Accurate breast lesion segmentation by exploiting spatio-temporal information with deep recurrent and convolutional network

Mingjian Chen, Hao Zheng, Changsheng Lu, Enmei Tu, Jie Yang, Nikola K. Kasabov

Abstract: Breast lesion segmentation in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is a challenging task since DCE-MRI includes not only spatial information within slices but also temporal correlation between different sequences. Previous works only focused on one-side of the spatio-temporal information, therefore could not obtain accurate segmentation results. In this paper, we propose an automatic breast lesion segmentation framework with much less parameters utilizing both spatial and temporal features. Specifically, a breast region extraction is applied to eliminate unconcerned tissues. After that, the segmentation result can be obtained by a spatio-temporal segmentation network, where we exploit a multi-pathway structure and a fusion operation to unearth more spatio-temporal features. The proposed framework is validated on our real dataset and obtains a more accurate segmentation result compared to other approaches.