Review of Brain Lesion Detection and Classification using Neuroimaging Analysis Techniques

2015 ◽  
Vol 74 (6) ◽  
Author(s):  
Norhashimah Mohd Saad ◽  
Syed Abdul Rahman Syed Abu Bakar ◽  
Ahmad Sobri Muda ◽  
Musa Mohd Mokji
Keyword(s):  
Differential Diagnosis ◽  
Magnetic Resonance ◽  
Brain Lesion ◽  
Brain Lesions ◽  
Lesion Detection ◽  
Subjective Time ◽  
Analysis Techniques ◽  
Indispensable Tool ◽  
Time Required ◽  
Processing Techniques

Neuroimaging plays an important role in the diagnosis brain lesions such as tumors, strokes and infections. Within this context, magnetic resonance diffusion-weighted imaging (DWI) is clinically recommended in the differential diagnosis of several brain lesions by providing detailed information regarding lesion based on the diffusion of water molecules. Conventionally, the differential diagnosis of brain lesions is performed visually by professional neuroradiologists during a highly subjective, time-consuming process. In response, computer-aided detection/diagnosis (CAD) has become a major topic of research and, in light of novel image processing techniques, has become a widespread, possibly indispensable tool for accurate diagnosis and reduce the time required. The objective of this review is to show the recent published techniques and state-of-the-art neuroimaging techniques for the human brain lesions. The review covers neuroimaging modalities, magnetic resonance imaging, DWI and analysis techniques for CAD in detecting and classifying of brain lesion. 

scholarly journals Diagnosing necrotic meningioma: a distinctive imaging pattern in diffusion MRI and MR spectroscopy

2016 ◽  
Vol 30 (1) ◽  
pp. 57-61
Author(s):  
Gal Ben-Arie ◽  
Yonatan Serlin ◽  
Sebastian Ivens ◽  
Mony Benifla ◽  
Emanuela Cagnano ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Magnetic Resonance ◽  
Diffusion Weighted Imaging ◽  
Diffusion Mri ◽  
Final Diagnosis ◽  
Brain Lesions ◽  
Malignant Neoplasms ◽  
Resonance Spectroscopy ◽  
Advanced Imaging ◽  
Work Up

The differential diagnosis of necrotic meningiomas includes brain abscess and malignant neoplasms. We report and discuss hereby the work-up of two patients diagnosed with necrotic meningioma using diffusion-weighted imaging, magnetic resonance spectroscopy, resective surgery, and histopathology. The purpose of the present article is to add to the scant literature on the use of advanced imaging modalities in the routine investigation of brain lesions and their utility in arriving at the final diagnosis.

scholarly journals Radius-optimized efficient template matching for lesion detection from brain images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Subhranil Koley ◽  
Pranab K. Dutta ◽  
Iman Aganj
Keyword(s):  
Template Matching ◽  
Linear Time ◽  
Mathematical Formulation ◽  
Brain Lesion ◽  
Three Dimensional ◽  
Brain Lesions ◽  
Lesion Detection ◽  
Prediction Algorithm ◽  
Mathematical Framework ◽  
Satisfactory Outcome

AbstractComputer-aided detection of brain lesions from volumetric magnetic resonance imaging (MRI) is in demand for fast and automatic diagnosis of neural diseases. The template-matching technique can provide satisfactory outcome for automatic localization of brain lesions; however, finding the optimal template size that maximizes similarity of the template and the lesion remains challenging. This increases the complexity of the algorithm and the requirement for computational resources, while processing large MRI volumes with three-dimensional (3D) templates. Hence, reducing the computational complexity of template matching is needed. In this paper, we first propose a mathematical framework for computing the normalized cross-correlation coefficient (NCCC) as the similarity measure between the MRI volume and approximated 3D Gaussian template with linear time complexity, $${\mathbf{\mathcal{O}}}\left( {{\varvec{a}}_{{{\varvec{max}}}} {\varvec{N}}} \right)$$ O a max N , as opposed to the conventional fast Fourier transform (FFT) based approach with the complexity $${\mathbf{\mathcal{O}}}\left( {{\varvec{a}}_{{{\varvec{max}}}} {\varvec{N}}\log {\varvec{N}}} \right)$$ O a max N log N , where $${\varvec{N}}$$ N is the number of voxels in the image and $${\varvec{a}}_{{{\varvec{max}}}}$$ a max is the number of tried template radii. We then propose a mathematical formulation to analytically estimate the optimal template radius for each voxel in the image and compute the NCCC with the location-dependent optimal radius, reducing the complexity to $${\mathbf{\mathcal{O}}}\left( {\varvec{N}} \right)$$ O N . We test our methods on one synthetic and two real multiple-sclerosis databases, and compare their performances in lesion detection with FFT and a state-of-the-art lesion prediction algorithm. We demonstrate through our experiments the efficiency of the proposed methods for brain lesion detection and their comparable performance with existing techniques.

