Midsaggital Plane Detection in Magnetic Resonance Images Using Phase Congruency, Hessian Matrix and Symmetry Information: A Comparative Study

Freire, Paulo Guilherme de Lima; Silva, Bruno César Gregório da; Pinto, Carlos Henrique Villa; Moreira, Camilo Aparecido Ferri; Ferrari, Ricardo José

doi:10.1007/978-3-319-95162-1_17

“…This problem is circumvented by Ruppert et al [27] who proposed a new, simple, and fast metric known as average z-distance or z-score (in voxels), to measure MSP estimation error as a function of the distance between the two planes. This distance can be measured [27,33] by computing z coordinate from the plane equations, for the GT plane and the estimated plane, using each "x" and "y". It can be written as:…”

Section: Evaluation and Comparison On Synthetic Datasetsmentioning

confidence: 99%

“…The authors reported results on synthetic and real brain MRIs. A comparison study of three MSP extraction algorithms (symmetry-based [27], phase congruency [31], and Hessian-based [32]) is presented in [33]. In spite of the enormous variety of algorithms published on MSP extraction, there is no unanimity among the researchers about the best algorithm, due to the ambiguous longitudinal fissure lines, low-contrast brain images, mass effect, and absence of intensity standardization.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Automatic Midsagittal Plane Extraction in Brain MRI

Rehman

¹

,

Lee

²

2018

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In this paper, a fully automatic and computationally efficient midsagittal plane (MSP) extraction technique in brain magnetic resonance images (MRIs) has been proposed. Automatic detection of MSP in neuroimages can significantly aid in registration of medical images, asymmetric analysis, and alignment or tilt correction (recenter and reorientation) in brain MRIs. The parameters of MSP are estimated in two steps. In the first step, symmetric features and principal component analysis (PCA)-based technique is used to vertically align the bilateral symmetric axis of the brain. In the second step, PCA is used to achieve a set of parallel lines (principal axes) from the selected two-dimensional (2-D) elliptical slices of brain MRIs, followed by a plane fitting using orthogonal regression. The developed algorithm has been tested on 157 real T 1 -weighted brain MRI datasets including 14 cases from the patients with brain tumors. The presented algorithm is compared with a state-of-the-art approach based on bilateral symmetry maximization. Experimental results revealed that the proposed algorithm is fast (<1.04 s per MRI volume) and exhibits superior performance in terms of accuracy and precision (a mean z-distance of 0.336 voxels and a mean angle difference of 0.06).2 of 23 misalignment of brain MRIs. A general phenomenon in brain MRI scanning is that many neuroimaging scanners produce tilted and distorted brain images. The tilt of the head is not always detectable, due to many reasons such as the health conditions, immobility of patients, imprecision of the data calibration systems, and the inexperience of the technicians. Consequently, the slices of the brain MRIs are no more alike within the same orientation, at either the axial or coronal level [15]. Disoriented and misaligned brain MRIs can betray visual inspection and prevalently yield erroneous clinical perception [16]. In summary, assessment of brain MRIs for any anomaly based on cross-referencing of brain hemispheres (left and right), either by a human expert or computer-based software could be affected by false geometrical representation. Consequently, it is essential to correct the tilt and realign the brain MRIs data before further analysis.Manual misalignment correction is extremely time-consuming and laborious to perform on a huge scale. It also demands an urbane knowledge of brain anatomy. Therefore, it is neither sufficient nor efficient. Alignment or tilt correction of brain MRIs is tantamount to realigning the MSP with the center of the image matrix or image coordinate system [14]. If the MSP is computed precisely, the orientation problem of the MRI volume can be resolved. Thus, the tilt of the head volume can be assessed and adjusted. An ideal MSP can be defined as a virtual geometric plane passing through the interhemispheric fissure (IF) [17], about which the three-dimensional (3-D) anatomical structure of the brain (such as the ventricles, anterior/posterior commissures, corpus callosum, thalamus) exhibits maximum bilateral symmetry [18].Previously, ...

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“…Midline plane estimation algorithms in MRI are classified into two types: symmetry-based and shape-based. Symmetry-based approaches, also known as content-based, optimize a symmetry metric computed between candidate cerebral hemispheres until the optimal hemispheric separation is found [56]. Ruppert et al proposed an MSP algorithm based on bilateral symmetry maximization [57].…”

Section: Midline Estimation 31 Introductionmentioning

confidence: 99%

“…In this approach, symmetry is quantified using edge features and the optimal plane is sought through maximizing the correlation between the original image, and a flipped copy with respect to a candidate plane [57]. Shape-based algorithms make use of an initial estimation of the IF and use it as a landmark to fit a plane from points that lie in the IF region [56]. A classic shape-based algorithm by Brummer et.…”

