The Metamorphs model is a robust segmentation method which integrates both shape and appearance in a unified space. The standard Metamorphs model does not encode temporal information. Thus it is not effective in segmenting time series data, such as a cardiac cycle from MRI. Furthermore, it needs manual interaction to initialize the model, which is time consuming for temporal data. In this paper, we proposed a model to seamlessly couple both spatial and temporal information together in the Metamorphs method. It is also able to automatically initialize the model instead of manual initialization. We model energy terms as probability maps, then different energy terms can be easily fused by multiplying them together. Temporal Spectral Residual (TSR) is employed to rapidly generate a probability map in temporal data. Compared to traditional Metamorphs, the computational overhead of our model is very light due to the efficiency of the TSR method and the ease of coupling different energy functions by using probability maps. We validate this algorithm in a task of segmenting the left ventricle endocardium from 2D MR sequences, and our method shows performance superior to the traditional Metamorphs.