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We present an automatic lung lobe segmentation algorithm for COPD patients. The method enhances fissures, removes unlikely fissure candidates, after which a B-spline is fitted iteratively through the remaining candidate objects. The iterative fitting approach circumvents the need to classify each object as being part of the fissure or being noise, and allows the fissure to be detected in multiple disconnected parts. This property is beneficial for good performance in patient data, containing incomplete and disease-affected fissures. The proposed algorithm is tested on 22 COPD patients, resulting in accurate lobe-based densitometry, and a median overlap of the fissure (defined 3 voxels wide) with an expert ground truth of 0.65, 0.54 and 0.44 for the three main fissures. This compares to complete lobe overlaps of 0.99, 0.98, 0.98, 0.97 and 0.87 for the five main lobes, showing promise for lobe segmentation on data of patients with moderate to severe COPD.

With fluoroscopic analysis of knee implant kinematics the implant contour must be detected in each image frame, followed by estimation of the implant pose. With a large number of possibly low-quality images, the contour detection is a time-consuming bottleneck. The present paper proposes an automated contour detection method, which is integrated in the pose estimation.In a phantom experiment the automated method was compared with a standard method, which uses manual selection of correct contour parts. Both methods demonstrated comparable precision, with a minor difference in the Y-position (0.08 mm versus 0.06 mm). The precision of each method was so small (below 0.2 mm and 0.3°) that both are sufficiently accurate for clinical research purposes.The efficiency of both methods was assessed on six clinical datasets. With the automated method the observer spent 1.5 min per image, significantly less than 3.9 min with the standard method. A Bland–Altman analysis between the methods demonstrated no discernible trends in the relative femoral poses.The threefold increase in efficiency demonstrates that a pose estimation approach with integrated contour detection is more intuitive than a standard method. It eliminates most of the manual work in fluoroscopic analysis, with sufficient precision for clinical research purposes.

For multi atlas-based segmentation approaches, a segmentation fusion scheme which considers local performance measures may be more accurate than a method which uses a global performance measure. We improve upon an existing segmentation fusion method called SIMPLE and extend it to be localized and suitable for multi-labeled segmentations. We demonstrate the algorithm performance on 23 CT scans of COPD patients using a leave-one- out experiment. Our algorithm performs significantly better (p < 0.01) than majority voting, STAPLE, and SIMPLE, with a median overlap of the fissure of 0.45, 0.48, 0.55 and 0.6 for majority voting, STAPLE, SIMPLE, and the proposed algorithm, respectively.