TransRNetFuse: a highly accurate and precise boundary FCN-transformer feature integration for medical image segmentation

IF 4.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Baotian Li, Jing Zhou, Fangfang Gou, Jia Wu
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引用次数: 0

Abstract

Imaging examinations are integral to the diagnosis and treatment of cancer. Nevertheless, the intricate nature of medical images frequently necessitates that physicians follow time-consuming and potentially fallible diagnostic procedures. In response to these challenges, deep learning-based image segmentation technology has emerged as a potent instrument for aiding physicians in navigating diagnostic complexities by extracting pivotal information from extensive sets of medical images. Nonetheless, the majority of existing models prioritize overall high accuracy, often overlooking the sensitivity to local salient features and the precision of segmentation boundaries. This oversight limits the full realization of the practical utility of deep learning models in clinical settings. This study introduces a novel pathological image segmentation method, termed TransRNetFuse, which incorporates stepwise feature aggregation and a residual fully convolutional network architecture. The objective of this method is to address the issues associated with the extraction of local key features and the accurate delineation of boundaries in medical image segmentation. The proposed model achieves enhanced overall performance by merging a fully convolutional network branch with a Transformer branch and utilizing residual blocks along with dense U-net skip connections. It prevents attentional dispersion by emphasizing local features, and further employs an automatic augmentation strategy to identify the optimal data augmentation scheme, which is particularly advantageous for small-sample datasets. Furthermore, this paper introduces an edge enhancement loss function to enhance the model's sensitivity to tumor boundaries. A dataset comprising 2164 pathological images, provided by Hunan Medical University General Hospital, was utilized for model training. The experimental results indicate that the proposed method outperforms existing techniques, such as MedT, in terms of both accuracy and edge precision, thereby demonstrating its significant potential for application in the medical field. Code: https://github.com/GFF1228/-TransRNetFuse.git.

TransRNetFuse:用于医学图像分割的高精度边界FCN-transformer特征集成
影像检查对于癌症的诊断和治疗是不可或缺的。然而,由于医学图像的复杂性,医生往往需要遵循耗时且可能出错的诊断程序。为了应对这些挑战,基于深度学习的图像分割技术已经成为帮助医生通过从大量医学图像中提取关键信息来导航诊断复杂性的有力工具。然而,现有的大多数模型都优先考虑整体的高精度,往往忽略了对局部显著特征的敏感性和分割边界的精度。这种疏忽限制了深度学习模型在临床环境中的实际应用的充分实现。本研究引入了一种新的病理图像分割方法TransRNetFuse,该方法结合了逐步特征聚合和残差全卷积网络架构。该方法的目的是解决医学图像分割中局部关键特征的提取和边界的准确划分问题。该模型通过将全卷积网络分支与Transformer分支合并,并利用剩余块以及密集的U-net跳过连接来提高整体性能。它通过强调局部特征来防止注意力分散,并进一步采用自动增强策略来识别最优的数据增强方案,这对小样本数据集尤其有利。此外,本文还引入了边缘增强损失函数来增强模型对肿瘤边界的敏感性。利用湖南医科大学总医院提供的2164张病理图像数据集进行模型训练。实验结果表明,该方法在精度和边缘精度方面都优于MedT等现有技术,在医学领域具有很大的应用潜力。代码:https://github.com/GFF1228/-TransRNetFuse.git。
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来源期刊
Complex & Intelligent Systems
Complex & Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
9.60
自引率
10.30%
发文量
297
期刊介绍: Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.
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