (slice-by-slice) and 3D U-Net were compared using FCM as a surrogate truth. Five-fold cross-validation was conducted on the U-Nets and Dice similarity coefficient (DSC) and Hausdorff distance (HD) were used as performance metrics. Although 3D U-Net performed well, 2D U-Net outperformed 3D U-Net, both for center slice (DSC p=4.13×10^-9, HD p=1.40×10^-2) and volume segmentations (DSC p=2.72×10^-83, HD p=2.28×10^-10). Additionally, 2D U-Net outperformed FCM in center slice segmentation in terms of DSC (p=1.09×10^-7). The results suggest that 2D U-Net is promising in segmenting breast lesions and could be an effective alternative to FCM.