Combining Region Proposals and Deep SSAE Learnt Features for Detecting Left Ventricle in Cardiac MR Images

Automatic detection of left ventricle(LV)is an important step for further analyzing cardiac MR images.However,due to the image acquisition,partial volume effect,low resolution and high similarity to the surroundings,it is a challenging task for improving LV detection accuracy.In this paper an automatic detection method is proposed by combining region proposals and deep Stacked Sparse Auto-encoder(SSAE)learnt features.It consists of two components:1)At the stage of proposing candidate regions,a superpixel algorithm is firstly adopted to generate initial regions,then a hierarchical clustering algorithm using deep SSAE learnt feature is employed to make the final candidates;2)At the stage of detection,a SSAE network is used to extract deep feature of the resulting candidates,and the learnt feature is used to train a linear C-SVM classifier.Furthermore,a hard negative mining strategy is added for tuning the model adaptive to the sample imbalance problem.Experimental results of left ventricle detection on the Cardiac Atlas Project(CAP)data set show that,compared to the representative hand-crafted or region proposal based methods,the proposed method achieves competitive results.