Abstract: A patient-specific assessment of fracture probability for the femur proximal end is provided. 3D locations of the femur head center, a point on the femoral shaft center, and the femoral intercondylar notch are determined from a clinical image. A frontal plane, a perpendicular thereunto and a bone shaft axis are determined from the 3D locations. An FEA coordinate system is defined from the frontal plane, the perpendicular and the axis. Two FEA analyses are performed, one for neck fracture and one for pertrochanteric fracture, with the same displacement constraints and the same load magnitude but different load angles. The femur proximal end is divided into four anatomically-based regions. For each region and each load, maximum tensile and compressive principal strains are determined and, based on the body weight and the principal strains, a likelihood of fracture is obtained. The minimum of these 8 likelihoods gives the probability of fracture.

Abstract: A method for automated segmentation of a bone in an image dataset of a region of a human body, wherein said region comprises at least two adjacent bones, and wherein one of the bones comprises a shaft region and a head region, the method comprising identifying in the image dataset voxels belonging to a contiguous volume comprising both bones based on a density threshold; extracting a subset comprising the head region of the first bone, based on a boundary image slice; and applying a trained machine learning classifier to said subset, to generate a classification of each of voxel as belonging to one of the bones or a none-bone tissue.

Abstract: A method for automated segmentation of a bone in an image dataset of a region of a human body, wherein said region comprises at least two adjacent bones, and wherein one of the bones comprises a shaft region and a head region, the method comprising identifying in the image dataset voxels belonging to a contiguous volume comprising both bones based on a density threshold; extracting a subset comprising the head region of the first bone, based on a boundary image slice; and applying a trained machine learning classifier to said subset, to generate a classification of each of voxel as belonging to one of the bones or a none-bone tissue.

Abstract: A computer-implemented method for providing FEA analysis of at least a portion of at least one bone in a patient, the method comprising steps of: providing at least one image of at least a portion of a bone; selecting at least a portion of the bone; automatically performing an FE analysis of the selected portion of the bone; and displaying at least one result of the FE analysis. Bone selection and display of the bone, the selected portion thereof, and the results of the FE analysis occur via a hand-held device, with processing and data storage performed remotely.

Abstract: A computer-implemented method for providing FEA analysis of at least a portion of at least one bone in a patient, the method comprising steps of: providing at least one image of at least a portion of a bone; selecting at least a portion of the bone; automatically performing an FE analysis of the selected portion of the bone; and displaying at least one result of the FE analysis. Bone selection and display of the bone, the selected portion thereof, and the results of the FE analysis occur via a hand-held device, with processing and data storage performed remotely.