DMAeEDNet: Dense Multiplicative Attention Enhanced Encoder Decoder Network for Ultrasound-Based Automated Breast Lesion Segmentation

Abstract

Automated and precise segmentation of breast lesions can facilitate early diagnosis of breast cancer. Recent research studies employ deep learning for automatic segmentation of breast lesions using ultrasound imaging. Numerous studies introduce somewhat complex modifications to the well adapted segmentation network, U-Net for improved segmentation, however, at the expense of increased computational time. Towards this aspect, this study presents a low complex deep learning network, i.e., dense multiplicative attention enhanced encoder decoder network, for effective breast lesion segmentation in the ultrasound images. For the first time in this context, two dense multiplicative attention components are utilized in the encoding layer and the output layer of an encoder-decoder network with depthwise separable convolutions, to selectively enhance the relevant features. A rigorous performance evaluation using two public datasets demonstrates that the proposed network achieves dice coefficients of 0.83 and 0.86 respectively with an average segmentation latency of $19 ms$ . Further, a noise robustness study using an in-clinic recorded dataset without pre-processing indicates that the proposed network achieves dice coefficient of 0.72. Exhaustive comparison with some commonly used networks indicate its adeptness with low time and computational complexity demonstrating feasibility in real time.

Keywords

• Ultrasound
• breast lesion segmentation
• deep learning
• U-Net
• convolution neural network