Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network

Abstract Background: Accurate classification of focal cortical dysplasia (FCD) has been challenging due to the problematic visual detection in magnetic resonance imaging (MRI). Hence, recently, there has been a necessity for employing new techniques to solve the problem.Methods: MRI data were collected from 58 participants (30 subjects with FCD type II and 28 normal subjects). Morphological and intensity-based characteristics were calculated for each cortical level and then the performance of the three classifiers: decision tree (DT), support vector machine (SVM) and artificial neural network (ANN) was evaluated.Results: Metrics for evaluating classification methods, sensitivity, specificity and accuracy for the DT were 96.7%, 100% and 98.6%, respectively; It was 95%, 100% and 97.9% for the SVM and 96.7%, 100% and 98.6% for the ANN.Conclusion: Comparison of the performance of the three classifications used in this study showed that all three have excellent performance in specificity, but in terms of classification sensitivity and accuracy, the artificial neural network method has worked better.

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Automatic Localization and Discrete Volume Measurements of Hippocampi From MRI Data Using a Convolutional Neural Network

IEEE Access ◽

10.1109/access.2020.2994388 ◽

2020 ◽

Vol 8 ◽

pp. 91725-91739

Author(s):

Abol Basher ◽

Byeong C. Kim ◽

Kun Ho Lee ◽

Ho Yub Jung

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Volume Measurements ◽

Automatic Localization

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Automatic localization of cephalometric landmarks based on convolutional neural network

American Journal of Orthodontics and Dentofacial Orthopedics ◽

10.1016/j.ajodo.2021.09.012 ◽

2021 ◽

Author(s):

Jie Yao ◽

Wei Zeng ◽

Tao He ◽

Shanluo Zhou ◽

Yi Zhang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Automatic Localization

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Automatic Detection of Focal Cortical Dysplasia Type II in MRI: Is the Application of Surface-Based Morphometry and Machine Learning Promising?

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.608285 ◽

2021 ◽

Vol 15 ◽

Author(s):

Zohreh Ganji ◽

Mohsen Aghaee Hakak ◽

Seyed Amir Zamanpour ◽

Hoda Zare

Keyword(s):

Neural Network ◽

Machine Learning ◽

Focal Cortical Dysplasia ◽

Diagnostic System ◽

Cortical Dysplasia ◽

Type Ii ◽

Magnetic Resonance Imaging Mri ◽

Drug Resistant Epilepsy ◽

Sensitivity Specificity ◽

Postsurgical Outcomes

Background and ObjectivesFocal cortical dysplasia (FCD) is a type of malformations of cortical development and one of the leading causes of drug-resistant epilepsy. Postoperative results improve the diagnosis of lesions on structural MRIs. Advances in quantitative algorithms have increased the identification of FCD lesions. However, due to significant differences in size, shape, and location of the lesion in different patients and a big deal of time for the objective diagnosis of lesion as well as the dependence of individual interpretation, sensitive approaches are required to address the challenge of lesion diagnosis. In this research, a FCD computer-aided diagnostic system to improve existing methods is presented.MethodsMagnetic resonance imaging (MRI) data were collected from 58 participants (30 with histologically confirmed FCD type II and 28 without a record of any neurological prognosis). Morphological and intensity-based features were calculated for each cortical surface and inserted into an artificial neural network. Statistical examinations evaluated classifier efficiency.ResultsNeural network evaluation metrics—sensitivity, specificity, and accuracy—were 96.7, 100, and 98.6%, respectively. Furthermore, the accuracy of the classifier for the detection of the lobe and hemisphere of the brain, where the FCD lesion is located, was 84.2 and 77.3%, respectively.ConclusionAnalyzing surface-based features by automated machine learning can give a quantitative and objective diagnosis of FCD lesions in presurgical assessment and improve postsurgical outcomes.

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“Within a minute” detection of focal cortical dysplasia

Neuroradiology ◽

10.1007/s00234-021-02823-7 ◽

2021 ◽

Author(s):

Horst Urbach ◽

Marcel Heers ◽

Dirk-Matthias Altenmueller ◽

Andreas Schulze-Bonhage ◽

Anke Maren Staack ◽

...

Keyword(s):

Neural Network ◽

False Positive ◽

Focal Cortical Dysplasia ◽

Area Under The Curve ◽

Cortical Dysplasia ◽

Receiver Operating Curve ◽

Healthy Controls ◽

Periventricular Nodular Heterotopia ◽

Probability Map ◽

Nodular Heterotopia

Abstract Purpose To evaluate a MRI postprocessing tool for the enhanced and rapid detection of focal cortical dysplasia (FCD). Methods MP2RAGE sequences of 40 consecutive, so far MRI-negative patients and of 32 healthy controls were morphometrically analyzed to highlight typical FCD features. The resulting morphometric maps served as input for an artificial neural network generating a FCD probability map. The FCD probability map was inversely normalized, co-registered to the MPRAGE2 sequence, and re-transferred into the PACS system. Co-registered images were scrolled through “within a minute” to determine whether a FCD was present or not. Results Fifteen FCD, three subcortical band heterotopias (SBH), and one periventricular nodular heterotopia were identified. Of those, four FCD and one SBH were only detected by MRI postprocessing while one FCD and one focal polymicrogryia were missed, respectively. False-positive results occurred in 21 patients and 22 healthy controls. However, true positive cluster volumes were significantly larger than volumes of false-positive clusters (p < 0.001). The area under the curve of the receiver operating curve was 0.851 with a cut-off volume of 0.05 ml best indicating a FCD. Conclusion Automated MRI postprocessing and presentation of co-registered output maps in the PACS allowed for rapid (i.e., “within a minute”) identification of FCDs in our clinical setting. The presence of false-positive findings currently requires a careful comparison of postprocessing results with conventional MR images but may be reduced in the future using a neural network better adapted to MP2RAGE images.

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Using a recurrent neural network with S2 characteristics, efficient identification of localised cortical dysplasia

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-212463 ◽

2021 ◽

pp. 1-14

Author(s):

A. Karthika ◽

R. Subramanian ◽

S. Karthik

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Short Term Memory ◽

Focal Cortical Dysplasia ◽

Cortical Dysplasia ◽

Support Vector ◽

Sensitivity Rate ◽

Long Short Term Memory ◽

Artificial Neural Network Ann ◽

The Brain

Focal cortical dysplasia (FCD) is an inborn anomaly in brain growth and morphological deformation in lesions of the brain which induces focal seizures. Neurosurgical therapies were performed for the detection of FCD. Furthermore, it can be overcome through the presurgical evaluation of epilepsy. The surgical result is attained basically through the output of the presurgical output. In preprocessing the process of increasing true positives with the decrease in false negatives occurs which results in an effective outcome. MRI (Magnetic Resonance Imaging) outputs are efficient to predict the FCD lesions through T1- MPRAGE and T2- FLAIR efficient output can be obtained. In our proposed work we extract the S2 features through the testing of T1, T2 images. Using RNN-LSTM (Recurrent neural network-Long short-term memory) test images were trained and the FCD lesions were segmented. The output of our work is compared with the proposed work yields better results compared to the existing system such as artificial neural network (ANN), support vector machine (SVM), and convolution neural network (CNN). This approach obtained an accuracy rate of 0.195% (ANN), 0.20% (SVM), 0.14% (CNN), specificity rate of 0.23% (ANN), 0.15% (SVM), 0.13% (CNN) and sensitivity rate of 0.22% (ANN), 0.14% (SVM), 0.08% (CNN) respectively in comparison with RNN-LSTM.

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