Deformable models are powerful approaches to medical image analysis, particularly segmentation. However, the outcome of applying a parametric or geometric deformable model is often significantly dependent on its initialization. This is an obstacle to robust automatic segmentation. Based on theoretical analyses of the watershed transform, we propose a novel approach to reducing this sensitivity to initialization by deriving a vector field from topographic and Euclidean distance transforms. This vector field is aimed to extend the influence of the gradients at the boundary of the segmentation target over the entire image in a consistent fashion, while ignoring any irrelevant gradients in the original image. Initiated by one or more segmentation seeds, the vector field is first computed using an efficient numerical method, and subsequently participates in the model's evolution process. Integration of the vector field has so far been performed with a two-dimensional (2D) parametric deformable model and with a three-dimensional (3D) geodesic active contour level set model. We believe that our approach will enable a higher degree of automation for deformable-model-based segmentation, particularly in situations where the seeds can be placed automatically based on, for example, a priori knowledge regarding the anatomy and the intensity differentiation between the target and the background. Experiments on segmenting organs and tumors from CT and MR images using the integrated models have shown that this is a promising approach.