深度学习在人体脊柱图像分割中的应用研究

Zhao Feng, Qi Min, Xu Hua
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引用次数: 0

摘要

传统的分割方法只能分割灰度图像,限制了其应用范围;分割过程往往依赖医生的经验,会导致主观因素影响分割结果;因此,分割的准确性和效率难以达到实际应用效果。深度学习模型是一种模仿人脑神经连接的结构模型。深度学习模型可以准确提取图像中关键信息从低级到高级的多层次特征,并对数据解读进行反馈,从而实现准确高效的图像分割结果。将深度学习算法引入医学影像分割,可以准确表达脊柱图像中更深层次的关键信息,实现更好的图像分割效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on the Application of Deep Learning in Human Spinal Image Segmentation
Traditional segmentation methods can only segment grayscale images, which limits their application; The segmentation process often depends on the doctor’s experience, which can lead to subjective factors affecting the results; Therefore, the accuracy and efficiency of segmentation are difficult to achieve practical application results. The deep learning model is a structural model that mimics the neural connections within the human brain. The deep learning model can accurately extract multi-level features of key information in images from low-level to high-level, and provide feedback on data interpretation, thereby achieving accurate and efficient image segmentation results. Introducing deep learning algorithms into medical image segmentation can accurately express the key information at a deeper level in spinal images, achieving better image segmentation results.
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