Automated localization of Epileptic Focus Using Convolutional Neural Network

Feng, Cuixia; Zhao, Hulin; Zhang, Jun; Cheng, Zhibiao; Wen, Junhai

doi:10.1145/3378904.3378928

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Using a CNN, the authors of [ 12 ] aimed to classify and localize FCD lesions in the MRIs of patients with epilepsy via FCD simulation. For training, the authors acquired 636 MRIs from 89 healthy pediatric and adult subjects and locally added a bright region to these images to simulate the FCD lesion area.…”

Section: Methodsmentioning

confidence: 99%

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

Jiménez-Murillo,

Castro-Ospina,

Duque-Muñoz

et al. 2023

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%

“…In recent years, significant progress has been made in the development of automated techniques to aid in the detection and classification of FCD [ 11 , 12 , 13 ]. In this review, we aim to address this gap in the literature and present a comprehensive summary of current state-of-the-art approaches for automatic FCD detection.…”

Section: Introductionmentioning

confidence: 99%

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

Jiménez-Murillo,

Castro-Ospina,

Duque-Muñoz

et al. 2023

Sensors

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“…With the advent of Fully Convolutional Neural (FCN) networks and Deep Learning principles, image segmentation capabilities have significantly increased, making a notable contribution to the automated medical image segmentation process [28][29][30][31]. The author's in [32] proposed four-layer Convolutional Neural Network (CNN) model for FCD lesion detection. The goal of this approach is to identify dysplasia regions in MRI images through screening.…”

Section: Related Workmentioning

confidence: 99%

Efficient Detection of Focal Cortical Dysplasia Using Novel Two Fold Attention Mechanism

Gopika,

Kowshalya

2024

Preprint

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Focal Cortical Dysplasia (FCD) is a malformation of cortical development that leads to frequent pharmacological pediatric epilepsy. The only treatment for FCD is surgery, and the use of imaging techniques helps physicians plan the surgery. Magnetic Resonance Imaging (MRI) is an effective tool to predict the FCD lesion. The automatic segmentation of FCD lesions technique may help locate the lesions in the patient’s MRI slices. This research work proposes a novel two fold attention mechanism namely Hybrid Attention Gate_U shaped encoder decoder Network (HAG_UNET) model to detect the FCD accurately. The proposed model exploiting its novel attention mechanism is effective in accurate FCD lesion detection. The model tends to identify crucial features for FCD detection using the proposed novel two fold attention mechanism compared to state of art model. Experiment are done in python using standard datasets. A total of 11 subjects are used for the experiment. Metrics, namely IOU, precision, recall, and F1_score, are used for evaluation. Compared to UNET, the proposed model showed 5.89%, 4.92% and 3.15% improvement in terms of IOU, Recall and F1_score respectively. Compared to Attention_UNET, the proposed model showed 5.03%, 4.9%, 1.34% and 2.1% improvements in terms of IOU, Recall, Precision and F1_score respectively.

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