Section: Midline Estimation 31 Introductionmentioning

confidence: 99%

Neurological Symmetry Analysis Applications in Flair MRI

Gibicar

2024

Preprint

0

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<p>Neurological diseases are one of the leading causes of disability in the global population. Fluid Attenuated Inversion Recovery (FLAIR) MRI is an important imaging modality for diagnosing neurological diseases such as Alzheimer's disease (AD) and cerebrovascular disease (CVD). Analyzing characteristics of neurological diseases using FLAIR MRI provide useful neuroimaging biomarkers for diagnosis, disease monitoring/progression, and treatment planning. There is growing evidence that these neurological diseases may affect cerebral symmetry. Thus, this thesis presents the development of automated segmentation tools to facilitate clinical symmetry analysis of neuroimaging biomarkers in AD and CVD cohorts. To do this, a fully unsupervised midline estimation technique was proposed, validated against existing works, and yielded the lowest average error across all metrics: Hausdorff distance (HD) = 0.32 ¬± 0.23, mean absolute difference (MAD) = 1.10 ¬± 0.38 mm and volume difference = 7.52 ¬± 5.40 and 5.35 ¬± 3.97 ml, for left and right hemispheres, respectively. Next, white matter lesion segmentation algorithms were explored and evaluated to later develop biomarkers for CVD subjects. Of all methods explored, the SC U-Net architecture was found to be the best algorithm for WML segmentation in terms of highest Dice similarity coefficient (DSC) over most dimensions (mean DSC = 0.71 over all volumes). These automated segmentation tools enabled downstream clinical symmetry analysis. Using the midline estimation tool, hemispheric asymmetry texture biomarkers of the normal-appearing brain matter (NABM) were analyzed in a dementia cohort, and textural asymmetry was found to increase with the progression of AD. Symmetry analysis was also performed in a CVD cohort, using both the midline and WML algorithms. Significant associations between WML asymmetry and carotid disease biomarkers were found. This work demonstrates that automated algorithms developed for FLAIR MRI can be used to measure asymmetrical properties of neurological diseases, that may aid in the detection of disease, treatment planning and clinical outcomes.</p>

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“…Midline plane estimation algorithms in MRI are classified into two types: symmetry-based and shape-based. Symmetry-based approaches, also known as content-based, optimize a symmetry metric computed between candidate cerebral hemispheres until the optimal hemispheric separation is found [56]. Ruppert et al proposed an MSP algorithm based on bilateral symmetry maximization [57].…”

Section: Midline Estimation 31 Introductionmentioning

confidence: 99%

Neurological Symmetry Analysis Applications in Flair MRI

Gibicar

2024

Preprint

0

View full text Add to dashboard Cite

<p>Neurological diseases are one of the leading causes of disability in the global population. Fluid Attenuated Inversion Recovery (FLAIR) MRI is an important imaging modality for diagnosing neurological diseases such as Alzheimer's disease (AD) and cerebrovascular disease (CVD). Analyzing characteristics of neurological diseases using FLAIR MRI provide useful neuroimaging biomarkers for diagnosis, disease monitoring/progression, and treatment planning. There is growing evidence that these neurological diseases may affect cerebral symmetry. Thus, this thesis presents the development of automated segmentation tools to facilitate clinical symmetry analysis of neuroimaging biomarkers in AD and CVD cohorts. To do this, a fully unsupervised midline estimation technique was proposed, validated against existing works, and yielded the lowest average error across all metrics: Hausdorff distance (HD) = 0.32 ¬± 0.23, mean absolute difference (MAD) = 1.10 ¬± 0.38 mm and volume difference = 7.52 ¬± 5.40 and 5.35 ¬± 3.97 ml, for left and right hemispheres, respectively. Next, white matter lesion segmentation algorithms were explored and evaluated to later develop biomarkers for CVD subjects. Of all methods explored, the SC U-Net architecture was found to be the best algorithm for WML segmentation in terms of highest Dice similarity coefficient (DSC) over most dimensions (mean DSC = 0.71 over all volumes). These automated segmentation tools enabled downstream clinical symmetry analysis. Using the midline estimation tool, hemispheric asymmetry texture biomarkers of the normal-appearing brain matter (NABM) were analyzed in a dementia cohort, and textural asymmetry was found to increase with the progression of AD. Symmetry analysis was also performed in a CVD cohort, using both the midline and WML algorithms. Significant associations between WML asymmetry and carotid disease biomarkers were found. This work demonstrates that automated algorithms developed for FLAIR MRI can be used to measure asymmetrical properties of neurological diseases, that may aid in the detection of disease, treatment planning and clinical outcomes.</p>

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Midsaggital Plane Detection in Magnetic Resonance Images Using Phase Congruency, Hessian Matrix and Symmetry Information: A Comparative Study

Cited by 5 publications

References 17 publications

An Efficient Automatic Midsagittal Plane Extraction in Brain MRI

An Efficient Automatic Midsagittal Plane Extraction in Brain MRI

Neurological Symmetry Analysis Applications in Flair MRI

Neurological Symmetry Analysis Applications in Flair MRI

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