Abstract

The objective of our study is to evaluate the accuracy of an X-ray based image segmentation system for patient specific instrument (PSI) design or any other surgical application that requires 3D modeling of the knee.

The process requires two bilateral short film X-ray images of knee and a standing long film image of the leg including the hip and ankle. The short film images are acquired with an X-ray positioner device that is embedded with fiducial markers to correct for setup variation in source and cassette position. An automated image segmentation algorithm, based on a statistical model that couples knee bone shape and radiographic appearance, calculates 3D surface models of the knee from the bi-lateral short films (Imorphics, Manchester UK) (Figure 1). Surface silhouettes are used to inspect and refine the automatically generated segmentation; the femur and tibia mechanical axes are then calculated using automatically generated surface model landmarks combined with user-defined markups of the hip and ankle center from the standing long film (Figure 2).

The accuracy of the 2D/3D segmentation system was evaluated using simulated X-ray imagery generated from one-hundred osteoarthritic, lower limb CT image samples using the Insight Toolkit (Kitware, Inc.). Random, normally distributed variations in source and cassette positions were included in the dataset. Surface accuracy was measured using root-mean-square (RMS) point-to-surface (P2S) distance calculations with respect to paired benchmark CT segmentations. Landmark accuracy was calculated by measuring angular differences between the 2D/3D generated femur and tibia mechanical tibia with respect to paired CT-generated landmark data.

The paired RMS sample mean and standard deviation of femur P2S errors on the distal quarter of the femur after auto-segmentation was 1.08±0.20mm. The RMS sample mean and standard deviation of tibia P2S errors on the proximal quarter of the tibia after auto-segmentation was 1.16±0.25mm. The paired sample mean and standard deviation of the femur and tibia mechanical axis accuracy with respect to benchmark CT data landmarks were 0.02±0.42[deg] and −0.33±0.56[deg], respectively. Per surface-vertex sample RMS P2S errors are illustrated in Figure 3.

Visual inspection of RMS results found the automatically segmented femur to be very accurate in the shaft, distal condyles, and posterior condyles, which are important for PSI guide fit and accurate planning. Similarly, the automatically segmented tibia was very accurate in the shaft and plateaus, which are also important for PSI guide fit. Osteophytes resulted in some RMS differences (Figure 3), as was expected due to the know limitations of osteophyte imaging with X-ray. PSI-type applications that utilize X-ray should account for osteophyte segmentation error. Overall, our results based on simulated radiographic data demonstrate that X-ray based 2D/3D segmentation is a viable tool for use in orthopaedic applications that require accurate 3D segmentations of knee bones.

Bone marrow lesions (BMLs) have been extensively linked to the osteoarthritis (OA) disease pathway in the knee. Semi-quantitative evaluation has been unable to effectively study the spatial and temporal distribution of BMLs and consequently little is understood about their natural history. This study used a novel statistical model to precisely locate the BMLs within the subchondral bone and compare BML distribution with the distribution of denuded cartilage.

MR images from individuals (n=88) with radiographic evidence of OA were selected from the Osteoarthritis Initiative. Slice-by-slice, subvoxel delineation of the lesions was performed across the paired images using the criteria laid out by Roemer (2009). A statistical bone model was fitted to each image across the cohort, creating a dense set of anatomically corresponded points which allowed BML depth, position and volume to be calculated. The association between BML and denudation was also measured semi-quantitatively by visually scoring the lesions as either overlapping or adjacent to denuded AC, or not.

At baseline 75 subjects had BMLs present in at least one compartment. Of the 188 compartments with BMLs 46% demonstrated change greater than 727mm cubed, the calculated smallest detectable difference. The majority of lesions were found in medial compartments compared to lateral compartments and the patella (Figure 1A). Furthermore, in the baseline images 76.9% of all BMLs either overlapped or were adjacent to denuded bone. The closeness of this relationship in four individuals is shown in Figure 1B.

The distribution of lesions follows a clear trend with the majority found in the patellofemoral joint, medial femoro-tibial joint and medial tibial compartment. Moreover the novel method of measurement and display of BMLs demonstrates that there is a striking similarity between the spatial distribution of BMLs and denuded cartilage in subjects with OA. This co-location infers the lesions have a mechanical origin much like the lesions that occur in healthy patients as a direct result of trauma. It is therefore suggested that OA associated BMLs are in fact no different from the BMLs caused by mechanical damage, but occur as a result of localised disruption to the joint mechanics, a common feature of OA.