Experiment Setup

5.5.1.1 Image Material

Thirty female ballet dancers from the Grand Théâre of Geneva and 13 additional female subjects (average age of all 43 subjects: 25.1 years) gave written consent to be acquired using two different MRI protocols on a 1.5T MRI device (Philips Medical Systems). These protocols were defined by MIRALab and the radiological department of the University Hospital of Geneva [Gil07]. The first protocol VIBE (Axial 3D T1, TR/TE= 4.15/1.69 ms, FOV/Matrix= 35 cm/256×256, resolution=

1.367×1.367×5 mm) was used to acquire both thighs of each subject in supine position covering the FOV from the iliac crests to the beginning of the proximal tibias (Fig. 5.16(a)). This protocol had fast acquisition time resulting in low resolution images. This produced strong partial volume effect which is clearly visible in the articular area (middle of Fig. 5.16(b)).

(a)

VB TF cVB

(b)

Figure 5.16: MRI images acquired in supine position used in experiments.(a)Coronal slice of MRI volume acquired with VIBE protocol and with a right femur bone overlay. (b) In the left subfigure the different field of views (FOV) of the VIBE (VB), right cropped VIBE (cVB) and right TRUEFISP (TF) images can be compared. Bones are only partially visible in the TF and cVB datasets, particularly the femurs. In the middle and right subfigures, enlarged coronal slices of the hip articulation with cVB and TF protocols are respectively shown, where the black rectangle in the cVB slice corresponds to the TF FOV extents.

The second protocol TRUEFISP (Sagittal 3D T2^∗, TR/TE= 10.57/4.63 ms, FOV/Matrix= 20 cm/384

×384, resolution= 0.52×0.52×0.6 mm) had a significantly smaller FOV around the right or left hip joint but it offered a better resolution and signal-to-noise ratio (right of Fig. 5.16(b)). The

left part of Fig. 5.16(b)shows the large difference between the FOV of VIBE (VB) and TRUEFISP (TF) protocols. It also illustrates an additional type of artificially created images, which was obtained by cropping the large VIBE datasets around the hip joint with slightly larger FOV than the TF. These cropped images are hereafter referred to as cropped VIBE (cVB). Volumes of cVB and TF corresponded in average to 6.2% and 3.5% of VB’s volume, respectively. These images with small FOV will be used to assess the impact of FOV and SSMs builit with complete and corrupted shapes on the segmentation.

Furthermore, dancers entered the scanner in right and left split postures (Fig. 5.17(a)) and were scanned using the same protocols, producing the “split” datasets (Figs. 5.17(b)and(c)). Subjects were monitored and asked to remain as still as possible during the acquisition to avoid the formation of motion artifacts.

(a) (b) (c)

Figure 5.17:MRI images acquired in split position used in our experiments.(a)A volunteer before entering the MRI scanner in split posture. (b)A coronal slice of a split MRI image with bone overlay. (c)Volume rendering of a split MRI showing the extreme posture performed by the subject.

5.5.1.2 Shape and Appearance Priors Computation

The left and right hip joint bones (i.e., the bone “sides”) which consist of the pair of hip bones and femurs (i.e. the bone “types”) were semi-manually segmented as described in Sec. 4.3.1.1of previous Chap. 4, yielding a large collection of 172 models. These models were used as reference or training shapes. For each shape chosen for training purposes, four different shape resolutions were created based on the multi-resolution scheme (Sec. 4.2.1.3). From the training dataset composed of images and shapes, shape and appearance priors were computed as follows. SSMs per shape resolution were built after point correspondence refinement (Sec.4.3.1.2) with different multi-resolution alignment schemes (Section 4.3.1.3) that will be specified in each experiment.

For the highest shape resolution level, no SSM was computed and the segmentation evolution regularization was only dictated by the smoothing constraints. This allowed the segmentation of fine details not represented by the SSM.

