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Neural Networks for In Situ Detection of Glioma Infiltration using Optical Coherence Tomography

机译:神经网络使用光学相干断层扫描技术原位检测神经胶质瘤浸润

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In brain cancer surgery, maximal tumor resection improves overall survival and quality of life survival in low-grade and high-grade glioma. Different technologies such as intraoperative magnetic resonance imaging and computed tomography have made major contributions; however, these technologies do not provide quantitative, real-time and three-dimensional continuous guidance. Optical Coherence Tomography (OCT) is a non-invasive, label-free, real-time, high-resolution imaging modality that has been explored for glioma infiltration detection. Here we report a novel Artificial Neural Network (ANN)-based computer-aided diagnosis (CAD) method for automated, real-time, in situ detection of glioma-infiltrated tumor margins. Near 500 volumetric OCT samples were intraoperatively obtained from resected brain tissue specimens of 21 patients with glioma tumors of different stages and labeled as either non-cancerous or glioma-infiltrated based on histopathology evaluation (gold standard). Labeled OCT images from 12 patients were used as training dataset to develop the artificial neural network. Unlabeled OCT images from the other 9 patients were used as a validation dataset to quantify the method detection performance. The CAD system achieved excellent levels of both sensitivity and specificity (-90%) for detecting glioma-infiltrated tissue with high spatial resolution (-16 μm laterally). Previous methods for OCT-based detection of glioma-infiltrated brain tissue rely on underlying optical properties such as attenuation coefficient from the OCT signal requiring sacrificing spatial resolution and cumbersome calibration procedures. By overcoming these major challenges, our novel ANN-assisted CAD system will enable implementing practical OCT-guided surgical tools for continuous, real-time and accurate intra-operative detection of glioma-infiltrated brain tissue, facilitating maximal glioma resection and superior surgical outcomes for glioma patients.
机译:在脑癌手术中,最大肿瘤切除改善了低级和高等胶质瘤的整体存活率和生活质量。术中磁共振成像和计算机断层扫描等不同技术取得了重大贡献;然而,这些技术没有提供定量,实时和三维连续引导。光学相干断层扫描(OCT)是一种非侵入性,无标签,实时,高分辨率的成像模型,已被探索为胶质瘤渗透检测。在这里,我们报告了一种新的人工神经网络(ANN)基础的计算机辅助诊断(CAD)方法,用于自动化,实时,原位检测胶质瘤浸润的肿瘤余量。接近500体积的OCT样品术上从切除的脑组织标本的21例不同阶段的胶质瘤肿瘤患者中术中获得,并标记为基于组织病理学评估(金标准)的非癌细胞或胶质瘤渗透。标记为12名患者的OCT图像被用作培训数据集以开发人工神经网络。来自其他9名患者的未标记的OCT图像被用作验证数据集以量化方法检测性能。 CAD系统实现了敏感性和特异性(-90%)的优异水平,用于检测具有高空间分辨率的胶质瘤渗透组织(-16μm)。以前的基于OCT基渗透脑组织检测的方法依赖于底层光学性质,例如来自OCT信号的衰减系数,需要牺牲空间分辨率和麻烦的校准程序。通过克服这些重大挑战,我们的新颖神经安辅助CAD系统将实现实施实用的OCT-DECOWIGED外科手术工具,用于连续,实时和准确地术中检测胶质瘤浸润的脑组织,促进最大的胶质瘤切除和卓越的手术结果胶质瘤患者。

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