scholarly journals Magnetic resonance imaging in the diagnosis of non-tumorous multifocal brain lesions mimicking multiple sclerosis

2020 ◽  
Vol 49 ◽  
Author(s):  
I. A. Krotenkova ◽  
V. V. Bryukhov ◽  
R. N. Konovalov ◽  
M. N. Zakharova ◽  
M. V. Krotenkova
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
Differential Diagnosis ◽  
Magnetic Resonance ◽  
Correct Diagnosis ◽  
Laboratory Data ◽  
Clinical Manifestations ◽  
Brain Lesions ◽  
Resonance Imaging ◽  
Vascular Abnormalities

The diagnosis of multiple sclerosis (MS) is quite challenging due to its variable clinical manifestations and lack of a definitive test. Magnetic resonance imaging (MRI) is one of the tools to confirm the diagnosis and also helps in differential diagnosis with other disorders and in exclusion of MS-mimicking diseases. In this article, based on the analysis of clinical cases, we discuss the differential diagnosis of MS with the following non-tumorous multifocal brain lesions: vascular abnormalities caused by hypoxia and ischemia, cerebral autosomal dominant angiopathy with subcortical infarctions and leukoencephalopathy, Susac syndrome, primary angiitis of the central nervous system, and neurosarcoidosis. We present both MRI criteria for MS and disorders that have similar MRI signs, and additional clinical and laboratory data that is essential for correct diagnosis.

scholarly journals Radius-Optimized Efficient Template Matching for Lesion Detection from Brain Images

2020 ◽  
Author(s):  
Subhranil Koley ◽  
Pranab K. Dutta ◽  
Iman Aganj
Keyword(s):  
Template Matching ◽  
Linear Time ◽  
Mathematical Formulation ◽  
Brain Lesion ◽  
Three Dimensional ◽  
Brain Lesions ◽  
Lesion Detection ◽  
Prediction Algorithm ◽  
Mathematical Framework ◽  
Satisfactory Outcome

AbstractComputer-aided detection of brain lesions from volumetric magnetic resonance imaging (MRI) is in demand for fast and automatic diagnosis of neural diseases. The template-matching technique can provide satisfactory outcome for automatic localization of brain lesions; however, finding the optimal template size that maximizes similarity of the template and the lesion remains challenging. This increases the complexity of the algorithm and the requirement for computational resources, while processing large MRI volumes with three-dimensional (3D) templates. Hence, reducing the computational complexity of template matching is needed. In this paper, we first propose a mathematical framework for computing the normalized cross-correlation coefficient (NCCC) as the similarity measure between the MRI volume and approximated 3D Gaussian template with linear time complexity, 𝒪(amaxN), as opposed to the conventional fast Fourier transform (FFT) based approach with the complexity 𝒪(amaxNlog N), where N is the number of voxels in the image and amax is the number of tried template radii. We then propose a mathematical formulation to analytically estimate the optimal template radius for each voxel in the image and compute the NCCC with the location-dependent optimal radius, reducing the complexity to 𝒪(N). We test our methods on one synthetic and two real multiple-sclerosis databases, and compare its performance in lesion detection with FFT and a state-of-the-art lesion prediction algorithm. We demonstrate through our experiments the efficiency of the proposed method for brain lesion detection and its comparable performance with existing techniques.

scholarly journals Contrast-Enhanced Black Blood MRI Sequence Is Superior to Conventional T1 Sequence in Automated Detection of Brain Metastases by Convolutional Neural Networks

