Convolutional Neural Networks for Automatic Detection of Focal Cortical Dysplasia

Aliev, Ruslan; Kondrateva, Ekaterina; Sharaev, Maxim; Бронов, Олег; Marinets, Alexey; Субботин, С. А.; Bernstein, Alexander; Burnaev, Evgeny

doi:10.1007/978-3-030-71637-0_67

Cited by 4 publications

(6 citation statements)

References 13 publications

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“…Most approaches formulate the problem of FCD detection as a classification task. 13,16,24,34,35,40,46,69,70,[71][72][73][74] Input data range from raw MRI data to morphometric maps or surface features. They can be one-dimensional, that is, single voxels (or vertices if the input data are surface-based), or two-or three-dimensional images.…”

Section: Classificationmentioning

confidence: 99%

“…The output is thus generated on the voxel level, 24,46,[71][72][73] vertex level, 13,18,35,40,70 or patch level. 16,34,69,74 Figure 2 shows examples of the outputs from Multi-centre Epilepsy Lesion Detection (MELD), 18 Morphometric Analysis Program v2018 (MAP18), 24 and deepFCD 16 models.…”

Section: Classificationmentioning

confidence: 99%

“…Its output is a value between 0 and 1, akin to the probability for the input data belonging to a particular class, as shown in Figure 3A,B. For FCD detection, the possible classes are usually either “lesional” or “nonlesional.” Most approaches formulate the problem of FCD detection as a classification task 13,16,24,34,35,40,46,69,70,71–74 . Input data range from raw MRI data to morphometric maps or surface features.…”

Section: What Is Automatic Fcd “Detection”?mentioning

confidence: 99%

“…They can be one‐dimensional, that is, single voxels (or vertices if the input data are surface‐based), or two‐ or three‐dimensional images. In the two‐ and three‐dimensional cases, an algorithm often only operates on smaller image parts, so‐called “patches.” The output is thus generated on the voxel level, 24,46,71–73 vertex level, 13,18,35,40,70 or patch level 16,34,69,74 . Figure 2 shows examples of the outputs from Multi‐centre Epilepsy Lesion Detection (MELD), 18 Morphometric Analysis Program v2018 (MAP18), 24 and deepFCD 16 models.…”

Section: What Is Automatic Fcd “Detection”?mentioning

confidence: 99%

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Artificial intelligence for the detection of focal cortical dysplasia: Challenges in translating algorithms into clinical practice

et al. 2023

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Focal cortical dysplasias (FCDs) are malformations of cortical development and one of the most common pathologies causing pharmacoresistant focal epilepsy. Resective neurosurgery yields high success rates, especially if the full extent of the lesion is correctly identified and completely removed. The visual assessment of magnetic resonance imaging does not pinpoint the FCD in 30%–50% of cases, and half of all patients with FCD are not amenable to epilepsy surgery, partly because the FCD could not be sufficiently localized. Computational approaches to FCD detection are an active area of research, benefitting from advancements in computer vision. Automatic FCD detection is a significant challenge and one of the first clinical grounds where the application of artificial intelligence may translate into an advance for patients' health. The emergence of new methods from the combination of health and computer sciences creates novel challenges. Imaging data need to be organized into structured, well‐annotated datasets and combined with other clinical information, such as histopathological subtypes or neuroimaging characteristics. Algorithmic output, that is, model prediction, requires a technically correct evaluation with adequate metrics that are understandable and usable for clinicians. Publication of code and data is necessary to make research accessible and reproducible. This critical review introduces the field of automatic FCD detection, explaining underlying medical and technical concepts, highlighting its challenges and current limitations, and providing a perspective for a novel research environment.

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Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: What Is Automatic Fcd “Detection”?mentioning

confidence: 99%

Section: What Is Automatic Fcd “Detection”?mentioning

confidence: 99%

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Artificial intelligence for the detection of focal cortical dysplasia: Challenges in translating algorithms into clinical practice

et al. 2023

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“…Their proposal method gave a patient-wise recall value of 82.5, region-wise values of 48 for recall and 89 for precision, and pixel-wise values of 40.1 for recall, 80.69 for precision, and 52.47 for the Dice coefficient. Ruslan Aliev et al [ 57 ] automatically detected lesions of FCD using convolutional neural networks, and they proposed a new metric for the detection algorithm’s assessment. They applied their method on a data set with 15 labelled patients, and they obtained efficacious detection of FCD’s lesions in eleven out of fifteen subjects.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Cortical Thickness Changes in Epileptogenic Lesions Using Machine Learning

2023

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Epilepsy is a neurological disorder characterized by abnormal brain activity. Epileptic patients suffer from unpredictable seizures, which may cause a loss of awareness. Seizures are considered drug resistant if treatment does not affect success. This leads practitioners to calculate the cortical thickness to measure the distance between the brain’s white and grey matter surfaces at various locations to perform a surgical intervention. In this study, we introduce using machine learning as an approach to classify extracted measurements from T1-weighted magnetic resonance imaging. Data were collected from the epilepsy unit at King Abdulaziz University Hospital. We applied two trials to classify the extracted measurements from T1-weighted MRI for drug-resistant epilepsy and healthy control subjects. The preprocessing sequence on T1-weighted MRI images was performed using C++ through BrainSuite’s pipeline. The first trial was performed on seven different combinations of four commonly selected measurements. The best performance was achieved in Exp6 and Exp7, with 80.00% accuracy, 83.00% recall score, and 83.88% precision. It is noticeable that grey matter volume and white matter volume measurements are more significant than the cortical thickness measurement. The second trial applied four different machine learning classifiers after applying 10-fold cross-validation and principal component analysis on all extracted measurements as in the first trial based on the mentioned previous works. The K-nearest neighbours model outperformed the other machine learning classifiers with 97.11% accuracy, 75.00% recall score, and 75.00% precision.

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Convolutional Neural Networks Architecture for Processing Brain MRI Images

Zabudko

2024

2024 Conference of Young Researchers in Electrical and Electronic Engineering (ElCon)

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Convolutional Neural Networks for Automatic Detection of Focal Cortical Dysplasia

Cited by 4 publications

References 13 publications

Artificial intelligence for the detection of focal cortical dysplasia: Challenges in translating algorithms into clinical practice

Artificial intelligence for the detection of focal cortical dysplasia: Challenges in translating algorithms into clinical practice

Detecting Cortical Thickness Changes in Epileptogenic Lesions Using Machine Learning

Convolutional Neural Networks Architecture for Processing Brain MRI Images

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