Multi-parametric (ADC/PWI/T2-w) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme

Kazerooni, Anahita Fathi; Mohseni, Meysam; Rezaei, Sahar; Bakhshandehpour, Gholamreza; Rad, Hamidreza Saligheh

doi:10.1007/s10334-014-0442-7

Cited by 31 publications

(33 citation statements)

References 34 publications

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“…Therefore, further research will focus on automation by incorporating prior knowledge, e.g. tissue probability maps based on an image atlas (Juan-Albarracín et al, 2015), feature-specific knowledge (Kazerooni et al, 2015) or user input (Menze et al, 2015). …”

Section: Discussionmentioning

confidence: 99%

“…Due to the lack of manually annotated ground truth, unsupervised methods depend more strongly on the incorporation of additional prior knowledge, e.g. by imposing spatial coherence (Nie et al, 2009) or feature-specific knowledge (Kazerooni et al, 2015), to achieve a valid tissue segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…FCM takes into account the MRI feature overlap between tissue classes, assigning fuzzy membership values to different tissue types. FCM was introduced for brain tumor segmentation by Phillips et al (1995) and was later combined with knowledge-based techniques for improved performance (Kazerooni et al, 2015, Fletcher-Heath et al, 2001). Several studies have applied finite mixture modelling for unsupervised tumor segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…As extensive MP-MRI datasets might be more prone to variations in the acquisition protocol, unsupervised methods benefit from not requiring a uniform training dataset. Kazerooni et al are among the first to explore unsupervised classification methods for MP-MRI based glioma segmentation (Kazerooni et al, 2015). They employed spatial fuzzy C-means clustering to a combination of cMRI, DWI and PWI data, followed by a region growing step.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI

Sauwen

Acou

Cauter

et al. 2016

NeuroImage: Clinical

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Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI

Sauwen

Acou

Cauter

et al. 2016

NeuroImage: Clinical

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“…Multiple methods for (semi)automatic tumor segmentation based on MRI images have been described for different tumors, including breast [2][3][4][5][6], prostate [7,8], brain [9][10][11], and head and neck [12] tumors. A variety of segmentation algorithms were used in these studies, including volume growing, threshold-based methods,…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of subcutaneous mouse tumors by multiparametric MR analysis based on endogenous contrast

Hectors

Jacobs

Strijkers

et al. 2014

Magn Reson Mater Phy

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Development and Validation of a Deep Learning Model for Brain Tumor Diagnosis and Classification Using Magnetic Resonance Imaging

et al. 2022

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IMPORTANCEDeep learning may be able to use patient magnetic resonance imaging (MRI) data to aid in brain tumor classification and diagnosis. OBJECTIVE To develop and clinically validate a deep learning system for automated identification and classification of 18 types of brain tumors from patient MRI data. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study was conducted using MRI data collected between 2000 and 2019 from 37 871 patients. A deep learning system for segmentation and classification of 18 types of intracranial tumors based on T1-and T2-weighted images and T2 contrast MRI sequences was developed and tested. The diagnostic accuracy of the system was tested using 1 internal and 3 external independent data sets. The clinical value of the system was assessed by comparing the tumor diagnostic accuracy of neuroradiologists with vs without assistance of the proposed system using a separate internal test data set. Data were analyzed from March 2019 through February 2020. MAIN OUTCOMES AND MEASURES Changes in neuroradiologist clinical diagnostic accuracy inbrain MRI scans with vs without the deep learning system were evaluated. RESULTS A deep learning system was trained among 37 871 patients (mean [SD] age, 41.6 [11.4] years; 18 519 women [48.9%]). It achieved a mean area under the receiver operating characteristic curve of 0.92 (95% CI, 0.84-0.99) on 1339 patients from 4 centers' data sets in diagnosis and classification of 18 types of tumors. Higher outcomes were found compared with neuroradiologists for accuracy and sensitivity and similar outcomes for specificity (for 300 patients in the Tiantan

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Multi-parametric (ADC/PWI/T2-w) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme

Abstract: The proposed MRI-based multi-parametric segmentation approach has the potential to accurately segment tumorous regions, leading to an efficient design of the pre-surgical treatment planning.

Cited by 31 publications

References 34 publications

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI

Automatic segmentation of subcutaneous mouse tumors by multiparametric MR analysis based on endogenous contrast

Development and Validation of a Deep Learning Model for Brain Tumor Diagnosis and Classification Using Magnetic Resonance Imaging

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