利用 UNET++ 网络开发脑磁共振成像图像分割程序,用于放射外科手术规划

Bui Ha, Tuấn Kiên Nguyễn, Dương Trần, Ngọc Toàn Trần, Quang Tuấn Hồ, Thu Trang Vũ
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引用次数: 0

摘要

图像处理是医学领域最重要、应用最广泛的技术之一。磁共振成像(MRI)可提供高对比度和高分辨率的诊断图像,特别是针对低密度组织。因此,支持肿瘤预测的应用得到了研究和开发。本文使用 UNET ++ 深度学习模型,利用应用人工智能识别和检测肿瘤,取得了识别率约为 80% 的成果。该结果为内置的医生支持软件系统在实践中提供了大量的内置功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of Brain MRI Image Segmentation program using UNET++ network for radiosurgery planning
Image processing is one of the most important and widely used techniques in the medical field. Magnetic Resonance Imaging (MRI) can provide diagnostic images with high contrast and high resolution, especially for low-density tissue. Therefore, applications to support tumor prediction are researched and developed. In this paper, we use applied artificial intelligence to identify and detect tumors using the UNET ++ deep learning model, which achieved results with a recognition rate of about 80%. The results for a great deal of built-in functionality in the built-in physician support software system in practice.
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