Institute of Fundamental Technological Research

In this work, we propose a deep learning method for breast mass segmentation in ultrasound (US). Variations in breast mass size and image characteristics make the automatic segmentation difficult. To addressthis issue, we developed a selective kernel (SK) U-Net convolutional neural network. The aim of the SKswas to adjust network's receptive fields via an attention mechanism, and fuse feature maps extractedwith dilated and conventional convolutions. The proposed method was developed and evaluated usingUS images collected from 882 breast masses. Moreover, we used three datasets of US images collectedat different medical centers for testing (893 US images). On our test set of 150 US images, the SK-U-Netachieved mean Dice score of 0.826, and outperformed regular U-Net, Dice score of 0.778. When evaluatedon three separate datasets, the proposed method yielded mean Dice scores ranging from 0.646 to 0.780. Additional fine-tuning of our better-performing model with data collected at different centers improvedmean Dice scores by ~6%. SK-U-Net utilized both dilated and regular convolutions to process US images. We found strong correlation, Spearman's rank coefficient of 0.7, between the utilization of dilated convo-lutions and breast mass size in the case of network's expansion path. Our study shows the usefulness ofdeep learning methods for breast mass segmentation. SK-U-Net implementation and pre-trained weightscan be found at github.com/mbyr/bus_seg.

attention mechanism, breast mass segmentation, convolutional neural networks, deep learning, receptive field, ultrasound imaging