Segmentation remains one of the main problem of image analysis. In particular- , 3D medical image segmentation is difficult due to noise, low contrast and outliers resulting from any 3D imaging technologies. However, image segmentation is a first step required for quantitative measurements, automatic diagnosis and modelling of anatomical structures in medical images. Due to the complex shape of anatomical structures and the large inter-patient shape variability, powerful modelling tools are required. In this report, we describe a segmentation tool based on deformable surfaces well suited to the shape reconstruction and modelling of anatomical structures from 3D medical images. The deformable surfaces are based on discrete meshes that can represent manifolds without any topology restrictions. These meshes rely on shape constraints at local and global scale for introducing prior knowledge on the structures to segment and for regularizing the surface deformations. Different data terms are proposed to cover reconstructio- n from different imaging modalities. Time sequence of 3D images, or 4D images, are commonly used for heart motion analysis and cardiac pathologies diagnosis. In this paper, we extend the deformable surface reconstruction framework to the 4D case. In particular, we introduce temporal constraints to regularize deformations in the time dimension. Prior knowledge on the heart motion is introduced through trajectory constraints that are complementa- ry to spatial constraints. Segmentation of the heart left ventricle in different imaging modalities is demonstrated.