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首页> 外文会议>IEEE International Conference on Image Processing >A NEW DEEP-LEARNING APPROACH FOR EARLY DETECTION OF SHAPE VARIATIONS IN AUTISM USING STRUCTURAL MRI
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A NEW DEEP-LEARNING APPROACH FOR EARLY DETECTION OF SHAPE VARIATIONS IN AUTISM USING STRUCTURAL MRI

机译：一种新的深度学习方法，用于利用结构MRI早期检测自闭症形状变化

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This paper introduces a novel shape-based computer-aided diagnosis (CAD) system using magnetic resonance (MR) brain images for autism diagnosis at different life stages. To improve the classification robustness, the system fuses the shape features extracted from the cerebral cortex (Cx) and cerebral white matter (CWM). Fusion is conducted based on the findings suggesting that Cx changes in autism are related to CWM abnormalities. The CAD system starts with segmenting Cx and CWM using a 3D joint model that combines intensity, shape, and spatial information. Then, Spherical Harmonic (SPHARM) is applied to the re-constructed meshes of Cx to derive 4 metrics for each mesh point; normal curvature, mean curvature, gaussian curvature, and Cx surface reconstruction error. To analyze the CWM shape, distance maps of its gyri are computed and three more shape features are extracted for these gyri. Finally, all the extracted shape features are fed to a multi-level deep network for feature fusion and diagnosis. The CAD system has been evaluated using subjects from the ABIDE database (8-12.8 years), achieving an accuracy of 93%, and from NDAR/Pitt database (16-51 years), achieving an accuracy of 97%. Also in order to show the capability of the system for early diagnosis, it has been tested on NDAR/IBIS database for infants, resulting in an accuracy of 85%. These initial results on the 3 databases hold the promise of efficient autism diagnosis.
机译：本文介绍了一种新颖的基于形状的计算机辅助诊断（CAD）系统，使用磁共振（MR）脑图像在不同寿命的自闭症诊断。为了提高分类稳健性，系统熔化从脑皮质（CX）和脑白质物质（CWM）中提取的形状特征。融合是基于调查结果进行的，这表明自闭症的CX变化与CWM异常有关。 CAD系统从使用相结合强度，形状和空间信息的3D联合模型开始分段CX和CWM。然后，将球形谐波（SpHarm）应用于CX的重建网格，以导出每个网格点的4个度量;正常曲率，平均曲率，高斯曲率和CX表面重建误差。为了分析CWM形状，计算其Gyri的距离图，并为这些Gyri提取了三种更多形状的特征。最后，所有提取的形状特征都被馈送到多级深网络，用于特征融合和诊断。 CAD系统已使用来自柬数据库（8-12.8岁）的科目进行评估，实现了93％的准确性，并从Ndar / Pitt数据库（16-51岁），实现了97％的准确性。还为了显示系统早期诊断的能力，它已经在Ndar / Ibis数据库上进行了用于婴儿的测试，从而准确度为85％。 3个数据库的这些初步结果持有了高效的自闭症诊断的承诺。

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