brain abnormality
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Author(s):  
Adhi Lakshmi ◽  
Thangadurai Arivoli ◽  
M. Pallikonda Rajasekaran ◽  
N. Bhuvaneshwary ◽  
S. Sathya

2021 ◽  
Author(s):  
Tina Khodadadifar ◽  
Zahra Soltaninejad ◽  
Amir Ebneabbasi ◽  
Claudia R. Eickhoff ◽  
Christian Sorg ◽  
...  

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Wonseok Whi ◽  
Hongyoon Choi ◽  
Jin Chul Paeng ◽  
Gi Jeong Cheon ◽  
Keon Wook Kang ◽  
...  

Abstract Background The whole brain is often covered in [18F]Fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) in oncology patients, but the covered brain abnormality is typically screened by visual interpretation without quantitative analysis in clinical practice. In this study, we aimed to develop a fully automated quantitative interpretation pipeline of brain volume from an oncology PET image. Method We retrospectively collected 500 oncologic [18F]FDG-PET scans for training and validation of the automated brain extractor. We trained the model for extracting brain volume with two manually drawn bounding boxes on maximal intensity projection images. ResNet-50, a 2-D convolutional neural network (CNN), was used for the model training. The brain volume was automatically extracted using the CNN model and spatially normalized. For validation of the trained model and an application of this automated analytic method, we enrolled 24 subjects with small cell lung cancer (SCLC) and performed voxel-wise two-sample T test for automatic detection of metastatic lesions. Result The deep learning-based brain extractor successfully identified the existence of whole-brain volume, with an accuracy of 98% for the validation set. The performance of extracting the brain measured by the intersection-over-union of 3-D bounding boxes was 72.9 ± 12.5% for the validation set. As an example of the application to automatically identify brain abnormality, this approach successfully identified the metastatic lesions in three of the four cases of SCLC patients with brain metastasis. Conclusion Based on the deep learning-based model, extraction of the brain volume from whole-body PET was successfully performed. We suggest this fully automated approach could be used for the quantitative analysis of brain metabolic patterns to identify abnormalities during clinical interpretation of oncologic PET studies.


Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2080
Author(s):  
Venkatesan Rajinikanth ◽  
Shabnam Mohamed Aslam ◽  
Seifedine Kadry

Ischemic stroke lesion (ISL) is a brain abnormality. Studies proved that early detection and treatment could reduce the disease impact. This research aimed to develop a deep learning (DL) framework to detect the ISL in multi-modality magnetic resonance image (MRI) slices. It proposed a convolutional neural network (CNN)-supported segmentation and classification to execute a consistent disease detection framework. The developed framework consisted of the following phases; (i) visual geometry group (VGG) developed VGG16 scheme supported SegNet (VGG-SegNet)-based ISL mining, (ii) handcrafted feature extraction, (iii) deep feature extraction using the chosen DL scheme, (iv) feature ranking and serial feature concatenation, and (v) classification using binary classifiers. Fivefold cross-validation was employed in this work, and the best feature was selected as the final result. The attained results were separately examined for (i) segmentation; (ii) deep-feature-based classification, and (iii) concatenated feature-based classification. The experimental investigation is presented using the Ischemic Stroke Lesion Segmentation (ISLES2015) database. The attained result confirms that the proposed ISL detection framework gives better segmentation and classification results. The VGG16 scheme helped to obtain a better result with deep features (accuracy > 97%) and concatenated features (accuracy > 98%).


2021 ◽  
Author(s):  
Jeevitha R ◽  
Selvaraj D

Brain tumours has huge heterogeneity and there is always a familiarity between normal and abnormal tissues and hence the extraction of tumour portions from normal images becomes persistent. In this paper, MRI brain tumor detection is performed from a brain images using Fuzzy C-means(FCM) algorithm and sebsequently Convolutional Neural Network(CNN) algorithm is employed. Here, firstly preprocessing step is performed by Skull Stripping algorithm followed by Segmentation process. Fuzzy C-means algorithm is used to segment the Cerebrospinal Fluid(CSF), Grey matter(GM) and White Matter(WM) from the database. The third part is to extract features to find whether the tumor is present or not, here eleven features are extracted like mean, entropy, S.D(Standard Deviation). The final part is the classification process done by Convolutional Neural Network(CNN) in which it is able to differentiate whether the input image is normal image or an abnormal image. Compared to other methods, here the values of the features extracted are higher for normal images than for abnormal Images and it is shown from the graphs drawn from the extracted features.


Author(s):  
Moshe Bronshtein ◽  
Michal Rosenberg Friedman ◽  
Ayala Gover ◽  
Ron Beloosesky ◽  
Nizar Khatib

2021 ◽  
pp. 101445
Author(s):  
Yi-Peng Han ◽  
Xingyao Tang ◽  
Min Han ◽  
Jinkui Yang ◽  
Marly Augusto Cardoso ◽  
...  

2021 ◽  
Author(s):  
Asmita Dixit

Abstract With lot happening in the field of Deep Learning, classification of brain tumor is still a matter of concern. Brain tumor segmentation and classification using MRI scans has achieved lot of interest in the area of medical imaging. The emphasis still lies on developing automatic computer-aided system for early predictions and diagnosis. MRI of brain Tumors not only varies in shape but sometimes gives less contrasted details also. In this paper, we present a FastAI based Transfer Learning tumor classification in which pre-trained model with segmented features classifies tumor based on its learning. The proposed model with the technique of Deep learning applies ResNet152 as base model to extract features from the MRI brain images. With certain changes in the last 3 layers of ResNet152, 97% accuracy in Dataset-253, 96% accuracy in Dataset-205 is achieved. Models such as Resnet50, VGG16, ResNet34 and Basic CNN is also evaluated. The model improved from ResNet152 has provided improved results. The observations suggest that usage of Transfer Learning is effective when the Dataset is limited. The prepared model is effective and can be collaborated in computer-aided brain MR images Tumor classification.


2021 ◽  
Author(s):  
Wonseok Whi ◽  
Hongyoon Choi ◽  
Jin Chul Paeng ◽  
Keon Wook Kang ◽  
Dong Soo Lee ◽  
...  

Abstract Background: The whole brain is often covered in [18F]Fluorodeoxyglucose Positron emission tomography ([18F]FDG-PET) in oncology patients, but the covered brain abnormality is typically screened by visual interpretation without quantitative analysis in clinical practice. In this study, we aimed to develop a fully automated quantitative interpretation pipeline of brain volume from an oncology PET image.Method: We retrospectively collected five hundred oncologic [18F]FDG-PET scans for training and validation of the automated brain extractor. We trained the model for extracting brain volume with two manually drawn bounding boxes on maximal intensity projection (MIP) images. ResNet-50, a convolutional neural network (CNN) was used for the model training. The brain volume was automatically extracted using the CNN model and spatially normalized. As an application of this automated analytic method, we enrolled twenty-four subjects with small cell lung cancer (SCLC) and performed voxelwise two-sample T-test for automatic detection of metastatic lesions.Result: The deep learning-based brain extractor successfully identified the existence of whole-brain volume, with the accuracy of 98% for the validation set. The performance of extracting the brain measured by the intersection-over-union (IOU) of 3-D bounding boxes was 72.9±12.5% for the validation set. As an example of the application to automatically identify brain abnormality, this approach successfully identified the metastatic lesions in three of the four cases of SCLC patients with brain metastasis. Conclusion: Based on the deep-learning based model, the brain volume was successfully extracted from whole-body FDG PET. We suggest this fully automated approach could be used for the quantitative analysis of brain metabolic pattern to identify abnormality during clinical interpretation of oncologic PET studies.


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