基于人工智能的医学图像多器官分割及其在医药领域的应用

Application of Artificial Intelligence Algorithms for Medical Image Multi-Organ Segmentation in the Field of Medicine

准确的医学图像多器官分割对临床应用和医药发展意义重大。然而,传统基于手工特征设计的图像处理方法难以处理图像中的组织纹理和复杂形态。近年来,随着人工智能的兴起,端到端的深度学习方法展现出在自动化医学图像分析方面的强大潜力。尤其是基于卷积神经网络和Transformer的U-Net系列网络,实现了对医学数据的精确语义分割,更在临床决策和疗效评估中提高了诊断和治疗的准确性。简介目前基于深度学习的医学图像多器官分割算法,重点关注U-Net系列网络的发展及多器官分割在医药领域的应用。

Accurate multi-organ segmentation in medical images holds significant importance for various clinical application and pharmaceutical development.However,traditional image processing methods based on manual feature design struggle with the complex textures and shapes in medical images.In recent years,with the rise of artificial intelligence,end-to-end deep learning approaches have demonstrated powerful potential in automated medical image analysis.Particularly,U-Net series of networks based on convolutional neural networks and Transformers have achieved precise semantic segmentation of medical data,which has significantly enhanced the accuracy of medical diagnosis and treatment in clinical decision-making and efficacy assessment.This paper reviews current deep learning-based algorithms for organ segmentation in medical images,focusing on the development of U-Net family networks and the application of multiorgan segmentation in medical advancements.