1,847 research outputs found
Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences
In this article, we present a graph-based method using a cubic template for
volumetric segmentation of vertebrae in magnetic resonance imaging (MRI)
acquisitions. The user can define the degree of deviation from a regular cube
via a smoothness value Delta. The Cube-Cut algorithm generates a directed graph
with two terminal nodes (s-t-network), where the nodes of the graph correspond
to a cubic-shaped subset of the image's voxels. The weightings of the graph's
terminal edges, which connect every node with a virtual source s or a virtual
sink t, represent the affinity of a voxel to the vertebra (source) and to the
background (sink). Furthermore, a set of infinite weighted and non-terminal
edges implements the smoothness term. After graph construction, a minimal
s-t-cut is calculated within polynomial computation time, which splits the
nodes into two disjoint units. Subsequently, the segmentation result is
determined out of the source-set. A quantitative evaluation of a C++
implementation of the algorithm resulted in an average Dice Similarity
Coefficient (DSC) of 81.33% and a running time of less than a minute.Comment: 23 figures, 2 tables, 43 references, PLoS ONE 9(4): e9338
Fully Automatic Segmentation of Lumbar Vertebrae from CT Images using Cascaded 3D Fully Convolutional Networks
We present a method to address the challenging problem of segmentation of
lumbar vertebrae from CT images acquired with varying fields of view. Our
method is based on cascaded 3D Fully Convolutional Networks (FCNs) consisting
of a localization FCN and a segmentation FCN. More specifically, in the first
step we train a regression 3D FCN (we call it "LocalizationNet") to find the
bounding box of the lumbar region. After that, a 3D U-net like FCN (we call it
"SegmentationNet") is then developed, which after training, can perform a
pixel-wise multi-class segmentation to map a cropped lumber region volumetric
data to its volume-wise labels. Evaluated on publicly available datasets, our
method achieved an average Dice coefficient of 95.77 0.81% and an average
symmetric surface distance of 0.37 0.06 mm.Comment: 5 pages and 5 figure
MRI of the axial skeleton in spondyloarthritis : the many faces of new bone formation
Spondyloarthritis has two hallmark features: active inflammation and structural lesions with new bone formation. MRI is well suited to assess active inflammation, but there is increasing interest in the role of structural lesions at MRI. Recent MRI studies have examined the established features of new bone formation and demonstrated some novel features which show diagnostic value and might even have potential as possible markers of disease progression. Although MRI is not the first imaging modality that comes into mind for assessment of bony changes, these features of new bone formation can be detected on MRI-if one knows how to recognize them. This review illustrates the MRI features of new bone formation and addresses possible pitfalls
Vertebral body segmentation with GrowCut: Initial experience, workflow and practical application
In this contribution, we used the GrowCut segmentation algorithm publicly
available in three-dimensional Slicer for three-dimensional segmentation of
vertebral bodies. To the best of our knowledge, this is the first time that the
GrowCut method has been studied for the usage of vertebral body segmentation.
In brief, we found that the GrowCut segmentation times were consistently less
than the manual segmentation times. Hence, GrowCut provides an alternative to a
manual slice-by-slice segmentation process.Comment: 10 page
Validity and reliability of computerized measurement of lumbar intervertebral disc height and volume from magnetic resonance images
BACKGROUND CONTEXT: Magnetic resonance (MR) examinations of morphologic characteristics of intervertebral discs (IVDs) have been used extensively for biomechanical studies and clinical investigations of the lumbar spine. Traditionally, the morphologic measurements have been performed using time- and expertise-intensive manual segmentation techniques not well suited for analyses of large-scale studies.
Three-dimensional Segmentation of the Scoliotic Spine from MRI using Unsupervised Volume-based MR-CT Synthesis
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 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 for simple thresholding-based segmentation of the
vertebral bones. However, this implicitly assumes the availability of paired
MR-CT data, which is rare, especially in the case of scoliotic patients. In
this paper, we present a completely unsupervised, fully three-dimensional (3D)
cross-modality synthesis method for segmenting scoliotic spines. A 3D CycleGAN
model is trained for an unpaired volume-to-volume translation across MR and CT
domains. Then, the Otsu thresholding algorithm is applied to the synthesized CT
volumes for easy segmentation of the vertebral bones. The resulting
segmentation is used to reconstruct a 3D model of the spine. We validate our
method on 28 scoliotic vertebrae in 3 patients by computing the
point-to-surface mean distance between the landmark points for each vertebra
obtained from pre-operative X-rays and the surface of the segmented vertebra.
Our study results in a mean error of 3.41 1.06 mm. Based on qualitative
and quantitative results, we conclude that our method is able to obtain a good
segmentation and 3D reconstruction of scoliotic spines, all after training from
unpaired data in an unsupervised manner.Comment: To appear in the Proceedings of the SPIE Medical Imaging Conference
2021, San Diego, CA. 9 pages, 4 figures in tota
3DBGrowth: volumetric vertebrae segmentation and reconstruction in magnetic resonance imaging
Segmentation of medical images is critical for making several processes of
analysis and classification more reliable. With the growing number of people
presenting back pain and related problems, the semi-automatic segmentation and
3D reconstruction of vertebral bodies became even more important to support
decision making. A 3D reconstruction allows a fast and objective analysis of
each vertebrae condition, which may play a major role in surgical planning and
evaluation of suitable treatments. In this paper, we propose 3DBGrowth, which
develops a 3D reconstruction over the efficient Balanced Growth method for 2D
images. We also take advantage of the slope coefficient from the annotation
time to reduce the total number of annotated slices, reducing the time spent on
manual annotation. We show experimental results on a representative dataset
with 17 MRI exams demonstrating that our approach significantly outperforms the
competitors and, on average, only 37% of the total slices with vertebral body
content must be annotated without losing performance/accuracy. Compared to the
state-of-the-art methods, we have achieved a Dice Score gain of over 5% with
comparable processing time. Moreover, 3DBGrowth works well with imprecise seed
points, which reduces the time spent on manual annotation by the specialist.Comment: This is a pre-print of an article published in Computer-Based Medical
Systems. The final authenticated version is available online at:
https://doi.org/10.1109/CBMS.2019.0009
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