Automated identification of anatomical landmarks on 3D bone models reconstructed from CT scan images

https://doi.org/10.1016/j.compmedimag.2009.03.001Get rights and content

Abstract

Identification of anatomical landmarks on skeletal tissue reconstructed from CT/MR images is indispensable in patient-specific preoperative planning (tumour referencing, deformity evaluation, resection planning, and implant alignment and anchoring) as well as intra-operative navigation (bone registration and instruments referencing). Interactive localisation of landmarks on patient-specific anatomical models is time-consuming and may lack in repeatability and accuracy. We present a computer graphics-based method for automatic localisation and identification (labelling) of anatomical landmarks on a 3D model of bone reconstructed from CT images of a patient. The model surface is segmented into different landmark regions (peak, ridge, pit and ravine) based on surface curvature. These regions are labelled automatically by an iterative process using a spatial adjacency relationship matrix between the landmarks. The methodology has been implemented in a software program and its results (automatically identified landmarks) are compared with those manually palpated by three experienced orthopaedic surgeons, on three 3D reconstructed bone models. The variability in location of landmarks was found to be in the range of 2.15–5.98 mm by manual method (inter surgeon) and 1.92–4.88 mm by our program. Both methods performed well in identifying sharp features. Overall, the performance of the automated methodology was better or similar to the manual method and its results were reproducible. It is expected to have a variety of applications in surgery planning and intra-operative navigation.

Introduction

Anatomical landmarks are distinct regions or points on skeletal tissues with uniqueness in shape characteristics in their vicinity. Most of the landmarks used during surgical procedures are manually palpable and geometrically recognizable. For example, in knee joint major landmarks include: on distal femur bone—most distal point of medial and lateral condyles (MC and LC), medial and lateral epicondyle eminences (ME and LE), peak on medial and lateral side of anterior ridge (MP and LP) of prominent patellar groove, and adductor magnus attachment tubercle (AT); on proximal tibia—tibial tuberosity (TT), Gerdy's tubercle (GT), apex of Fibula (HF), most medial and lateral point of tibial plateau (MP and LP), and medial and lateral intercondylar tubercle (MIT and LIT). These are shown in Fig. 1.

Accurate localisation of 3D landmarks on skeletal tissue is indispensable in biomechanical studies and computer integrated surgery, including custom implant design and positioning [1], [2], [3], [4], intra-operative navigation, joint kinematics study [5], [6], [7], deformities assessment [8], tumour resection [9], referencing [10], and registration in shape models [11] (Lorenz and Krahnstover [29]). In situations involving tumours and highly deformed bones, patient-specific 3D anatomical models reconstructed from CT or MR images are used for visualisation and surgical planning (tumour localisation, resection planning, and prosthesis positioning). Various methods used in current practice for localising anatomical landmarks on 3D bone models are given in Table 1 along with the issues associated with them. Variability associated with identifying and locating anatomical landmarks (for example, on the knee) has the potential to affect the surgical outcome. Semi-automatic methods for landmark identification with interactive control offer the possibility of overcoming the above problems. However, existing computational approaches to landmark detection often suffer from a significant number of false detections [12].

Several studies have been reported on interactive localisation of landmarks on dry bone models, laser digitized data [8], [13] or medical images [3]. Van Sint Jan [14] presented a comprehensive list of bony landmarks and described the procedure for locating them on a patient by palpation. Della Croce et al. [15] carried out experimental studies on manual location of lower limb landmarks on dry bone models with a group of surgeons and concluded that the variations are in the range 6–25 mm. Liu et al. [8] manually identified landmarks on a scanned surface of foot model, guided by curvature values, for evaluating tibial torsion. Reproducibility of the results appears to be dependent on user's knowledge of landmarks. The effect of scanning artefacts is not very clear. Yang et al. [16] studied the relationships between bony and soft tissue landmarks using cephalometric radiographs to diagnose facial growth abnormalities prior to treatment. Maudgil et al. [17] manually identified anatomical landmarks on a 3D model reconstructed from MRI images for morphometric analysis. In general, manual localisation of landmarks and related measurements consume time, require a high level of expertise, are tedious and time-consuming, and may lack in accuracy/repeatability.

Advances in medical imaging, image analysis, and computational capabilities offer an opportunity to exploring shape property for localising anatomical landmarks. Invariant geometric measures such as curvatures, extreme points, and higher order derivatives can be used for classifying surface regions [18] based on their shapes. Surface curvature is independent of position and orientation of patients and it can help in reliable detection of geometric features, from which landmarks can be derived. Surfaces can be classified into various regions such as ridges, umbilicus and singular lines [19], [20]. Drerup and Hierholzer [27] identified lumbar dimples based on surface curvature, and used it to study spine shape and muscle movement over pelvis. Ehrhardt et al. [28] used pre-defined templates to locate landmarks on 3D hip models for surgery planning. Accurate morphing and registering the atlas model with the patient model is however necessary before transferring the landmarks. Frangi et al. [11] generated dense landmark nodes (decimating dense triangular data of the boundary) on 3D shapes and used for constructing active shape models. This method does not actually identify or localise individual landmarks. Rohr [21] and Worz and Rohr [12] showed the use of invariant principles (first order derivatives) in identifying landmarks from 2D CT images. These image based landmarks are used for multi-modal image registration. This could be extended to localising 3D skeletal landmarks from reconstructed three-dimensional anatomical models, but does not appear to have been attempted so far.

