Adaptive segmentation of cerebrovascular tree in time-of-flight magnetic resonance angiography

Hao, Jiaxin; Li, M. L.; Tang, Fan

doi:10.1007/s11517-007-0244-4

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…Different appearance models were proposed for time-of-flight (TOF) MRA [6][7][8][9], phase contrast (PC) MRA [10], and CT-angiography (CTA) [11]. These models estimate intensity distributions for vessels and other tissues, often by fitting a Finite Mixture Model (FMM) to the dataset's histogram.…”

Section: A Previous Workmentioning

confidence: 99%

Automatic SWI Venography Segmentation Using Conditional Random Fields

Bériault

Xiao

Collins

et al. 2015

IEEE Trans. Med. Imaging

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Susceptibility-weighted imaging (SWI) venography can produce detailed venous contrast and complement arterial dominated MR angiography (MRA) techniques. However, these dense reversed-contrast SWI venograms pose new segmentation challenges. We present an automatic method for whole-brain venous blood segmentation in SWI using Conditional Random Fields (CRF). The CRF model combines different first and second order potentials. First-order association potentials are modeled as the composite of an appearance potential, a Hessian-based shape potential and a non-linear location potential. Second-order interaction potentials are modeled using an auto-logistic (smoothing) potential and a data-dependent (edge) potential. Minimal post-processing is used for excluding voxels outside the brain parenchyma and visualizing the surface vessels. The CRF model is trained and validated using 30 SWI venograms acquired within a population of deep brain stimulation (DBS) patients (age range [Formula: see text] years). Results demonstrate robust and consistent segmentation in deep and sub-cortical regions (median kappa = 0.84 and 0.82), as well as in challenging mid-sagittal and surface regions (median kappa = 0.81 and 0.83) regions. Overall, this CRF model produces high-quality segmentation of SWI venous vasculature that finds applications in DBS for minimizing hemorrhagic risks and other surgical and non-surgical applications.

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Section: A Previous Workmentioning

confidence: 99%

Automatic SWI Venography Segmentation Using Conditional Random Fields

Bériault

Xiao

Collins

et al. 2015

IEEE Trans. Med. Imaging

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“…CNN incorporates numerous First-and second-order potentials. SWI venogram segmentation is robust, comprehensive, and entirely automated by combining appearance, shape, position, auto-logistic (Ising) interaction, and datadependent interaction potentials (Hao et al, 2008).…”

Section: Proposed Systemmentioning

confidence: 99%

Design and Implementation of Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

Mohanabharathi

2024

JoIPIR

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Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that uses MRI technology to quantify brain activity by detecting changes in blood flow and applying the Blood-oxygen-level dependent (BOLD) contrast. Current neuroscience research suggests a modular brain structure. To understand the complex patterns of interaction between brain areas. The proposed system makes use of the CNN algorithm, which is an efficient method for dividing segmentation. A brain area is defined as a collection of subjects who exhibit a similar pattern of interaction between their brain areas. A thorough experimental examination utilizing benchmark data demonstrates that our technique is both effective and efficient. Two real-world fMRI studies found that the rifleman technique has the potential to improve comprehension of both normal brain activity and the abnormalities associated with psychiatric diseases. That is, mental diseases are typically defined by How an individual feels, acts, thinks, or perceives events. This is typically linked to specific areas or functions of the brain or the rest of the nervous system. This is unacceptable for a variety of time series sectors. As a result, we provide the statistical region merging approach, which is applied to picture segmentation. The method evaluates the values inside a regional range and combines them using the merging criteria, resulting in a smaller list. . As a result, we gather additional information and compare the findings to those already in the database. As a result, we decide if a person is normal or aberrant, as well as whether they are unwell.

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