We propose a novel spatiotemporal constraint based on shape and appearance and combine it with a level set deformable model for Left Ventricle (LV) segmentation in 4D gated cardiac SPECT, particularly in the presence of perfusion defects. The model incorporates appearance and shape information into a soft-to-hard probabilistic constraint, and utilizes spatiotemporal regularization via a Maximum A Posteriori (MAP) framework. This constraint force allows more flexibility than the rigid forces of shape constraint-only schemes, as well as other state of the art joint shape and appearance constraints. The combined model can hypothesize defective LV borders based on prior knowledge. We present comparative results to illustrate the improvement gain. A brief defect detection example is finally presented as an application of the proposed method.