There appears to be very little reported work on fully or semi-automated identification of anatomical landmarks on skeletal tissue. One reason could be that these landmarks are influenced by bone morphology, and hence prior knowledge of anatomical landmarks is necessary to locate and identify them precisely [9]. Also positional uncertainty of anatomical landmarks is caused by the fact that they are not clearly identifiable discrete points, but are rather relatively large and curved areas [15]. The ideal situation would be the ability to palpitate or compute the same reference point on bony prominence in a repeated manner. Characterizing specific landmarks for the desired application based on their invariant geometric characteristics and identifying them would be an effective way to automate the procedure, increase robustness and repeatability, and reduce false identifications.

In this work, we have developed a systematic approach to identify anatomical landmarks driven by their general geometric characteristics (curvature analysis) and adjacency relationship between the landmarks (rules), and demonstrated the same for a knee joint. The methodology is described first, followed by the results of its implementation. The last section summarizes the work and concludes with directions for future research.

Section snippets

Methodology

The overall methodology for identifying anatomical landmarks is shown in Fig. 2. First a 3D surface model is reconstructed from a set of CT images. It is assumed that the reconstructed model is perfectly segmented (bone density thresholding) to represent the true anatomical morphology of the bone. Then principal curvatures and their derivatives are computed on every vertex of the triangulated surface. These vertices are segregated based on the curvature descriptors into various regions.

Results and discussion

A set of CT images were obtained for the left knee of a 26-year-old male (format: DICOM, resolution: 512 × 512 pixels; thickness: 0.67 mm; slices: 668). A software program developed in our lab was employed for reconstructing its 3D model [25]. The CT images were smoothed and processed with a gradient operator to highlight high intensity change. Images were thresholded (range of intensity values) by analyzing image intensity histograms to eliminate soft tissue for more effective extraction of bone.

Conclusion

We have described an automated approach for localising and identifying landmarks on 3D anatomical models. This has been achieved by combining surface characteristics (curvature) and spatial adjacency of the landmarks on a particular bone. False localisation of anatomical landmarks is minimized by the use of recursive search and voting algorithm and spatial-adjacency relation. The quantitative results show that they are reproducible and generally superior or close to manual method of

Disclosure

None.

Acknowledgements

This work is a part of an ongoing project in OrthoCAD Network Research Centre at Indian Institute of Technology Bombay for developing a computer aided orthopaedic implant design and surgery planning system in collaboration with Tata Memorial Hospital, Mumbai. It is supported by the Office of the Principal Scientific Adviser to the Government of India, New Delhi.

K. Subburaj is a PhD scholar in the Department of Mechanical Engineering, Indian Institute of Technology Bombay, India and attached to the OrthoCAD Network Research Centre. He received his M.Eng from the M.S. University of Baroda, India in 2005. His current research interests are BioCAD, 3D Geometric Reasoning, Surgery Planning, and Rapid Prototyping.

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    K. Subburaj is a PhD scholar in the Department of Mechanical Engineering, Indian Institute of Technology Bombay, India and attached to the OrthoCAD Network Research Centre. He received his M.Eng from the M.S. University of Baroda, India in 2005. His current research interests are BioCAD, 3D Geometric Reasoning, Surgery Planning, and Rapid Prototyping.

    Dr. B. Ravi is a professor of Mechanical Engineering at IIT Bombay and Founder-coordinator of OrthoCAD Network Research Centre. He completed his engineering degree from National Institute of Technology, Rourkela in 1986, followed by masters and PhD from Indian Institute of Science, Bangalore in 1992. His areas of research include casting design and simulation, product lifecycle engineering, and Bio-CAD/CAM. He has published about 160 technical papers, and given over 100 invited talks. He is a reviewer for ASME, IEEE, IJAMS, and IJPR. He is a member of ASME Bio Manufacturing Committee, and Fellow of the Institution of Engineers (India).

    Dr. Manish Agarwal is an orthopaedic oncologist and associate professor at Tata Memorial Hospital, Mumbai. He completed his MBBS and MS (Ortho) from University of Bombay in 1987 and 1992 respectively. His main interest is limb salvage surgery for bone and soft tissue tumours. He is a member of the musculoskeletal tumor society of North America and has several publications in peer reviewed journals. His current research includes development of an indigenous tumor prosthetic system as well as clinical trials with herbs like Curcumin and ashwagandha for bone sarcomas.

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