3D Automatic Feature Construction System for Lower Limb Alignment

Xing, Qi; Yang, Wenzhen; Theiss, Mark M.; Li, Jihui; Peng, Qiang; Chen, Jim X.

doi:10.1109/cw.2010.15

Cited by 5 publications

(5 citation statements)

References 30 publications

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“…A benefit of fitting deformable bone models is that adding more landmarks is trivial, as positioning the landmark once is sufficient to find it in all subsequent subjects. This enables inclusion of landmarks without easily discernible morphological features, in contrast to methods that use specific bone morphology features [12][13][14][15][16] . Further research using the proposed method could focus on defining better 3D measurements to assess the bone morphometry.…”

Section: Discussionmentioning

confidence: 99%

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Automatic Assessment of Lower-Limb Alignment from Computed Tomography

Kuiper

Seevinck

Viergever

et al. 2023

Journal of Bone and Joint Surgery

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Background: Preoperative planning of lower-limb realignment surgical procedures necessitates the quantification of alignment parameters by using landmarks placed on medical scans. Conventionally, alignment measurements are performed on 2-dimensional (2D) standing radiographs. To enable fast and accurate 3-dimensional (3D) planning of orthopaedic surgery, automatic calculation of the lower-limb alignment from 3D bone models is required. The goal of this study was to develop, validate, and apply a method that automatically quantifies the parameters defining lower-limb alignment from computed tomographic (CT) scans.Methods: CT scans of the lower extremities of 50 subjects were both manually and automatically segmented. Thirty-two manual landmarks were positioned twice on the bone segmentations to assess intraobserver reliability in a subset of 20 subjects. The landmarks were also positioned automatically using a shape-fitting algorithm. The landmarks were then used to calculate 25 angles describing the lower-limb alignment for all 50 subjects. Results:The mean absolute difference (and standard deviation) between repeat measurements using the manual method was 2.01 ± 1.64 mm for the landmark positions and 1.05°± 1.48°for the landmark angles, whereas the mean absolute difference between the manual and fully automatic methods was 2.17 ± 1.37 mm for the landmark positions and 1.10°± 1.16°for the landmark angles. The manual method required approximately 60 minutes of manual interaction, compared with 12 minutes of computation time for the fully automatic method. The intraclass correlation coefficient showed good to excellent reliability between the manual and automatic assessments for 23 of 25 angles, and the same was true for the intraobserver reliability in the manual method. The mean for the 50 subjects was within the expected range for 18 of the 25 automatically calculated angles. Conclusions:We developed a method that automatically calculated a comprehensive range of 25 measurements that defined lower-limb alignment in considerably less time, and with differences relative to the manual method that were comparable to the differences between repeated manual assessments. This method could thus be used as an efficient alternative to manual assessment of alignment.Level of Evidence: Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence. Surgical correction of a lower-limb malalignment typically consists of an osteotomy in the femur and/or tibia. For surgical planning, the malalignment is quantified using predefined landmarks and angles, such as the center points of the femoral head, knee, and ankle, which define the mechanical hip-knee-ankle (mHKA) angle. Paley 1 described these angles, and the methods to restore the alignment, for almost all types of malalignments.Conventionally, these angles are measured on 2-dimensional (2D) radiographs to define the malalignment in the frontal and sagittal planes. The introduction of 3-dimensional (3D) imaging and planning now enables mor...

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Section: Discussionmentioning

confidence: 99%

“…Subburaj et al 12,13 proposed methods that used the bone surface curvature. Several studies used alignment of the axis of inertia, model cross-sections, or shape-fitting [14][15][16] . Other authors matched statistical shape models (SSMs) of bones annotated with predefined landmarks to the bones of new patients [17][18][19][20] .…”

mentioning

confidence: 99%

Automatic Assessment of Lower-Limb Alignment from Computed Tomography

Kuiper

Seevinck

Viergever

et al. 2023

Journal of Bone and Joint Surgery

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“…A benefit of fitting deformable bone models is that adding more landmarks is trivial, as indicating the landmark once is sufficient to find it on all subsequent subjects. This enables inclusion of landmarks without easily discernible morphological features, in contrast to methods that use specific bone morphology features 77,[154][155][156][157] . Using the proposed method, further research could focus on defining better 3D measurements to assess the bone morphometry.…”

