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首页> 外文会议>International Conference on Image Analysis and Recognition >Lesion Localization in Paediatric Epilepsy Using Patch-Based Convolutional Neural Network
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Lesion Localization in Paediatric Epilepsy Using Patch-Based Convolutional Neural Network

机译：基于补丁的卷积神经网络在小儿癫痫病灶定位中的应用

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Focal Cortical Dysplasia (FCD) is one of the most common causes of paediatric medically intractable focal epilepsy. In cases of medically resistant epilepsy, surgery is the best option to achieve a seizure-free condition. Pre-surgery lesion localization affects the surgery outcome. Lesion localization is done through examining the MRI for FCD features, but the MRI features of FCD can be subtle and may not be detected by visual inspection. Patients with epilepsy who have normal MRI are considered to have MRI-negative epilepsy. Recent advances in machine learning and deep learning hold the potential to improve the detection and localization of FCD without the need to conduct extensive pre-processing and FCD feature extraction. In this research, we apply Convolutional Neural Networks (CNNs) to classify FCD in children with focal epilepsy and localize the lesion. Two networks are presented here, the first network is applied on the whole-slice of the MR images, and the second network is taking smaller patches extracted from the slices of each MRI as input. The patch-wise model successfully classifies all healthy patients (13 out of 13), while 12 out of 13 cases are correctly identified by the whole-slice model. Using the patch-wise model, we identified the lesion in 17 out of 17 MR-positive subjects with coverage of 85% and for MR-negative subjects, we identify 11 out of 13 FCD subjects with lesion coverage of 66%. The findings indicate that convolutional neural network is a promising tool to objectively identify subtle lesions such as FCD in children with focal epilepsy.
机译：局灶性皮质发育不良（FCD）是小儿医学上难于治疗的局灶性癫痫的最常见原因之一。如果是抗药性癫痫，手术是实现无癫痫病的最佳选择。手术前病变的位置会影响手术效果。病变的定位是通过检查MRI的FCD特征来完成的，但FCD的MRI特征可能很微妙，可能无法通过目视检查检测到。 MRI正常的癫痫患者被视为MRI阴性癫痫。机器学习和深度学习的最新进展具有改进FCD的检测和定位的潜力，而无需进行大量的预处理和FCD特征提取。在这项研究中，我们应用卷积神经网络（CNN）对局灶性癫痫患儿的FCD分类并定位病灶。这里介绍了两个网络，第一个网络应用于MR图像的整个切片，第二个网络将从每个MRI切片中提取的较小色块用作输入。逐片模型成功地对所有健康患者进行了分类（13名患者中的13名），而全切片模型正确地识别了13名患者中的12名。使用逐块模型，我们在17名MR阳性受试者中识别出了17个病变，覆盖率达到85％，对于MR阴性受试者，我们从13例FCD受试者中识别出了11个病变，病变覆盖率达到66％。研究结果表明，卷积神经网络是一种客观识别局灶性癫痫儿童细微病变如FCD的有前途的工具。

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