Image Fusion Segmentation
Image Fusion Segmentation - Related Videos
06:10
Live Imaging of Mouse Secondary Palate Fusion
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Cited by 11 •
2017
Here, we present a protocol for live imaging of mouse secondary palate fusion using confocal microscopy. This protocol can be used in combination with a variety of fluorescent reporter mouse lines, and with pathway inhibitors for mechanistic insight. This protocol can be adapted for live imaging in other developmental systems.
04:06
Single-Molecule Imaging to Visualize Fusion Protein Condensates Assembly on DNA Curtains
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2025
This video describes a method to visualize the assembly of fusion protein condensates on DNA molecules at specific loci using single-molecule imaging. These condensates are formed due to LCD-LCD and LCD-DBD interactions, which allow for the recruitment of RNA polymerase II and enhanced gene transcription.
02:45
Nuclear Fusion
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2020
The process of converting very light nuclei into heavier nuclei is also accompanied by the conversion of mass into large amounts of energy, a process called fusion. The principal source of energy in the sun is a net fusion reaction in which four hydrogen nuclei fuse and ultimately produce one helium nucleus and two positrons. A helium nucleus has a mass that is 0.7% less than that of four hydrogen nuclei; this lost mass is converted into energy during the fusion. This reaction produces about...
09:19
Analysis of SNARE-mediated Membrane Fusion Using an Enzymatic Cell Fusion Assay
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2012
We have developed a cell fusion assay that quantifies SNARE-mediated membrane fusion events by activated expression of β-galactosidase.
07:38
Fluorescent Labeling of COS-7 Expressing SNAP-tag Fusion Proteins for Live Cell Imaging
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Cited by 8 •
2010
SNAP-tag and CLIP-tag protein labeling systems enable the specific, covalent attachment of molecules, including fluorescent dyes, to a protein of interest in live cells. Once cloned and expressed, the tagged protein can be used with a variety of substrates for numerous downstream applications without having to clone again.
FAQs
Spatial registration is the enabling step that places images from different modalities into a shared anatomical coordinate system. In Image Fusion Segmentation, this alignment allows structural and functional or metabolic information to refer to corresponding locations. Its quality directly affects whether a detected boundary or tissue label reflects the same anatomy, making registration essential before integrated interpretation.
These fusion levels combine information at different stages of analysis. Pixel-level fusion integrates image data directly, feature-level fusion combines extracted characteristics, and decision-level fusion merges outputs from separate analyses. The distinction determines how structural, functional, or metabolic signals contribute to segmentation and whether integration occurs before feature interpretation or after modality-specific decisions.
Structural modalities such as MRI or CT provide anatomical detail, while functional or metabolic information contributes complementary evidence about tissue behavior. Combining these signals can help distinguish regions that appear similar anatomically but differ in function. For segmentation, this complementary information may improve tissue characterization and support more precise identification of abnormal regions.
Results depend on how well the input images are spatially registered, how complementary signals are integrated, and which segmentation algorithm is applied. The selected fusion level also shapes the available information for classification. Together, these choices influence whether the resulting analysis can delineate organs, tissues, lesions, or other anatomical and abnormal regions consistently.
A typical workflow begins by acquiring complementary medical images, then aligning them through spatial registration. The registered data are integrated at the pixel, feature, or decision level, after which segmentation algorithms classify or delineate relevant structures. The resulting regions can then support interpretation, treatment planning, image-guided procedures, or quantitative assessment.
The approach is useful when one image type provides anatomical detail and another contributes functional or metabolic information. Medical teams may apply it to support diagnosis, characterize tissue, plan treatment, guide procedures, or quantify selected regions. Researchers also use it in multimodal analysis, where combining image sources can support clinical investigation and comparison.
Segmented fused images can identify and delineate organs, tissues, lesions, or other abnormal regions while retaining complementary information from multiple modalities. These outputs support quantitative assessment and may improve the interpretation of tissue differences. In personalized medicine and clinical research, such multimodal results provide a basis for more individualized analysis, planning, and evaluation.