Segmenting Bones Using Statistical Shape Modeling and Local Template Matching

Accurate bone segmentation is necessary to develop chair- side manufacturing of implants based on additive manufacturing. Various automatic segmentation techniques have been proposed to streamline the process (e.g. graph-cut or deep-learning), but these techniques do not provide anatomical correspondences during the segmentation process, which makes exploitation of segmentation more difficult to predict miss- ing bone parts in case of fracture or its premorbid shape for degenerative diseases. Bone segmentation using active shape model (ASM) would pro- vide anatomical correspondences. However, this technique is error prone for thin structures, such as the scapular blade or orbital walls. There- fore, we developed a new method relying on shape model fitting and local correction relying on image similarities. The method was evaluated on three challenging anatomical locations: (i) healthy and osteoarthritic scapulae, (ii) orbital bones, and (iii) mandible. On average, results were accurate with surface distance of about 0.5mm and average Dice coef- ficients above 90%. This approach was able to separate joint bone sur- faces, even in challenging pathological situations such as osteoarthritis. Since anatomical correspondences are propagated during segmentation, the method can directly provide anatomical measurements, define per- sonalized cutting guides, or determine the bone regions to be used to contour patient-specific implants