Bone Segmentation
Bone Segmentation - Related Videos
06:45
Automated Joint Space Detection Improves Bone Segmentation Accuracy
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2025
The development of an automated joint space detection workflow enabled high-throughput segmentation of distinct murine hindpaw bones with >98% accuracy in wild-type animals. Flexible application to forepaws and paws with inflammatory-erosive arthritis was achieved, but with deprecated performance that warrants further optimization in future studies using publicly available data.
10:09
Adjustable Stiffness, External Fixator for the Rat Femur Osteotomy and Segmental Bone Defect Models
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Cited by 19 •
2014
One constraint of preclinical research in the field of bone repair is the lack of experimental control over the local mechanical environment within a healing bone lesion. We report the design and use of an external fixator for bone repair with the ability to change fixator stiffness in vivo.
04:32
Half-segmental Diaphyseal Bone Defect Model in Rats for Evaluating Bone Substitute Performance in Load-bearing Regions
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2025
This study established a rat femoral half-segmental defect model to evaluate the mechanical and osteogenic performance of bone substitute materials under load-bearing conditions without the use of internal or external fixation.
08:49
Models of Bone Metastasis
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Cited by 115 •
2012
Animal models are frequently utilized to study cancer metastasis to bone. In this protocol we will describe two common methods of tumor inoculation for bone metastasis studies and briefly describe some of the analyses utilized to monitor and quantify these models.
09:36
Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
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2018
We present a workflow for segmenting and quantifying trabecular bones for 2D and 3D images based on the bone's outer boundary using an ImageJ plugin. This approach is more efficient and accurate than the current manual hand-contouring approach, and provides layer-by-layer quantifications, which are not available in current commercial software.
FAQs
These image properties provide complementary evidence for assigning pixels or regions to bone. Intensity differences help distinguish bone from nearby structures, while boundaries indicate where tissue transitions occur. Spatial features add information about how regions are arranged within the anatomical image. Combining these cues supports more precise separation, which improves subsequent measurements of skeletal form and development.
Rule-based processing classifies bone regions using explicitly defined image criteria, whereas trained computational models learn classification patterns from example data. The distinction affects how segmentation decisions are produced and applied to anatomical images. Both approaches aim to isolate bone tissue, but their different decision mechanisms provide alternative strategies for analyzing skeletal structures during developmental studies.
Bone does not appear as an isolated measurement target; its location and organization within an anatomical image help identify the relevant region. Spatial information therefore complements intensity and boundary cues when classifying tissue. Preserving these relationships is important because later analyses may examine overall shape, structural organization, or three-dimensional arrangement rather than bone presence alone.
A typical workflow begins with anatomical images, followed by classification of bone regions using rule-based processing or trained computational models. The resulting segmented regions can then support measurements of growth, shape, mineralization, and structural change. Researchers may also use the separated regions to construct three-dimensional representations, linking image processing with quantitative analysis of skeletal development.
Segmented images can support quantitative comparisons of bone growth, shape, mineralization, and structural changes at different developmental stages. Because the same types of regions can be analyzed separately from surrounding anatomy, researchers can examine how skeletal features change over time. These measurements help turn anatomical images into data for evaluating developmental progression and variation.
By separating bone regions consistently, researchers can compare skeletal form and development between normal and altered conditions. The resulting measurements may reveal differences in growth, shape, mineralization, or structural organization. Such comparisons support phenotype quantification, allowing developmental changes to be evaluated systematically rather than relying only on qualitative inspection of anatomical images.