In the modeling of the IPs appearance, we exploited both the first order statistics by computing

an average intensity profile (IP) per vertex and a multivariate Gaussian distribution (MGD) (Sec.

4.4). For each IP, 25 intensity samples at the shape interior regularly spaced by 0.5 mm were computed. Similarly, we computed 5 samples at the shape exterior [Gil07].

5.5.1.3 Initialization and Evolution Settings

In experiments involving the constrained global initialization of Sec. 5.2.3, the HJC was manually positioned in the images with an error experimentally set as δ= [1, 1, 1]voxels, where minor changes (e.g.,δ= [1.5, 1.5, 1.5]) had no significant effect in the overall segmentation performance.

The parameters we used for the DE minimizer were suggested in the work of [SP95]:F=0.8,C= 0.9 and the number of individuals was set as 10 times the total number of parameters to optimize.

Furthermore, we used the “RAND1BIN” crossover scheme and set the number of generations to 100. Each individual of the first generation was uniformly randomized.

In all experiments equal weight was given to image forces and energies in both the constrained initialization (i.e., β = 0.5 in Sec. 5.2.3.4) and the deformable model-based segmentation (i.e.

α^g=α^ip =α^mgd in Sec. 5.3.2.1). All experiments used the gradient force f^g in conjunction with exclusively one type of IP-based force, either f^ip or f^mgd. Similarly, the smoothing force (Sec.

5.3.3.1) was exclusively used with either the PDM-based force f^pdm (Sec. 5.3.3.3) or the shape memory force f^smem (Sec. 5.3.3.2), with equal weights (i.e. α^smo = α^pdm = α^smem). When hip bones were simultaneously segmented, the weight α^sym of the symmetric force (Sec. 5.3.2.3) corresponded to 10% of the external forces’ total weight. The total contribution of external forces was set to 30% of the total forces as proposed in [GMT10]. Constraint point-based forces (Sec.

5.3.2.2) were not used in any experiment, except in those based on collaborative segmentation (Sec.5.5.7) since they involved user interaction.

The multi-resolution evolution scheme (Sec. 4.2.1.4) adopted an iteration schedule of 300/100/20/5 (i.e. first level had 300 iterations, second one had 100, etc.). Similarly, the search depth L used in image forces decreased according to the schedule 10/10/5/2 with a constant search step S of 0.5 mm (Sec. 5.3.2.1). Settings for the special case of MRF-based force are presented in the corresponding experiment (Sec. 5.5.3).

5.5.1.4 Description of Experiments

We used the distance measures presented in Sec. 4.5.1.1to assess the accuracy and robustness of the segmentation based on the available ground truth. To statistically compare the results of two segmentation approaches based on the statistical significance procedure of Sec. 4.5.1.2, the ASRSD measure was selected since it is correlated to both the average and variance of the distance. A total of 6 main experiments were set up:

– Experiment 1 – Hip joint bone segmentation(Sec.5.5.2): comprehensive study of femurs and hip bones segmentation in supine MRI with different FOV.

– Experiment 2 – Robust SSM and MRF Local Modeling (Sec. 5.5.3): evaluation in the seg-mentation context of SSMs built with complete and corrupted shapes, by also investigating the impact of the training size and the performance of MRF.

– Experiment 3 – Dual-Posture MRI Segmentation (Sec. 5.5.4): segmentation of femurs in supine and split MRI acquired with the VB protocol. This set of MRI images is denoted as

“dual-posture” MRI.

– Experiment 4 – Coupled registration-segmentation (Sec. 5.5.5): Coupled segmentation and registration of femurs in multiple VB and TF images.

– Experiment 5 – GPU-based Segmentation (Sec. 5.5.6): segmentation of supine VB images with the GPU-based approach.

– Experiment 6 – Collaborative segmentation(Sec. 5.5.7): illustration of the collaborative seg-mentation.

All experiments were run on a single-core 3.40 GHz PC equipped with an NVidia GTX 8800 graphics board with 768 Mb RAM.