Breast cancer is one of the common cancers that endanger the health of women
globally. Accurate target lesion segmentation is essential for early clinical
intervention and postoperative follow-up. Recently, many convolutional neural
networks (CNNs) have been proposed to segment breast tumors from ultrasound
images. However, the complex ultrasound pattern and the variable tumor shape
and size bring challenges to the accurate segmentation of the breast lesion.
Motivated by the selective kernel convolution, we introduce an enhanced
selective kernel convolution for breast tumor segmentation, which integrates
multiple feature map region representations and adaptively recalibrates the
weights of these feature map regions from the channel and spatial dimensions.
This region recalibration strategy enables the network to focus more on
high-contributing region features and mitigate the perturbation of less useful
regions. Finally, the enhanced selective kernel convolution is integrated into
U-net with deep supervision constraints to adaptively capture the robust
representation of breast tumors. Extensive experiments with twelve
state-of-the-art deep learning segmentation methods on three public breast
ultrasound datasets demonstrate that our method has a more competitive
segmentation performance in breast ultrasound images.Comment: 12 pages, 8 figure