Registration of a Statistical Shape Model of the Lumbar Spine to 3D Ultrasound Images

Khallaghi, Siavash; Mousavi, Parvin; Gong, Ren Hui; Gill, Sean; Boisvert, Jonathan; Fichtinger, Gabor; Pichora, David R.; Borschneck, Dan; Abolmaesumi, Purang

doi:10.1007/978-3-642-15745-5_9

Cited by 29 publications

(16 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When the convex hull is computed, the size of this overextension is reduced but it is not entirely removed. To address this problem, the use of a shape template for the acoustic shadow, designed from the input of several experts (Tran and Rohling, 2010) or a statistical atlas (Khallaghi et al, 2010;Hacihaliloglu et al, 2014), could be a solution.…”

Section: Segmentation Resultsmentioning

confidence: 99%

“…registration) between freehand 3D ultrasound data and a generic 3D model (or, more generally, a statistical atlas) of the spine surface, using methods proposed in the context of image-guided epidural anesthesia. Khallaghi et al (2010) optimize the similarity between the ultrasound data and synthetic ultrasound images generated from the 3D spine model using a simulation method. Hacihaliloglu et al (2014) propose instead to match bone surfaces from the model to bone surfaces enhanced using local phase tensor features in the ultrasound image.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Segmentation of the spinous process and its acoustic shadow in vertebral ultrasound images

Berton

Cheriet

Marie-Claude

et al. 2016

Computers in Biology and Medicine

View full text Add to dashboard Cite

Spinal ultrasound imaging is emerging as a low-cost, radiation-free alternative to conventional X-ray imaging for the clinical follow-up of patients with scoliosis. Currently, deformity measurement relies almost entirely on manual identification of key vertebral landmarks. However, the interpretation of vertebral ultrasound images is challenging, primarily because acoustic waves are entirely reflected by bone. To alleviate this problem, we propose an algorithm to segment these images into three regions: the spinous process, its acoustic shadow and other tissues. This method consists, first, in the extraction of several image features and the selection of the most relevant ones for the discrimination of the three regions. Then, using this set of features and a LDA classifier, each pixel of the image is classified as belonging to one of the three * Corresponding author Email addresses: florian.berton@polymtl.ca (Florian Berton), farida.cheriet@polymtl.ca (Farida Cheriet), marie-claude_miron@ssss.gouv.qc.ca regions. Finally, the image is segmented by regularizing the pixel-wise classification results to account for some geometrical properties of vertebrae. The feature set was first validated by analyzing the classification results across a learning database. The database contained 107 vertebral ultrasound images acquired with convex and linear probes. Classification rates of 84%, 92% and 91% were achieved for the spinous process, the acoustic shadow and other tissues, respectively. Dice similarity coefficients of 0.72 and 0.88 were obtained respectively for the spinous process and acoustic shadow, confirming that the proposed method accurately segments the spinous process and its acoustic shadow in vertebral ultrasound images. Furthermore, the centroid of the automatically segmented spinous process was located at an average distance of 0.38 mm from that of the manually labeled spinous process, which is on the order of image resolution. This suggests that the proposed method is a promising tool for the measurement of the Spinous Process Angle and, more generally, for assisting ultrasound-based assessment of scoliosis progression.

show abstract

Section: Segmentation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Segmentation of the spinous process and its acoustic shadow in vertebral ultrasound images

Berton

Cheriet

Marie-Claude

et al. 2016

Computers in Biology and Medicine

View full text Add to dashboard Cite

show abstract

“…26 Spine volumes have seen increasing use for procedures such as facet joint injection. 27 Ten healthy human participants (five male, five female, ages ranging from 21 to 26) were recruited. The liver, right kidney, and spine are scanned using freehand ultrasound in each participant Fig.…”

Section: Image Acquisitionmentioning

confidence: 99%

Hole filling with oriented sticks in ultrasound volume reconstruction

et al. 2015

Self Cite

View full text Add to dashboard Cite

Abstract. Volumes reconstructed from tracked planar ultrasound images often contain regions where no information was recorded. Existing interpolation methods introduce image artifacts and tend to be slow in filling large missing regions. Our goal was to develop a computationally efficient method that fills missing regions while adequately preserving image features. We use directional sticks to interpolate between pairs of known opposing voxels in nearby images. We tested our method on 30 volumetric ultrasound scans acquired from human subjects, and compared its performance to that of other published hole-filling methods. Reconstruction accuracy, fidelity, and time were improved compared with other methods.

show abstract

“…[39]. More recently, SSM-based shape reconstruction using 3D US data was also applied to the lumbar spine for needle insertion purpose [40].…”

Section: Ultrasound Imagingmentioning

confidence: 99%

A Survey on Statistical Modeling and Machine Learning Approaches to Computer Assisted Medical Intervention: Intraoperative Anatomy Modeling and Optimization of Interventional Procedures

Morooka

Nakamoto

Sato

2013

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYThis paper reviews methods for computer assisted medical intervention using statistical models and machine learning technologies, which would be particularly useful for representing prior information of anatomical shape, motion, and deformation to extrapolate intraoperative sparse data as well as surgeons' expertise and pathology to optimize interventions. Firstly, we present a review of methods for recovery of static anatomical structures by only using intraoperative data without any preoperative patient-specific information. Then, methods for recovery of intraoperative motion and deformation are reviewed by combining intraoperative sparse data with preoperative patient-specific stationary data, which is followed by a survey of articles which incorporated biomechanics. Furthermore, the articles are reviewed which addressed the used of statistical models for optimization of interventions. Finally, we conclude the survey by describing the future perspective.

show abstract

Registration of a Statistical Shape Model of the Lumbar Spine to 3D Ultrasound Images

Cited by 29 publications

References 21 publications

Segmentation of the spinous process and its acoustic shadow in vertebral ultrasound images

Segmentation of the spinous process and its acoustic shadow in vertebral ultrasound images

Hole filling with oriented sticks in ultrasound volume reconstruction

A Survey on Statistical Modeling and Machine Learning Approaches to Computer Assisted Medical Intervention: Intraoperative Anatomy Modeling and Optimization of Interventional Procedures

Contact Info

Product

Resources

About