Robust, atlas-free, automatic segmentation of brain MRI in health and disease

Selvaganesan, Kartiga; Whitehead, Emily; DeAlwis, Paba M.; Schindler, Matthew K.; Inati, Souheil; Saad, Ziad S.; Ohayon, Joan; Cortese, Irene; Smith, Bryan; Jacobson, Steven; Nath, Avindra; Reich, Daniel S.; Inati, Sara K.; Nair, Govind

doi:10.1016/j.heliyon.2019.e01226

Cited by 17 publications

(23 citation statements)

References 39 publications

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“…These images can then be used for medical diagnosis [ 4 ]. Since MRI benefits from the magnetic technology with no significant reported harm, it is widely used for the analysis of brain diseases because this method of imaging harms none of the very sensitive brain tissues [ 4 , 5 ]. MRIs can also be employed to diagnose MS. Analyzing these images, physicians can determine what part of the brain has been damaged and to what extent the disease has spread.…”

Section: Introductionmentioning

confidence: 99%

“…Physicians mostly disagree on distinguishing MS edges from healthy tissues. Many physicians attempt to consult each other to detect MS edges so that they can properly deal with the disease or its spread in operation or possible treatment [ 5 ]. In other words, physicians attempt to separate MS areas from brain tissue with the least possible errors.…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm

Iswisi

Karan

Rahebi

2021

BioMed Research International

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The damaged areas of brain tissues can be extracted by using segmentation methods, most of which are based on the integration of machine learning and data mining techniques. An important segmentation method is to utilize clustering techniques, especially the fuzzy C-means (FCM) clustering technique, which is sufficiently accurate and not overly sensitive to imaging noise. Therefore, the FCM technique is appropriate for multiple sclerosis diagnosis, although the optimal selection of cluster centers can affect segmentation. They are difficult to select because this is an NP-hard problem. In this study, the Harris Hawks optimization (HHO) algorithm was used for the optimal selection of cluster centers in segmentation and FCM algorithms. The HHO is more accurate than other conventional algorithms such as the genetic algorithm and particle swarm optimization. In the proposed method, every membership matrix is assumed as a hawk or an HHO member. The next step is to generate a population of hawks or membership matrices, the most optimal of which is selected to find the optimal cluster centers to decrease the multiple sclerosis clustering error. According to the tests conducted on a number of brain MRIs, the proposed method outperformed the FCM clustering and other techniques such as the k -NN algorithm, support vector machine, and hybrid data mining methods in accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

[Retracted] Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm

Iswisi

Karan

Rahebi

2021

BioMed Research International

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“…Manual volumetric analysis of the SN is time-consuming and subjective. Therefore, we applied a novel semi-automatic local statistics signature-based segmentation method [37,38] to quantify the volume of SN.…”

Section: Introductionmentioning

confidence: 99%

Semi-Automatic Signature-Based Segmentation Method for Quantification of Neuromelanin in Substantia Nigra

et al. 2019

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In Parkinson’s disease (PD), there is a reduction of neuromelanin (NM) in the substantia nigra (SN). Manual quantification of the NM volume in the SN is unpractical and time-consuming; therefore, we aimed to quantify NM in the SN with a novel semi-automatic segmentation method. Twenty patients with PD and twelve healthy subjects (HC) were included in this study. T1-weighted spectral pre-saturation with inversion recovery (SPIR) images were acquired on a 3T scanner. Manual and semi-automatic atlas-free local statistics signature-based segmentations measured the surface and volume of SN, respectively. Midbrain volume (MV) was calculated to normalize the data. Receiver operating characteristic (ROC) analysis was performed to determine the sensitivity and specificity of both methods. PD patients had significantly lower SN mean surface (37.7 ± 8.0 vs. 56.9 ± 6.6 mm2) and volume (235.1 ± 45.4 vs. 382.9 ± 100.5 mm3) than HC. After normalization with MV, the difference remained significant. For surface, sensitivity and specificity were 91.7 and 95 percent, respectively. For volume, sensitivity and specificity were 91.7 and 90 percent, respectively. Manual and semi-automatic segmentation methods of the SN reliably distinguished between PD patients and HC. ROC analysis shows the high sensitivity and specificity of both methods.

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“…Segmenting MR images containing abnormal tissue has been studied previously in the field of MS, where WM lesions can have a significant impact on GM and WM volumes [22][23][24]. In MS, the error in volume measurements mainly arises from misclassification of WM lesions as GM or CSF [25].…”

Section: Introductionmentioning

confidence: 99%

Overestimation of grey matter atrophy in glioblastoma patients following radio(chemo)therapy

Gommlich

Raschke

Petr

et al. 2021

Magn Reson Mater Phy

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Objective Brain atrophy has the potential to become a biomarker for severity of radiation-induced side-effects. Particularly brain tumour patients can show great MRI signal changes over time caused by e.g. oedema, tumour progress or necrosis. The goal of this study was to investigate if such changes affect the segmentation accuracy of normal appearing brain and thus influence longitudinal volumetric measurements. Materials and methods T1-weighted MR images of 52 glioblastoma patients with unilateral tumours acquired before and three months after the end of radio(chemo)therapy were analysed. GM and WM volumes in the contralateral hemisphere were compared between segmenting the whole brain (full) and the contralateral hemisphere only (cl) with SPM and FSL. Relative GM and WM volumes were compared using paired t tests and correlated with the corresponding mean dose in GM and WM, respectively. Results Mean GM atrophy was significantly higher for full segmentation compared to cl segmentation when using SPM (mean ± std: ΔVGM,full = − 3.1% ± 3.7%, ΔVGM,cl = − 1.6% ± 2.7%; p < 0.001, d = 0.62). GM atrophy was significantly correlated with the mean GM dose with the SPM cl segmentation (r = − 0.4, p = 0.004), FSL full segmentation (r = − 0.4, p = 0.004) and FSL cl segmentation (r = -0.35, p = 0.012) but not with the SPM full segmentation (r = − 0.23, p = 0.1). Conclusions For accurate normal tissue volume measurements in brain tumour patients using SPM, abnormal tissue needs to be masked prior to segmentation, however, this is not necessary when using FSL.

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Robust, atlas-free, automatic segmentation of brain MRI in health and disease

Cited by 17 publications

References 39 publications

[Retracted] Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm

[Retracted] Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm

Semi-Automatic Signature-Based Segmentation Method for Quantification of Neuromelanin in Substantia Nigra

Overestimation of grey matter atrophy in glioblastoma patients following radio(chemo)therapy

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