Section: Discussionmentioning

confidence: 99%

“…This thesis is not the first work on automated orthopaedic planning, as many authors before have developed, researched and evaluated techniques to automate alignment assessment 127,154,155,157 and pre-operative planning 20,131,132,201,202 . Additionally, various computer-assisted surgical planning tools are currently commercially available that assist in different steps of the planning process 22 .…”

Section: Future Perspectives In Automated Orthopaedic Planningmentioning

confidence: 99%

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Computer-Assisted Osteotomy Planning

Kuiper¹

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In this thesis, the ultimate goal has been to strive towards (automatic) computer assisted planning of reconstructive osteotomies. However, the quality of the output of such a workflow depends on the quality of all the intermediary steps, between the acquisition of a medical image, to the virtual reconstruction of the bone. In each chapter of this thesis, we have tried to overcome some of the challenges posed by these steps, and to evaluate the intermediate results. First, a method to improve the registration of CT to MRI scans for knee joint images was developed. Large deformations can arise due to differences in patient positioning between scanners, making registration complex. The study showed that non-rigid registration using deformable B-Splines could be improved by initializing the registration with a rigid registration of bones and estimating soft tissue deformation using a dual quaternion-based interpolation step. Second, an automatic and efficient deep learning-based method for semantic segmentation of six separate bones from lower extremity CT scans was proposed and validated. Existing deep learning segmentation algorithms were found to be slow and memory-intensive for this application. The optimized architecture of a cascaded U-net approach outperformed the state-of-the-art nn-Unet in memory efficiency, speed, and accuracy. Automated morphometric measurements on the hip joint were clinically validated, showing good to excellent correlation with measurements derived from manual segmentations. Third, the impact of using CT and MRI to segment bone and/or cartilage on pre-operative osteotomy planning of the forearm in adolescent patients was evaluated. An automated, deterministic planning method was developed to directly compare planning outcomes and avoid inter- or intraoperator variation. Segmentations of bone from both CT and MRI were used to assess the impact of different modalities on planning. Additionally, segmentations of bone and bone combined with cartilage from MR images were analyzed to study the influence of cartilage on planning outcomes. Results showed excellent correlation between realignment parameters across different segmentations, with small significant differences in the translational part of realignment. A positive correlation was found between cartilage amount and differences in planning on bone and bone with cartilage, suggesting the importance of considering cartilage impact on joint shape in young patients. However, further research is needed to explore MRI as a potential replacement for CT in osteotomy planning due to the small sample size and time-consuming manual segmentation. Lastly, a method to automatically quantify alignment parameters describing lower extremity morphology from 3D bone models was evaluated. A completely automatic pipeline was developed to extract these parameters using a CT scan as input. The alignment parameters were calculated using landmarks from previous studies and expanded upon to quantify all parameters needed for lower extremity realignment planning. This automatic method eliminated the need for manual operation and produced results that closely corresponded to intrarater variability. This advancement could pave the way for automatic realignment planning of lower extremities, addressing cases where a healthy contralateral example is unavailable for unilateral bone realignment planning.

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Quantitative Measurements of Knee Alignments in Three-Dimension Bone Models

Yang

Zhang

et al. 2015

2015 International Conference on Virtual Reality and Visualization (ICVRV)

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3D Automatic Feature Construction System for Lower Limb Alignment

Cited by 5 publications

References 30 publications

Automatic Assessment of Lower-Limb Alignment from Computed Tomography

Automatic Assessment of Lower-Limb Alignment from Computed Tomography

Computer-Assisted Osteotomy Planning

Quantitative Measurements of Knee Alignments in Three-Dimension Bone Models

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