Segmentation of focal cortical dysplasia lesions from magnetic resonance images using 3D convolutional neural networks

Mutual information-based image registration is a common method for placement registration in radiotherapy. This paper uses the ART (Algebraic Reconstruction Technology) algorithm to reconstruct the electronic portal imaging device (EPID) field images obtained during low-dose radiotherapy. The reconstructed results MVCT and planned KVCT are used for maximum mutual information registration. The mutual information registration algorithm proposed in this paper combines gray level compression and area search, which greatly improves the speed of maximum mutual information registration. The feasibility of the algorithm is verified by experiments, which are divided into eight groups of different phantom placement and one group of phantom deformation to explore the low-dose rigid registration ability and the ability to resist cross-period lesion changes of the algorithm.

Fast Registration Algorithm of MVCT and KVCT Based on Mutual Information

Focal cortical dysplasia (FCD) is a congenital brain malformation that is closely associated with epilepsy. Early and accurate diagnosis is essential for effectively treating and managing FCD. Magnetic resonance imaging (MRI)-one of the most commonly used non-invasive neuroimaging methods for evaluating the structure of the brain-is often implemented along with automatic methods to diagnose FCD. In this review, we define three categories for FCD identification based on MRI: visual, semi-automatic, and fully automatic methods. By conducting a systematic review following the PRISMA statement, we identified 65 relevant papers that have contributed to our understanding of automatic FCD identification techniques. The results of this review present a comprehensive overview of the current state-of-the-art in the field of automatic FCD identification and highlight the progress made and challenges ahead in developing reliable, efficient methods for automatic FCD diagnosis using MRI images. Future developments in this area will most likely lead to the integration of these automatic identification tools into medical image-viewing software, providing neurologists and radiologists with enhanced diagnostic capabilities. Moreover, new MRI sequences and higher-field-strength scanners will offer improved resolution and anatomical detail for precise FCD characterization. This review summarizes the current state of automatic FCD identification, thereby contributing to a deeper understanding and the advancement of FCD diagnosis and management. 

Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review

Focal Cortical Dysplasia (FCD) is a neurodevelopmental disorder characterized by abnormal neuronal migration, differentiation, and maturation, resulting in a range of clinical symptoms including drug-resistant epilepsy. Accurate and timely diagnosis of FCD is essential for effective treatment and management of patients with this condition. In recent years, there have been significant advances in neuroimaging and machine learning techniques, enabling automated detection of FCD lesions with increasing accuracy and efficiency. In this survey article, we provide an overview of the current state-of-the-art in computational techniques for the automated detection of FCD lesions using neuroimaging. We first introduce some common imaging findings that radiologists look for in FCD lesions, then review the automatic detection techniques studied in the last decade. These techniques have shown promising results in detecting and localizing FCD lesions, improving diagnostic accuracy and reducing misdiagnoses, diagnosis time, and cost of care. We suggest that the use of these types of quantitative image analysis tools and algorithms can play a key role in improving the overall management and outcome of patients with FCD. However, further studies are needed to validate and optimize the performance of these techniques in clinical practice. In summary, the combination of advanced neuroimaging and computational techniques has the potential to revolutionize the diagnosis and treatment of FCD, leading to better outcomes for patients with this condition. 

Application of Neuroimaging in Diagnosis of Focal Cortical Dysplasia: A Survey of Computational Techniques

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.

Using a Recurrent Neural Network with S2 Characteristics, Efficient Identification of Localised Cortical Dysplasia

Focal cortical dysplasia (FCD) is one of the most common causes of intractable epilepsy. The automatic localization of magnetic resonance (MR) images of epileptic lesions caused by FCD can be performed by using the convolutional neural network (CNN) technology in the field of artificial intelligence, which is helpful for doctors to better diagnose. This study trained a four-layer CNN, including the 8630 learning parameters and three convolutional layers followed by the pooling layer and one full connection layer, and finally, output an image as a disease probability value. The network can classify the MR images with the disease or not, and the accuracy of the final classification of the new data is 92.45%. On the basis of correct classification, the accurate rate of FCD lesions localization can reach 92.86% by using this network.

Automated localization of Epileptic Focus Using Convolutional Neural Network

The registration of two-dimensional MV electronic portal imaging device (EPID) images and digital reconstruction radiograph (DRR) images has been widely used for the setup error correction of radiotherapy, the approaches estimate the 3D transformation is not very accurate. The purpose of this paper is to verify the feasibility of a new setup error estimation method that registers 3D planning CT image and 3D image that is reconstructed based on EPID. EPID images were acquired and used to reconstruct the MVCT by Algebraic Reconstruction Technique (ART) algorithm. The reconstructed image and the planning CT were registered by maximizing the mutual information (MI) between two 3D images. The registration error is less than 3mm, which is suitable for clinical implementation. The study demonstrated that the 3D/3D registration method proposed for the setup error correction of radiotherapy is feasible.

Registration between MVCT reconstructed from EPID and kVCT

Focal cortical dysplasia (FCD) is the most common factor leading to intractable epilepsy. It is helpful for doctors to automatically detect the FCD lesion before the operation. In this study, two methods to detect and locate the lesion are proposed. The first method is based on the symmetrical characteristics of the brain image, and can approximately detect abnormal areas caused by FCD in Magnetic Resonance (MR) images. The second method involves detecting local highlighted areas in MR images based on the expectation-maximization (EM) algorithm. The two methods were applied to 15 specific MR images of 9 epileptic patients. The recognition accuracy of the symmetrical feature algorithm and EM algorithm was 80 % (12/15) and 100% (15/15), respectively, and the detection accuracy of the combination of two algorithms is 80%. The symmetrical feature algorithm can be applied to any of the axial or coronal MR images of the modality, and the EM algorithm is suitable for detecting the local hyperintense of the MR image. The two methods were able to detect lesion areas in MR images and achieve desirable results.

Detection of Focal Cortical Dysplasia lesions in MR images

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Papers

Automated localization of Epileptic Focus Using Convolutional Neural Network

Registration between MVCT reconstructed from EPID and kVCT

Detection of Focal Cortical Dysplasia lesions in MR images