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1016
Author(s):  
Jonathan Kottlors ◽  
Simon Geissen ◽  
Hannah Jendreizik ◽  
Nils Große Hokamp ◽  
Philipp Fervers ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Metastases ◽  
Convolutional Neural Networks ◽  
Brain Lesion ◽  
Brain Lesions ◽  
Automated Detection ◽  
Lesion Detection ◽  
Black Blood ◽  
Mri Imaging ◽  
Contrast Enhanced

Background: in magnetic resonance imaging (MRI), automated detection of brain metastases with convolutional neural networks (CNN) represents an extraordinary challenge due to small lesions sometimes posing as brain vessels as well as other confounders. Literature reporting high false positive rates when using conventional contrast enhanced (CE) T1 sequences questions their usefulness in clinical routine. CE black blood (BB) sequences may overcome these limitations by suppressing contrast-enhanced structures, thus facilitating lesion detection. This study compared CNN performance in conventional CE T1 and BB sequences and tested for objective improvement of brain lesion detection. Methods: we included a subgroup of 127 consecutive patients, receiving both CE T1 and BB sequences, referred for MRI concerning metastatic spread to the brain. A pretrained CNN was retrained with a customized monolayer classifier using either T1 or BB scans of brain lesions. Results: CE T1 imaging-based training resulted in an internal validation accuracy of 85.5% vs. 92.3% in BB imaging (p < 0.01). In holdout validation analysis, T1 image-based prediction presented poor specificity and sensitivity with an AUC of 0.53 compared to 0.87 in BB-imaging-based prediction. Conclusions: detection of brain lesions with CNN, BB-MRI imaging represents a highly effective input type when compared to conventional CE T1-MRI imaging. Use of BB-MRI can overcome the current limitations for automated brain lesion detection and the objectively excellent performance of our CNN suggests routine usage of BB sequences for radiological analysis.

The role of magnetic resonance imaging in differential diagnosis of adnexal masses during pregnancy

GYNECOLOGY ◽  
2014 ◽  
Vol 16 (1) ◽  
pp. 69-72
Author(s):  
S.A. Martynov ◽  
◽  
L.V. Adamyan ◽  
E.A. Kulabukhova ◽  
P.V. Uchevatkina ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Differential Diagnosis ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Adnexal Masses

scholarly journals CSF proteome in multiple sclerosis subtypes related to brain lesion transcriptomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maria L. Elkjaer ◽  
Arkadiusz Nawrocki ◽  
Tim Kacprowski ◽  
Pernille Lassen ◽  
Anja Hviid Simonsen ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Protein Phosphatase ◽  
Brain Lesion ◽  
Brain Lesions ◽  
Cns Diseases ◽  
Secondary Progressive ◽  
Apolipoprotein A ◽  
Proteomic Approach ◽  
Apolipoprotein C ◽  
Altered Proteins

AbstractTo identify markers in the CSF of multiple sclerosis (MS) subtypes, we used a two-step proteomic approach: (i) Discovery proteomics compared 169 pooled CSF from MS subtypes and inflammatory/degenerative CNS diseases (NMO spectrum and Alzheimer disease) and healthy controls. (ii) Next, 299 proteins selected by comprehensive statistics were quantified in 170 individual CSF samples. (iii) Genes of the identified proteins were also screened among transcripts in 73 MS brain lesions compared to 25 control brains. F-test based feature selection resulted in 8 proteins differentiating the MS subtypes, and secondary progressive (SP)MS was the most different also from controls. Genes of 7 out these 8 proteins were present in MS brain lesions: GOLM was significantly differentially expressed in active, chronic active, inactive and remyelinating lesions, FRZB in active and chronic active lesions, and SELENBP1 in inactive lesions. Volcano maps of normalized proteins in the different disease groups also indicated the highest amount of altered proteins in SPMS. Apolipoprotein C-I, apolipoprotein A-II, augurin, receptor-type tyrosine-protein phosphatase gamma, and trypsin-1 were upregulated in the CSF of MS subtypes compared to controls. This CSF profile and associated brain lesion spectrum highlight non-inflammatory mechanisms in differentiating CNS diseases and MS subtypes and the uniqueness of SPMS.

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