Despite its high sensitivity, MR imaging has low specificity and high false positive issues. Therefore, automatic breast lesion detection algorithms are necessary. To this end, we propose a new network, dense attention network (DANet), for breast lesion segmentation in MR images. In DANet, we designed a feature fusion and selection mechanism. Features from the corresponding encoder layer and from all previous decoder layers are fused by concatenation. To highlight the rich-informative channels, a channel attention module is introduced. DANet showed better segmentation results compared to commonly applied segmentation networks on our 2D contrast-enhanced T1-weighted breast MR dataset.