In Medical image analysis
Complete left bundle branch block (cLBBB) is an electrical conduction disorder associated with cardiac disease. Septal flash (SF) involves septal leftward contraction during early systole followed by a lengthening motion toward the right ventricle and affects several patients with cLBBB. It has been revealed that cLBBB patients with SF may be at risk of cardiac function reduction and poor prognosis. Therefore, accurate identification of SF may play a vital role in counseling patients about their prognosis. Generally, Septal flash is identified by echocardiography using visual "eyeballing". However, this conventional method is subjective as it depends on operator experience. In this study, we build a linear attention cascaded net (LACNet) capable of processing echocardiography to identify SF automatically. The proposed method consists of a cascaded CNN-based encoder and an LSTM-based decoder, which extract spatial and temporal features simultaneously. A spatial transformer network (STN) module is employed to avoid image inconsistency and linear attention layers are implemented to reduce data complexity. Moreover, the left ventricle (LV) area-time curve calculated from segmentation results can be considered as a new independent disease predictor as SF phenomenon leads to transient left ventricle area enlargement. Therefore, we added the left ventricle area-time curve to LACNet to enrich input data diversity. The result shows the possibility of using echocardiography to diagnose cLBBB with SF automatically.
Qu Mingjun, Wang Yonghuai, Li Honghe, Yang Jinzhu, Ma Chunyan
Attention, Deep learning, Septal flash, cLBBB