Automatic brain tumour detection and neovasculature assessment with multiseries MRI analysis

Szwarc, Pawel; Kawa, Jacek; Rudzki, Marcin; Piętka, Ewa

doi:10.1016/j.compmedimag.2015.06.002

Cited by 31 publications

(19 citation statements)

References 48 publications

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“…The addition of these algorithms to human interpretation could potentially lead to greater sensitivity and improved accuracy of intracranial metastases characterization, based both on standard postcontrast exams as well as advanced physiologic imaging such as perfusion-related acquisitions (Ambrosini et al, 2010; Yang et al, 2013; Szwarc et al, 2015). …”

Section: Enhancing Detection Of Small Metastasesmentioning

confidence: 99%

Brain metastases: neuroimaging

Pope

2018

Handbook of Clinical Neurology

150

130

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Magnetic resonance imaging (MRI) is the cornerstone for evaluating patients with brain masses such as primary and metastatic tumors. Important challenges in effectively detecting and diagnosing brain metastases and in accurately characterizing their subsequent response to treatment remain. These difficulties include discriminating metastases from potential mimics such as primary brain tumors and infection, detecting small metastases, and differentiating treatment response from tumor recurrence and progression. Optimal patient management could be benefited by improved and well-validated prognostic and predictive imaging markers, as well as early response markers to identify successful treatment prior to changes in tumor size. To address these fundamental needs, newer MRI techniques including diffusion and perfusion imaging, MR spectroscopy, and positron emission tomography (PET) tracers beyond traditionally used 18-fluorodeoxyglucose are the subject of extensive ongoing investigations, with several promising avenues of added value already identified. These newer techniques provide a wealth of physiologic and metabolic information that may supplement standard MR evaluation, by providing the ability to monitor and characterize cellularity, angiogenesis, perfusion, pH, hypoxia, metabolite concentrations, and other critical features of malignancy. This chapter reviews standard and advanced imaging of brain metastases provided by computed tomography, MRI, and amino acid PET, focusing on potential biomarkers that can serve as problem-solving tools in the clinical management of patients with brain metastases.

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Section: Enhancing Detection Of Small Metastasesmentioning

confidence: 99%

Brain metastases: neuroimaging

Pope

2018

Handbook of Clinical Neurology

150

130

View full text Add to dashboard Cite

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“…Due to a large volume of this medical data, manual interpretation is rarely possible. Therefore, robust and optimized protocols, also called computer aided diagnosis systems are necessary for patient's treatment planning …”

Section: Introductionmentioning

confidence: 99%

“…Therefore, robust and optimized protocols, also called computer aided diagnosis systems are necessary for patient's treatment planning. 5 A human brain is susceptible to several abnormalities including cerebrovascular diseases, degenerative diseases, inflammatory diseases, and neoplastic (tumor) diseases. 6 Being noninvasive medical examination, MRI is currently the best examination for assessment of brain disorders.…”

Section: Introductionmentioning

confidence: 99%

Computer aided diagnosis of brain abnormalities using texture analysis of MRI images

Gilanie

Bajwa

Waraich

et al. 2019

Int J Imaging Syst Tech

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The drive of this study is to develop a robust system. A method to classify brain magnetic resonance imaging (MRI) image into brain‐related disease groups and tumor types has been proposed. The proposed method employed Gabor texture, statistical features, and support vector machine. Brain MRI images have been classified into normal, cerebrovascular, degenerative, inflammatory, and neoplastic. The proposed system has been trained on a complete dataset of Brain Atlas‐Harvard Medical School. Further, to achieve robustness, a dataset developed locally has been used. Extraordinary results on different orientations, sequences of both of these datasets as per accuracy (up to 99.6%), sensitivity (up to 100%), specificity (up to 100%), precision (up to 100%), and AUC value (up to 1.0) have been achieved. The tumorous slices are further classified into primary or secondary tumor as well as their further types as glioma, sarcoma, meningioma, bronchogenic carcinoma, and adenocarcinoma, which could not be possible to determine without biopsy, otherwise.

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“…It is radiologically documented that variation in age, weather, culture, and ethnicity, results in a change of medical traits of patients and is the source of variability and multiformity of medical data. Therefore, robust and optimized protocols, called computer aided diagnosis (CAD) systems are always decisive in a subject's successful treatment planning . However, if CAD systems trained on one dataset are deployed at remote location/site, may or may not be able to address the issue of variability and multiformity.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, robust and optimized protocols, called computer aided diagnosis (CAD) systems are always decisive in a subject's successful treatment planning. 9 However, if CAD systems trained on one dataset are deployed at remote location/site, may or may not be able to address the issue of variability and multiformity.…”

Section: Introductionmentioning

confidence: 99%

Automated and reliable brain radiology with texture analysis of magnetic resonance imaging and cross datasets validation

Gilanie

Bajwa

Waraich

et al. 2019

Int J Imaging Syst Tech

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Reliable brain tumor radiology is one of the serious mortality issues of medical hospitals and on priority of healthcare departments. In this research, the presence of brain tumor and its type (if exists) is automatically diagnosed from magnetic resonance imaging (MRI). The first step is most important where suitable parameters from Gabor texture analysis are extracted and then classified with a support vector machine. The drive of this research activity is to verify robustness of the proposed model on cross datasets, so that it could deal with variability and multiformity present in MRI data. Further to this, the developed approach is able to deploy as a real application in the local environment. Therefore, once a model has been trained and tested on an openly available benchmarked dataset, it is retested on a different dataset acquired from a local source. Standard evaluation measures, that is, accuracy, specificity, sensitivity, precision, and AUC‐values have been used to evaluate the robustness of the proposed method. It has been established that the proposed method has the ability to deal with multiformity, variability, and local medical traits present in brain MRI data.

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Automatic brain tumour detection and neovasculature assessment with multiseries MRI analysis

Cited by 31 publications

References 48 publications

Brain metastases: neuroimaging

Brain metastases: neuroimaging

Computer aided diagnosis of brain abnormalities using texture analysis of MRI images

Automated and reliable brain radiology with texture analysis of magnetic resonance imaging and cross datasets validation

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