Bah, Mamadou

Beillas, Philippe

Dicko, Ali-Hamadi

Faure, François

Fréchède, Bertrand

Gilles, Benjamin

Grenier, Thomas

Jolivet, Erwan

Lecomte, Christophe

Mailliez, Pierre

Moreau, Baptiste

Petit, Philippe

Portejoie, Pierre

Southampton (e-Prints Soton)

See	discussions,	stats,	and	author	profiles	for	this	publication	at:	https://www.researchgate.net/publication/305680288moreau	piper	segmentationDataset	·	July	201613	authors,	including:Christophe	LecomteUniversity	of	Southampton34	PUBLICATIONS			81	CITATIONS			SEE	PROFILET.	GrenierCreatis	Medical	Imaging	Research	Center29	PUBLICATIONS			67	CITATIONS			SEE	PROFILEFrancois	FaureUniversité	Grenoble	Alpes94	PUBLICATIONS			1,555	CITATIONS			SEE	PROFILEBenjamin	GillesFrench	National	Centre	for	Scientific	Resea…55	PUBLICATIONS			416	CITATIONS			SEE	PROFILEAvailable	from:	Baptiste	MoreauRetrieved	on:	28	July	2016 22nd Congress of the European Society of Biomechanics, July 10 - 13, 2016, Lyon, France A SEGMENTATION PIPELINE FOR THE CREATION OF STATISTICAL SHAPE MODELS IN THE PIPER PROJECT Baptiste Moreau (1)(2), Ali-Hamadi Dicko (3), Pierre Mailliez (4), Pierre Portejoie (5), Christophe Lecomte (4), Mamadou Bah (4), Thomas Grenier (5), Erwan Jolivet (6), Philippe Petit (1), Bertrand Fréchède (7), François Faure (3), Benjamin Gilles (2), Philippe Beillas (7)  1. LAB PSA Renault, France; 2. LIRMM, CNRS, Université de Montpellier, France; 3. Univ. Grenoble Alpes, France 4. Computational Engineering and Design Group, University of Southampton, Southampton SO17 1BJ, UK; 5. Université de Lyon, CNRS, INSERM, CREATIS, 69621 Villeurbanne, France ; 6. CEESAR, Nanterre, France; 7. Université de Lyon, Université Claude Bernard Lyon 1, IFSTTAR, Lyon, France Introduction While Human Body Models (HBMs) based on the finite element method have shown their usefulness in automotive safety, they are typically only available for a few sizes (e.g. average male). One aim of the PIPER project is to develop tools to better describe the population variability with such HBM. To this end, Statistical Shape Models (SSMs) of the full skeleton are being developed using a database of CT-based scans of 100 anatomical subjects. The present work aims at evaluating explored segmentation approaches for the SSM development.  Methods Full body CT-scans acquired in overlapping sessions (head, trunk, lower limbs, voxel 0.97x0.97x0.5mm) were segmented using (A) semi-automatic tools (e.g. Slicer3D, ScanIP) (B) image-to-image registration (Elastix) to transfer a labeled reference mask and (C) model-to-image registration. For (C), the idea is to align an atlas reconstructed from one subject (Fig. 1) with target CT-scan from other subjects. The alignment is driven by a simulation using SOFA framework [1]. Two CT-scans (reference with atlas, target) are used. The user, to avoid incorrect registration due to pose variability in subjects, guides the deformation interactively. Segmentations from (A) were used to define the atlas in (B) and (C). Evaluation criteria included time to generate meshes and their suitability for SSM [2] i.e., accuracy and correspondence.  Results Four full body scans were segmented semi-automatically. While it was relatively fast for long bones, it proved too lengthy for vertebrae and ribs as they can be poorly separated in elderly subjects. Image-to-image registration was applied to one subject and was promising. However, it required removing the scanning environment semi-automatically and merging the overlapping scans. An automatic merging process (C++/ITK) was developed and tested on 15 subjects. Model-to-image registration was successfully used with 25 CT-scans to generate registered meshes of thorax, spine, pelvis and lower extremities. This efficient approach (1 hour/subject) provided meshes that are accurately registered (Fig. 2) and with point matching between subjects. This is important for SSM as it may remove additional correspondence process.  Figure 1: Red surface mesh: atlas used to perform the registrations.   Figure 2: The green registered surface projected on a slice of the target PMHS CT-scan. Left: pelvis, lower limb. Right: sternum, thoracic vertebrae. Accuracy of registration: a Dice coefficient of 0.91 for a femur.  Discussion Model to image registration was selected as the most promising approach to achieve both efficiency and accuracy. Adjustments are ongoing prior to its broad application including the articulation of some segments (e.g. extremities) in the SOFA simulation and the inclusion of landmarks in the reference model. All models and associated SSMs will be publicly released at the end of the PIPER project.  References 1. Gilles, B., Reveret, L., & Pai, D. K. (2010) and https://www.sofa-framework.org. 2. Cootes, T.F., et al. (1995)  Acknowledgements This research has received funding from the European Union 7th Framework Program ([FP7/2007-2013]) under grant agreement 605544 [PIPER, http://piper-project.eu]. We would like to thank Volmo Ltd for its contributions to the semi-automatic segmentation. 

A segmentation pipeline for the creation of statistical shape models in the PIPER project

A segmentation pipeline for the creation of statistical shape models in the PIPER project

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