Generic Model Construction

4.5.2.1 Lower Limb Modeling

To illustrate the anatomical modeling, we applied our methodology on datasets produced from a female and healthy subject (age: 24, height: 1m68, weight: 58kg) acquisition. The protocol is an Axial 2D T1 Turbo Spin Echo, TR/TE = 578/18ms, FOV/FA = 40cm/90^○, matrix/resolution =

512x512/0.78x0.78mm. To acquire a full lower limb, two different sessions took place and during each of these sessions, three acquisitions were performed. Each acquisition was based on the same protocol, but with varying slice thickness. First session consisted of the hip (thickness:

2mm), thigh (10mm) and knee (4mm); second session acquired the knee (2mm), leg (10mm) and foot (4mm). All acquisitions of a session were merged into a unique volume, and the two session volumes were registered together according to an appropriate knee overlap. The two resulting datasets were created with the following dimensions (resolution): 202×398×595 (0.78×0.78×2.46 mm³) and 151×213×582 (0.68×0.68×2.5 mm³) for the first and second datasets, respectively. All acquisitions were performed at the University Hospital of Geneva on a 1.5T MRI device (Philips Medical Systems). The generic modeling has been done in collaboration INRIA who assisted us in the creation of the models before optimization, i.e. in the tasks of manual segmentation and model smoothing. The institutional medical-ethical committee approved the study and the subject gave written informed consent.

From the first dataset that covers the iliac crests to the knees, the thigh was segmented with Gilles’

interactive deformable model approach [GMT10]. In this process, bones, muscles, ligaments and cartilages were reconstructed. Our modeling approach was instead applied on the second dataset which covered the area from the knee to the foot which could not be automatically segmented with the previous approach. Similarly, we reconstructed soft and bony structures. We decided to use both approaches to yield a complex lower limb dataset composed of 33 muscles and 6 bones.

A manual segmentation was first conducted with ITK-SNAP to produce a labeled volumetric image (Sec. 4.2.2.1). Then, we reconstructed triangle meshes from the different labels and pro-duced 2-simplex meshes based on the simplex mesh conversion approach. Subsequently, meshes were smoothed (Sec. 4.2.2.2) followed by re-meshing optimization (Sec. 4.2.2.3) to get models of good quality and of various resolutions. Bones were initially segmented as they were easier to distinguish in the images, then soft tissues were segmented. The lower limb is composed of many muscles in close contact. As a result, the corresponding models were inter-penetrating to each other, particularly after the smoothing procedure (Fig. 4.15(a)). This was corrected with our approach (Sec. 4.2.4) as illustrated in Fig 4.15(b). During this process, we chose rigidity parameters that prevented muscles from diffusing into the bone models. The resulting generic models are visible in Figs.4.16(a),(b)and(c). Tendons were segmented by running first a tubu-lar tracing method [AB02,PJBB05]. This approach produced different centerlines which were in some occasions incomplete due to the poor image signal-to-noise ratio apparent in some slices of the images. As a consequence, we manually corrected them by interactively placing missing centerline points as explained in Sec. 4.2.2.4. These steps ultimately yielded a very good mod-eling of the complex tendon network of the foot which is depicted in Fig. 4.16(d)–(f). Tendons were then merged to the corresponding muscles by using the fusion process presented in Sec.

4.2.3. For example, Fig. 4.16(g) illustrates the merging of the tibialis anterior muscle and its tendon. Eventually, muscles and tendons were attached to corresponding bones by using the de-scribed procedure in Sec. 4.2.2.5. Attachments were identified on the images whenever possible or estimated from relevant anatomical literature.

(a) (b)

Figure 4.15: Inter-penetration removal of muscle models. (a) Before processing, some muscles models clearly inter-penetrate to each other.(b)Results after correction.

(a) (b) (c)

(d) (e) (f) (g)

Figure 4.16: Lower limb generic model construction. (a)-(c)3D models of reconstructed lower limb with bones and attached muscles.(d)-(f)Foot tendon models.(g)Tibialis anterior muscle model merged with its tendon, where the closeup view shows the merging area.

4.5.2.2 Knee Modeling

A high resolution MRI image of the knee was sagittaly acquired with dimensions of 512×512× 143 and resolution 0.39×0.39×1 mm³. This volumetric image offered better contrast of soft articular structures compared to the previous full lower limb images. By applying the generic model construction techniques, we produced 3D models of the articular soft structures (Fig.

4.17(f)) such as ligaments and cartilages, including meniscii, and bones (Fig. 4.17(e)). Bones were segmented with our automatic segmentation technique depicted in the next Chapter while soft tissues were reconstructed with the semi-manual process. After the smoothing and re-meshing optimization, inter-penetration removal (Sec. 4.2.4) was successfully applied to get smooth models in contact as illustrated in the labeled knee Figs. 4.17(b), (c) and (d) where articular cartilages and meniscii are in contact. A Clinical Senior Lecturer of the academic unit

(a) (b) (c)

(d) (e) (f)

Figure 4.17:Knee generic model construction.(a)This sagittal slice shows an example of tibia segmentation qualified as poor in its anterosuperior aspect (circled area). (b)and(c)sagittal slices in which cartilages and ligaments are notably visible, respectively. (d)Labeled frontal slice of the knee. Notice how structures are in close contact. (e)3D meshes of the generic knee model. (f)articular soft tissues of the generic knee model. Images rendered with MITK software.

of child health from University of Sheffield, thoroughly analyzed each of the 512 sagittal slices.

In each slice, a score was given to each structure using the evaluation scale “poor”, “fair” and

“good”. Furthermore, a series of comments were reported which provided precious insights to the given scores. Table4.1reports these scores for each type of structures as percentages. Given a structureXand an evaluation markS, the percentage quantifies the count of slices, among those

in which Xis visible, which were attributed the mark S. Scores are pretty satisfactory as more than 70% of the structure segmentation was denoted in average as good. In particular, cartilages were very well delineated (95.7% of good mark). When both good and fair segmentations were considered, the average percentage for all structures was around 84%.

The percentage of poorly segmented structures was relatively low (e.g., cartilages), except for lig-aments. Errors of ligament reconstruction were mostly observed in the attachment areas which were sometimes difficult to identify in the images despite the adapted imaging protocol. In the majority of cases, the lowest scores were attributed to small defaults of the modeled structures as depicted in Fig. 4.17(a). In this Figure, only the anterosuperior of the tibia was poorly seg-mented. Although the remaining of the tibia delineation was very satisfactory, the segmentation in this slice was reported as poor. As a result, the scoring procedure was quite strict which may explain some low values. Nevertheless, the radiologist expressed an overall positive assessment of the reconstructed structures, which looked very realistic and representative of healthy generic models.

Bone Cartilage Ligament

Poor 11.5% 3.7% 34.7%

Fair 25.7% 0.6% 11.8%

Good 62.6% 95.7% 53.5%

Table 4.1:Radiological evaluation of knee generic model construction. Percentage is related to the average number of slices in which the segmentation was labeled as poor, fair or good depending on the type of structure.