Segmentation of 4D Echocardiography Using Stochastic Online Dictionary Learning

Dictionary learning has been shown to be effective in exploiting spatiotemporal coherence for echocardiographic segmentation. To overcome the limitations of previous methods, we present a stochastic online dictionary learning approach for segmenting left ventricular borders from 4D echocardiography. It is based on stochastic approximations and processes a mini-batch of samples at a time, which results in lower memory consumption and lower computational cost than classical batch algorithms. In contrast to the previous methods, where dictionaries and their weights are optimized only on the most recently segmented frame, our stochastic online learning procedure optimizes the dictionaries and the corresponding weights by aggregating all the past information while adapting them to the dynamically changing data. The rate of updating the past information is controlled and varied according to the appearance scale to seek a balance between old and new information. Results on 26 4D echocardiographic images show the proposed method is more accurate, more robust, and faster than the previous batch algorithm.