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We present a workflow for producing a statistical shape model (SSM) of the femur with automatically defined regions resembling general anatomic features. Explicitly defined regions enforce correspondence of anatomical features, and allow the shapes of regions to be analysed independently if needed. A training set of manually segmented femur surfaces are partitioned according to Gaussian curvature. Partitioned regions across the training set are then grouped using mean-shift clustering to identify the most stable regions into which surfaces are divided. Reference piecewise parametric meshes are designed for and fitted to each region, and used to train regional SSMs through fitting -training iterations. Fitted region meshes are assembled into full femur meshes for training a whole femur region-based SSM (rSSM). Partitioning, clustering and shape modelling results are presented for 41 femurs. In comparison to a non-regional SSM, the rSSM was more efficient and correspondent in its approximation of unseen femurs.

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All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. Moisture-Dependent Wettability of Artifi cial Hydrophobic Soils and Its Relevance for Soil Water Desorption Curves Soil Physics Soil wettability strongly depends on soil water content. King (1981) indicated that soil hydrophobicity was stable between oven-dry and air-dry water contents, was either unchanged or increased between air-dry water content and wilting point, and fi nally decreased rapidly and became wettable near fi eld capacity. Th is is so-called single peak wettability (de Jonge et al., 1999). de Jonge et al. (1999) and reported double peak wettability in the soil water repellency-water content curve, that is, there were two strong water repellent points from saturation to oven-dry state, one in the dry region and the other one at higher water contents. Poulenard et al. (2004) and Lichner et al. (2006) showed that soil hydrophobicity increased with decreasing water content. Experimental data from a natural water repellent soil showed that water repellency decreased with increasing soil water content on heterogeneous soil-water interfaces (Täumer et al., 2005). de Jonge et al. (2007) proposed that water repellency was markedly increased between pF ( = log 10 (h), where h is the pressure head expressed in cm) 2.5 and 3 for natural hydrophobic coarse sandy soils. Karunarathna et al. (2010)

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Marker-based dynamic functional or regression methods are used to compute joint centre locations that can be used to improve linear scaling of the pelvis in musculoskeletal models, although large errors have been reported using these methods. This study aimed to investigate if statistical shape models could improve prediction of the hip joint centre (HJC) location. The inclusion of complete pelvis imaging data from computed tomography (CT) was also explored to determine if free-form deformation techniques could further improve HJC estimates. Mean Euclidean distance errors were calculated between HJC from CT and estimates from shape modelling methods, and functional-and regression-based linear scaling approaches. The HJC of a generic musculoskeletal model was also perturbed to compute the root-mean squared error (RMSE) of the hip muscle moment arms between the reference HJC obtained from CT and the different scaling methods. Shape modelling without medical imaging data significantly reduced HJC location error estimates (11.4 ± 3.3mm) compared to functional (36.9 ± 17.5mm, p=<0.001) and regression (31.2 ± 15mm, p=<0.001) methods. The addition of complete pelvis imaging data to the shape modelling workflow further reduced HJC error estimates compared to no imaging (6.6 ± 3.1mm, p=0.002). Average RMSE were greatest for the hip flexor and extensor muscle groups using the functional (16.71mm and 8.87mm respectively) and regression methods (16.15mm and 9.97mm respectively). The effects on moment-arms were less substantial for the shape modelling methods, ranging from 0.05 to 3.2mm. Shape modelling methods improved HJC location and muscle moment-arm estimates compared to linear scaling of musculoskeletal models in patients with hip osteoarthritis.