CEUS Net: Lesion Segmentation in Dynamic Contrast Enhanced Ultrasound with Feature Reweighted Attention Mechanism
Contrast enhanced ultrasound (CEUS) has been a popular clinical imaging technique for the dynamic visualization of the tumor microvasculature. Due to the heterogeneous intra tumor vessel distribution and ambiguous lesion boundary, automatic tumor segmentation in the CEUS sequence is challenging. To overcome these difficulties, we propose a novel network, CEUS Net, which is a novel U net network infused with our designed feature reweighted dense blocks. Specifically, CEUS Net incorporates the dynamic channel wise feature re weighting into the Dense block for adapting the importance of learned lesion relevant features. Besides, in order to efficiently utilize dynamic characteristics of CEUS modality, our model attempts to learn spatial temporal features encoded in diverse enhancement patterns using a multichannel convolutional module. The CEUS Net has been tested on tumor segmentation tasks of CEUS images from breast and thyroid lesions. It results in the dice index of 0.84, and 0.78 for CEUS segmentation of breast and thyroid respectively.
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)