Focused shape models for hip joint segmentation in 3D magnetic resonance images

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• We introduce a weighted shape learning approach applied to the human hip joint.
• Weights may be set to focus the shape representation energy to important areas.
• The highly weighted areas become the dominant representations within the model.
• Lower reconstruction errors and higher accuracy can be obtained in those areas.
• Validation was done on 35 3T unilateral small field of view MR scans.

Deformable models incorporating shape priors have proved to be a successful approach in segmenting anatomical regions and specific structures in medical images. This paper introduces weighted shape priors for deformable models in the context of 3D magnetic resonance (MR) image segmentation of the bony elements of the human hip joint. The fully automated approach allows the focusing of the shape model energy to a priori   selected anatomical structures or regions of clinical interest by preferentially ordering the shape representation (or eigen-modes) within this type of model to the highly weighted areas. This focused shape model improves accuracy of the shape constraints in those regions compared to standard approaches. The proposed method achieved femoral head and acetabular bone segmentation mean absolute surface distance errors of 0.55±0.18mm and 0.75±0.20mm respectively in 35 3D unilateral MR datasets from 25 subjects acquired at 3T with different limited field of views for individual bony components of the hip joint.

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