Three-dimensional segmentation of the scoliotic spine from MRI using unsupervised volume-based MR-CT synthesis

@inproceedings{Karthik2021ThreedimensionalSO,
  title={Three-dimensional segmentation of the scoliotic spine from MRI using unsupervised volume-based MR-CT synthesis},
  author={Enamundram Naga Karthik and Catherine Laporte and Farida Cheriet},
  booktitle={Medical Imaging},
  year={2021}
}
Vertebral bone segmentation from magnetic resonance (MR) images is a challenging task. Due to the inherent nature of the modality to emphasize soft tissues of the body, common thresholding algorithms are ineffective in detecting bones in MR images. On the other hand, it is relatively easier to segment bones from computed tomography (CT) images because of the high contrast between bones and the surrounding regions. For this reason, we perform a cross-modality synthesis between MR and CT domains… 

References

SHOWING 1-10 OF 20 REFERENCES

Automatic Segmentation of the Scoliotic Spine from Mr Images

TLDR
This paper presents a method based on convolutional neural networks to simultaneously segment VBs and IVDs in MRI data sets of AIS patients, a difficult problem which has not yet been adressed in the literature.

Semi-automatic segmentation of vertebral bodies in volumetric MR images using a statistical shape+pose model

TLDR
Quantitative and visual results demonstrate that the semi-automatic method for segmentation of vertebral bodies in volumetric MR images is promising for segmenting vertebrae in multi-slice MR images.

Segmentation of Vertebral Bodies in MR Images

TLDR
A fast and semi-automatic approach for spine segmentation in routine clinical MR images based on multiple-feature boundary classification and mesh inflation, and starts with a simple point-in-vertebra initialization.

Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models

TLDR
This is the first time IVDs have been automatically segmented from 3D volumetric scans and shape parameters obtained were used in preliminary analyses to accurately classify (100% sensitivity, 98.3% specificity) disc abnormalities associated with early degenerative changes.

Texture Analysis for Automatic Segmentation of Intervertebral Disks of Scoliotic Spines From MR Images

TLDR
A unified framework for automatic segmentation of intervertebral disks of scoliotic spines from different types of magnetic resonance (MR) image sequences is presented and it is suggested that the selected texture features and classification can contribute to solve the problem of oversegmentation inherent to existingautomatic segmentation methods.

Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences

TLDR
A graph-based method using a cubic template for volumetric segmentation of vertebrae in magnetic resonance imaging (MRI) acquisitions and an average Dice Similarity Coefficient (DSC) of 81.33% and a running time of less than a minute is presented.

Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data size

TLDR
The CycleGAN was successfully applied to unpaired CT and MR images of the head, these images do not have as much variation of intensity pairs as do images in the pelvic region due to the presence of joints and muscles.

Fully Automatic Localization and Segmentation of 3D Vertebral Bodies from CT/MR Images via a Learning-Based Method

TLDR
A learning-based, unified random forest regression and classification framework to tackle the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images is proposed.

Learning-Based Vertebra Detection and Iterative Normalized-Cut Segmentation for Spinal MRI

TLDR
The proposed vertebra detection and segmentation system is proved to be robust and accurate so that it can be used for advanced research and application on spinal MR images.

Assessment of the 3-D reconstruction and high-resolution geometrical modeling of the human skeletal trunk from 2-D radiographic images

TLDR
In vivo validation of a method for the three-dimensional (3-D) high-resolution modeling of the human spine, rib cage, and pelvis for the study of spinal deformities gives an overall accuracy of 3.3 /spl plusmn/ 3.8 mm, making it an adequate tool for clinical studies and mechanical analysis purposes.