Brain Tumor Segmentation
Brain Tumor Segmentation - Related Videos
04:25
Manual Segmentation of the Human Choroid Plexus Using Brain MRI
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Cited by 13 •
2023
Despite the crucial role of the choroid plexus in the brain, neuroimaging studies of this structure are scarce due to the lack of reliable automated segmentation tools. The present protocol aims to ensure gold-standard manual segmentation of the choroid plexus that can inform future neuroimaging studies.
10:39
A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment
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Cited by 11 •
2022
The intravital imaging method described here utilizes collagen second harmonic generation and endogenous fluorescence from the metabolic co-factor NAD(P)H to non-invasively segment an unlabeled tumor microenvironment into tumor, stromal, and vascular compartments for in-depth analysis of 4D intravital images.
03:45
Tumor Treating Fields Application in Human Brain Tumor Models: A Non-invasive Technique to Deliver Alternating Electric Fields to Human Brain for Treating Brain Tumor
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2023
This video demonstrates the application of Tumor Treating Fields or TTFields in human brain tumor models. This non-invasive technique delivers low-intensity alternating electric fields directly to the brain, resulting in inhibition of cell division and subsequent apoptosis.
10:13
A Brain Tumor/Organotypic Slice Co-culture System for Studying Tumor Microenvironment and Targeted Drug Therapies
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Cited by 29 •
2015
Many types of human brain tumors are localized to specific regions within the brain and are difficult to grow in culture. This protocol addresses the role of tumor microenvironment and investigates new drug treatments by analyzing fluorescent primary brain tumor cells growing in an organotypic mouse brain slice.
02:46
Establishing a Brain Tumor in an Organotypic Slice Co-culture Model
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2025
This video demonstrates the development of a 3D co-culture system. Mouse brain slices are placed on a membrane, and fluorescently labeled human brain tumor cells are allowed to grow on it. Once the system is ready, it is stained for imaging.
FAQs
Segmentation algorithms can evaluate image intensity, texture, contrast, and spatial patterns together rather than relying on a single visual feature. These characteristics provide complementary information about how tissue appears and where it is located relative to surrounding brain structures. Combining them supports more precise classification of image regions and helps produce a clinically useful tumor boundary or volume.
The analysis may classify individual pixels in image slices or three-dimensional voxels throughout an MRI volume. Voxel-based processing represents the tumor as a spatial structure rather than as separate, unrelated regions on each slice. This distinction matters when estimating tumor volume and preserving the boundary’s continuity across the imaged brain.
Both approaches can classify image regions, but they represent different engineering strategies for interpreting MRI data. Image-processing algorithms use defined operations on features such as intensity, texture, contrast, or spatial pattern, whereas machine-learning models perform classification through a learned computational approach. The resulting output can be evaluated as a tumor boundary or volume.
Accuracy affects the reliability of quantitative measurements derived from the segmented region, including the estimated tumor volume. More accurate boundaries can support treatment planning, surgical guidance, and comparison of tumor changes over time. In engineering research, accuracy also matters when assessing computer-assisted diagnostic systems or comparing automated results with manual annotations.
A typical workflow begins with MRI data and analyzes relevant image characteristics, including intensity, texture, contrast, and spatial patterns. An algorithm or machine-learning model then classifies pixels or three-dimensional voxels. The classified regions are converted into a tumor boundary or volume, which can subsequently support measurement, planning, guidance, or monitoring.
Automated segmentation is useful when researchers need quantitative tumor measurements or must process data more efficiently than manual annotation allows. The generated boundary or volume can provide structured information for analysis while reducing the time required to outline regions by hand. Manual annotations remain relevant as a comparison point when evaluating the performance of computer-assisted diagnostic systems.
The output provides a measurable representation of tumor extent that can be incorporated into treatment planning and surgical guidance. It also supports tumor characterization by converting image information into a defined region or volume. These outputs help biomedical engineers develop systems that connect medical imaging analysis with clinical decision-support and procedure-related tasks.
Applying the analysis to imaging obtained at different times can produce tumor boundaries or volumes for longitudinal comparison. Changes in these measurements may help characterize how the tumor’s imaged extent varies during monitoring. In biomedical engineering, this use connects segmentation with systems designed to organize quantitative imaging information across time rather than relying only on a single scan.