Hierarchical Multi-Atlas Segmentation Using Label-Specific Embeddings, Target-Specific Templates and Patch Refinement. In: Wu G, Coupé P, Zhan Y, Munsell BC, Rueckert D, editors. Patch-Based Techniques in Medical Imaging. Cham: Springer International Publishing; 2016. pp. 84-91. [DOI: 10.1007/978-3-319-47118-1_11]

Automated muscle segmentation from CT images of the hip and thigh using a hierarchical multi-atlas method

PURPOSE

Patient-specific quantitative assessments of muscle mass and biomechanical musculoskeletal simulations require segmentation of the muscles from medical images. The objective of this work is to automate muscle segmentation from CT data of the hip and thigh.

METHOD

We propose a hierarchical multi-atlas method in which each hierarchy includes spatial normalization using simpler pre-segmented structures in order to reduce the inter-patient variability of more complex target structures.

RESULTS

The proposed hierarchical method was evaluated with 19 muscles from 20 CT images of the hip and thigh using the manual segmentation by expert orthopedic surgeons as ground truth. The average symmetric surface distance was significantly reduced in the proposed method (1.53 mm) in comparison with the conventional method (2.65 mm).

CONCLUSION

We demonstrated that the proposed hierarchical multi-atlas method improved the accuracy of muscle segmentation from CT images, in which large inter-patient variability and insufficient contrast were involved.