Breast mass segmentation based on ultrasonic entropy maps and attention gated U-Net
This work addresses breast cancer diagnosis by improving segmentation accuracy in medical imaging, though it appears incremental as it adapts existing methods to new data types.
The researchers tackled breast mass segmentation in ultrasound imaging by proposing a deep learning approach using entropy parametric maps and an attention gated U-Net, achieving an average Dice score of 0.60 with entropy maps compared to 0.53 with regular US images.
We propose a novel deep learning based approach to breast mass segmentation in ultrasound (US) imaging. In comparison to commonly applied segmentation methods, which use US images, our approach is based on quantitative entropy parametric maps. To segment the breast masses we utilized an attention gated U-Net convolutional neural network. US images and entropy maps were generated based on raw US signals collected from 269 breast masses. The segmentation networks were developed separately using US image and entropy maps, and evaluated on a test set of 81 breast masses. The attention U-Net trained based on entropy maps achieved average Dice score of 0.60 (median 0.71), while for the model trained using US images we obtained average Dice score of 0.53 (median 0.59). Our work presents the feasibility of using quantitative US parametric maps for the breast mass segmentation. The obtained results suggest that US parametric maps, which provide the information about local tissue scattering properties, might be more suitable for the development of breast mass segmentation methods than regular US images.