ESKNet: An enhanced adaptive selection kernel convolution for ultrasound breast tumors segmentation

Abstract

Breast cancer has become one of the most dreaded diseases that can threaten the life of any woman. Accurate target lesion segmentation is essential for early clinical intervention and postoperative follow-up. Recently, many convolutional neural networks (CNNs) for segmenting breast tumors from ultrasound images have been presented. 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. Using three public breast ultrasound datasets, we conducted extensive experiments with many state-of-the-art deep learning segmentation methods. In the segmentation of the first ultrasound dataset (BUSI), the values of Jaccard, Precision, Recall, Specificity and Dice are 70.20%, 79.57%, 82.41%, 97.47% and 78.71%, respectively. The values of Jaccard, Precision, Recall, Specificity and Dice for our method on the second ultrasound dataset (Dataset B) are 71.65%, 81.01%, 82.66%, 99.01% and 79.92%, respectively. For the segmentation of external ultrasound dataset (STU), the mean values of Jaccard, Precision, Recall, Specificity and Dice are 75.14%, 84.73%, 89.25%, 97.53% and 84.76%, respectively. The experimental results fully demonstrate the superior performance of our method for segmenting breast ultrasound images. The source code is available on the following website: https://github.com/CGPxy/ESKNet.