Computing pancreatic morphology in 3D radiological scans could provide significant insight about a medical condition. However, segmenting the pancreas in magnetic resonance imaging (MRI) remains challenging due to high inter-patient variability. Also, the resolution and speed of MRI scanning present artefacts that blur the pancreas bound- aries between overlapping anatomical structures. This paper proposes a dual-stage automatic segmentation method: 1) a deep neural network is trained to address the problem of vague organ boundaries in high class-imbalanced data. This network integrates a novel loss function to rigorously optimise boundary delineation using the modified Hausdorff metric and a sinusoidal component; 2) Given a test MRI volume, the output of the trained network predicts a sequence of targeted 2D pan- creas classes that are reconstructed as a volumetric binary mask. An energy-minimisation approach fuses a learned digital contrast model to suppress the intensities of non-pancreas classes, which, combined with the binary volume performs a refined segmentation in 3D while reveal- ing dense boundary detail. Experiments are performed on two diverse MRI datasets containing 180 and 120 scans, in which the proposed ap- proach achieves a mean Dice score of 84.1 ± 4.6% and 85.7 ± 2.3%, respectively. This approach is statistically stable and outperforms state- of-the-art methods on MRI.