Joint detection and localization of multiple anatomical landmarks through learning

Dikmen, Mert; Zhan, Yiqiang; Zhou, Xiaoping

doi:10.1117/12.770914

Cited by 8 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other algorithms solve specific landmark detection problems by carefully studying the appearance characteristics of shape priors of the landmarks. Although these methods display robustness to common image distortions and have a low computational complexity, they demonstrate a lack of potential to be extended to other studies using different landmarks, different joints or different MR scans . Another widely used method for landmark detection is a boosting cascade of simple detectors .…”

mentioning

confidence: 99%

Automatic detection of anatomical landmarks on the knee joint using MRI data

Xue

Doellinger

et al. 2014

Magnetic Resonance Imaging

View full text Add to dashboard Cite

show abstract

mentioning

confidence: 99%

Automatic detection of anatomical landmarks on the knee joint using MRI data

Xue

Doellinger

et al. 2014

Magnetic Resonance Imaging

View full text Add to dashboard Cite

show abstract

“…Definition of such point locations (referred to as vertebral centroids, c i ) can be done automatically in CT (Dikmen et al 2008) and MR (Zhan et al 2012). Taking these locations as seed points for the segmentation, we formulated a spatially continuous min-cut problem with the objective function:

S = min_{u false(x false) \in false{0, 1 false}} true\int false[false(1 - u false) . D_{1} false(x false) + u . D_{2} false(x false) false] d x + true\int g false(x false) false| \nabla u false(x false) false| d x

where u ( x ) is a membership function defining whether each pixel x lies outside ( u ( x ) = 0) or inside ( u ( x ) = 1) the vertebrae.…”

Section: B Methodsmentioning

confidence: 99%

“…Automatic labeling of vertebrae in the MR image (Zhan et al 2012, Dikmen et al 2008) could be alternatively used to improve workflow.…”

Section: B Methodsmentioning

confidence: 99%

Registration of MRI to intraoperative radiographs for target localization in spinal interventions

et al. 2017

View full text Add to dashboard Cite

Purpose Decision support to assist in target vertebra localization could provide a useful aid to safe and effective spine surgery. Previous solutions have shown 3D-2D registration of preoperative CT to intraoperative radiographs to reliably annotate vertebral labels for assistance during level localization. We present an algorithm (referred to as MR-LevelCheck) to perform 3D-2D registration based on a preoperative MRI to accommodate the increasingly common clinical scenario in which MRI is used instead of CT for preoperative planning. Methods Straightforward adaptation of gradient/intensity-based methods appropriate to CT-to-radiograph registration is confounded by large mismatch and noncorrespondence in image intensity between MRI and radiographs. The proposed method overcomes such challenges with a simple vertebrae segmentation step using vertebra centroids as seed points (automatically defined within existing workflow). Forwards projections are computed using segmented MRI and registered to radiographs via gradient orientation (GO) similarity and the CMA-ES (Covariance-Matrix-Adaptation Evolutionary-Strategy) optimizer. The method was tested in an IRB-approved study involving 10 patients undergoing cervical, thoracic, or lumbar spine surgery following preoperative MRI. Results The method successfully registered each preoperative MRI to intraoperative radiographs and maintained desirable properties of robustness against image content mismatch and large capture range. Robust registration performance was achieved with projection distance error (PDE) (median ± iqr) = 4.3 ± 2.6 mm (median ± iqr) and 0% failure rate. Segmentation accuracy for the continuous max-flow method yielded Dice coefficient = 88.1 ± 5.2, Accuracy = 90.6 ± 5.7, RMSE = 1.8 ± 0.6 mm, and contour affinity ratio (CAR) = 0.82 ± 0.08. Registration performance was found to be robust for segmentation methods exhibiting RMSE < 3 mm and CAR > 0.50. Conclusion The MR-LevelCheck method provides a potentially valuable extension to a previously developed decision support tool for spine surgery target localization by extending its utility to preoperative MRI while maintaining characteristics of accuracy and robustness.

show abstract

“…Thus, in our implementation, we constrain B 1 to be same across all the voxels of an image as well as across images. An automatic module for the nipple detection in [1] was used. The technique was tested over a population comprising 40 data sets, each from a different patient.…”

Section: Implementation and Experimentsmentioning

confidence: 99%

Expanded pharmacokinetic model for population studies in breast MRI

et al. 2008

View full text Add to dashboard Cite

We propose a new model for pharmacokinetic analysis based on the one proposed by Tofts. Our model both eliminates the need for estimating the Arterial Input Function (AIF) and normalizes analysis so that comparisons across patients can be performed. Previous methods have attempted to circumvent the AIF estimation by using the pharmacokinetic parameters of multiple reference regions (RR). Viewing anatomical structures as filters, pharmacokinetic analysis tells us that 'similar' structures will be similar filters. By cascading the inverse filter at a RR with the filter at the voxel being analyzed, we obtain a transfer function relating the concentration of a voxel to that of the RR. We show that this transfer function simplifies into a five-parameter nonlinear model with no reference to the AIF. These five parameters are combinations of the three parameters of the original model at the RR and the region of interest. Contrary to existing methods, ours does not require explicit estimation of the pharmacokinetic parameters of the RR. Also, cascading filters in the frequency domain allows us to manipulate more complex models, such as accounting for the vascular tracer component. We believe that our model can improve analysis across MR parameters because the analyzed and reference enhancement series are from the same image. Initial results are promising with the proposed model parameters exhibiting values that are more consistent across lesions in multiple patients. Additionally, our model can be applied to multiple voxels to estimate the original pharmacokinetic parameters as well as the AIF.

show abstract

Joint detection and localization of multiple anatomical landmarks through learning

Cited by 8 publications

References 0 publications

Automatic detection of anatomical landmarks on the knee joint using MRI data

Automatic detection of anatomical landmarks on the knee joint using MRI data

Registration of MRI to intraoperative radiographs for target localization in spinal interventions

Expanded pharmacokinetic model for population studies in breast MRI

Contact Info

Product

Resources

About