Predicting anatomical landmarks and bone morphology of the femur using local region matching

Phan, Cong-Bo; Koo, Seungbum

doi:10.1007/s11548-015-1155-8

Cited by 13 publications

(10 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The average errors ranges from ∼1.5 to ∼4.5 mm (depending in the landmark) in Ref. 20, and from ∼2.5 to ∼6 mm in Ref. 21.…”

Section: Discussionmentioning

confidence: 95%

“…As mentioned in Sec. 1, other atlas-based landmark identification methods for musculoskeletal applications have been published, either using a single atlas 19,20 or an active shape model. 11,21 References 20 and 21 have reported landmark localization errors compared to expert readers for a variety of femoral and hip locations.…”

Section: Discussionmentioning

confidence: 99%

“…To simplify the registration step in such algorithms, the atlas volumes are typically first segmented to yield a set of bone surface meshes. Ehrhardt et al 19 and Phan et al 20 proposed methods that use only one atlas image combined with multistage local registrations conducted separately in regions surrounding each landmark. For cases where multiple atlases are available, registration and transfer of landmarks are achieved with active shape models 11,21 or by using individual registrations of each atlas followed by a voting stage to select the best-matching registered atlas for landmark transfer.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Atlas-based algorithm for automatic anatomical measurements in the knee

et al. 2019

View full text Add to dashboard Cite

We present an algorithm for automatic anatomical measurements in tomographic datasets of the knee. The algorithm uses a set of atlases, each consisting of a knee image, surface segmentations of the bones, and locations of landmarks required by the anatomical metrics. A multistage volume-to-volume and surface-tovolume registration is performed to transfer the landmarks from the atlases to the target volume. Manual segmentation of the target volume is not required in this approach. Metrics were computed from the transferred landmarks of a best-matching atlas member (different for each bone), identified based on a mutual information criterion. Leave-one-out validation of the algorithm was performed on 24 scans of the knee obtained using extremity cone-beam computed tomography. Intraclass correlation (ICC) between the algorithm and the expert who generated atlas landmarks was above 0.95 for all metrics. This compares favorably to inter-reader ICC, which varied from 0.19 to 0.95, depending on the metric. Absolute agreement with the expert was also good, with median errors below 0.25 deg for measurements of tibial slope and static alignment, and below 0.2 mm for tibial tuberosity-trochlear groove distance and medial tibial depth. The automatic approach is anticipated to improve measurement workflow and mitigate the effects of operator experience and training on reliability of the metrics.

show abstract

“…The average errors ranges from ∼1.5 to ∼4.5 mm (depending in the landmark) in Ref. 20, and from ∼2.5 to ∼6 mm in Ref. 21.…”

Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Atlas-based algorithm for automatic anatomical measurements in the knee

et al. 2019

View full text Add to dashboard Cite

show abstract

“…2a ). 20 For each point in a given region of interest, the closest point on the opposite contact surface was identified. For each point (point a) in a region of interest, the closest points in two consecutive frames (points b and c) were identified.…”

Section: Methodsmentioning

confidence: 99%

Relative movement on the articular surfaces of the tibiotalar and subtalar joints during walking

et al. 2018

Bone & Joint Research

View full text Add to dashboard Cite

ObjectivesThe objective of this study was to quantify the relative movement between the articular surfaces in the tibiotalar and subtalar joints during normal walking in asymptomatic individuals.Methods3D movement data of the ankle joint complex were acquired from 18 subjects using a biplanar fluoroscopic system and 3D-to-2D registration of bone models obtained from CT images. Surface relative velocity vectors (SRVVs) of the articular surfaces of the tibiotalar and subtalar joints were calculated. The relative movement of the articulating surfaces was quantified as the mean relative speed (RS) and synchronization index (SIENT) of the SRVVs.ResultsSIENT and mean RS data showed that the tibiotalar joint exhibited translational movement throughout the stance, with a mean SIENT of 0.54 (sd 0.21). The mean RS of the tibiotalar joint during the 0% to 20% post heel-strike phase was 36.0 mm/s (sd 14.2), which was higher than for the rest of the stance period. The subtalar joint had a mean SIENT value of 0.43 (sd 0.21) during the stance phase and exhibited a greater degree of rotational movement than the tibiotalar joint. The mean relative speeds of the subtalar joint in early (0% to 10%) and late (80% to 90%) stance were 23.9 mm/s (sd 11.3) and 25.1 mm/s (sd 9.5), respectively, which were significantly higher than the mean RS during mid-stance (10% to 80%).ConclusionThe tibiotalar and subtalar joints exhibited significant translational and rotational movement in the initial stance, whereas only the subtalar joint exhibited significant rotational movement during the late stance. The relative movement on the articular surfaces provided deeper insight into the interactions between articular surfaces, which are unobtainable using the joint coordinate system.Cite this article: C-B. Phan, D-P. Nguyen, K. M. Lee, S. Koo. Relative movement on the articular surfaces of the tibiotalar and subtalar joints during walking. Bone Joint Res 2018;7:501–507. DOI: 10.1302/2046-3758.78.BJR-2018-0014.R1.

show abstract

“…Hence, an automatic identification of landmarks is preferable. However, automatic methods for landmark detection should provide reproducible results, be robust against the spatial alignment of the femur and handle the large interindividual variability 17 of the femoral morphology.…”

mentioning

confidence: 99%

A robust method for automatic identification of femoral landmarks, axes, planes and bone coordinate systems using surface models

Fischer

Grothues

Habor

et al. 2020

Sci Rep

View full text Add to dashboard Cite

The identification of femoral landmarks is a common procedure in multiple academic fields. Femoral bone coordinate systems are used particularly in orthopedics and biomechanics, and are defined by landmarks, axes and planes. A fully automatic detection overcomes the drawbacks of a labor-intensive manual identification. In this paper, a new automatic atlas- and a priori knowledge-based approach that processes femoral surface models, called the A&A method, was evaluated. The A&A method is divided in two stages. Firstly, a single atlas-based registration maps landmarks and areas from a template surface to the subject. In the second stage, landmarks, axes and planes that are used to construct several femoral bone coordinate systems are refined using a priori knowledge. Three common femoral coordinate systems are defined by the landmarks detected. The A&A method proved to be very robust against a variation of the spatial alignment of the surface models. The results of the A&A method and a manual identification were compared. No significant rotational differences existed for the bone coordinate system recommended by the International Society of Biomechanics. Minor significant differences of maximally 0.5° were observed for the two other coordinate systems. This might be clinically irrelevant, depending on the context of use and should, therefore, be evaluated by the potential user regarding the specific application. The entire source code of the A&A method and the data used in the study is open source and can be accessed via https://github.com/RWTHmediTEC/FemoralCoordinateSystem.

show abstract

Predicting anatomical landmarks and bone morphology of the femur using local region matching

Abstract: Anatomical landmarks and features could be accurately predicted using the proposed local region matching method. This method offers robustness and accuracy in determining anatomical bony landmarks and bone morphology for clinical and biomechanical applications.

Cited by 13 publications

References 50 publications

Atlas-based algorithm for automatic anatomical measurements in the knee

Atlas-based algorithm for automatic anatomical measurements in the knee

Relative movement on the articular surfaces of the tibiotalar and subtalar joints during walking

A robust method for automatic identification of femoral landmarks, axes, planes and bone coordinate systems using surface models

Contact Info

Product

Resources

About