Robust statistical shape models for MRI bone segmentation in presence of small field of view

Schmid, J. and Kim, J. and Magnenat-Thalmann, N.

Abstract: Accurate bone modeling from medical images is essential in the diagnosis and treatment of patients because it supports the detection of abnormal bone morphology, which is often responsible for many musculoskeletal diseases (MSDs) of human articulations. In a clinical setting, images of the suspected joints are acquired in a high resolution but with a small field of view (FOV) in order to maximize the image quality while reducing acquisition time. However bones are only partially visible in such small FOVs. This presents difficult challenges in automated bone segmentation and thus limits the application of sophisticated algorithms such as statistical shape models (SSM), which have been generally proven to be an efficient technique for bone segmentation. Indeed, the reduced image information affects the initialization and evolution of these deformable model-based approaches. In this paper, we present a robust multi-resolution SSM algorithm with an adapted initialization to address the segmentation of MRI bone images acquired in small FOVs for modeling and computer-aided diagnosis. Our innovation stems from the derivation of a robust SSM based on complete and corrupted shapes, as well as from a simultaneous optimization of transformation and shape parameters to yield an efficient initialization technique. We demonstrate our segmentation algorithm using 86 clinical MRI images of the femur and hip bones. These images have a varied resolution and limited FOVs. The results of our segmentation (e.g., average distance error of 1.12+/-0.46 mm) are within the needs of image-based clinical diagnosis.

  journal = {Medical Image Analysis},
  author = {Schmid, J. and Kim, J. and Magnenat-Thalmann, N.},
  title = {Robust statistical shape models for MRI bone segmentation in presence of small field of view},
  publisher = {Elsevier},
  volume = {vol. 15},
  pages = {155-168},
  year = {2011},
  topic = {Medical}