SAA-SDM: Neural Networks Faster Learned to Segment Organ Images

IF 2.9 2区工程技术 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of Digital Imaging Pub Date : 2024-01-10 DOI:10.1007/s10278-023-00947-1

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引用次数: 0

Abstract

In the field of medicine, rapidly and accurately segmenting organs in medical images is a crucial application of computer technology. This paper introduces a feature map module, Strength Attention Area Signed Distance Map (SAA-SDM), based on the principal component analysis (PCA) principle. The module is designed to accelerate neural networks’ convergence speed in rapidly achieving high precision. SAA-SDM provides the neural network with confidence information regarding the target and background, similar to the signed distance map (SDM), thereby enhancing the network’s understanding of semantic information related to the target. Furthermore, this paper presents a training scheme tailored for the module, aiming to achieve finer segmentation and improved generalization performance. Validation of our approach is carried out using TRUS and chest X-ray datasets. Experimental results demonstrate that our method significantly enhances neural networks’ convergence speed and precision. For instance, the convergence speed of UNet and UNET + + is improved by more than 30%. Moreover, Segformer achieves an increase of over 6% and 3% in mIoU (mean Intersection over Union) on two test datasets without requiring pre-trained parameters. Our approach reduces the time and resource costs associated with training neural networks for organ segmentation tasks while effectively guiding the network to achieve meaningful learning even without pre-trained parameters.

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SAA-SDM：神经网络更快学会器官图像分割

摘要在医学领域，快速准确地分割医学图像中的器官是计算机技术的一项重要应用。本文介绍了一种基于主成分分析（PCA）原理的特征图模块--强度注意区域符号距离图（SAA-SDM）。该模块旨在加快神经网络的收敛速度，快速实现高精度。SAA-SDM 为神经网络提供了目标和背景的置信度信息，类似于签名距离图（SDM），从而增强了神经网络对目标相关语义信息的理解。此外，本文还介绍了一种为该模块量身定制的训练方案，旨在实现更精细的分割并提高泛化性能。我们使用 TRUS 和胸部 X 光数据集对我们的方法进行了验证。实验结果表明，我们的方法显著提高了神经网络的收敛速度和精度。例如，UNET 和 UNET + + 的收敛速度提高了 30% 以上。此外，Segformer 在两个测试数据集上的 mIoU（平均交集大于联合）分别提高了 6% 和 3%，而无需预先训练参数。我们的方法减少了为器官分割任务训练神经网络所需的时间和资源成本，同时即使没有预先训练的参数，也能有效指导网络实现有意义的学习。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Digital Imaging 医学-核医学

CiteScore

7.50

自引率

6.80%

发文量

192

审稿时长

6-12 weeks

期刊介绍： The Journal of Digital Imaging (JDI) is the official peer-reviewed journal of the Society for Imaging Informatics in Medicine (SIIM). JDI’s goal is to enhance the exchange of knowledge encompassed by the general topic of Imaging Informatics in Medicine such as research and practice in clinical, engineering, and information technologies and techniques in all medical imaging environments. JDI topics are of interest to researchers, developers, educators, physicians, and imaging informatics professionals. Suggested Topics PACS and component systems; imaging informatics for the enterprise; image-enabled electronic medical records; RIS and HIS; digital image acquisition; image processing; image data compression; 3D, visualization, and multimedia; speech recognition; computer-aided diagnosis; facilities design; imaging vocabularies and ontologies; Transforming the Radiological Interpretation Process (TRIP™); DICOM and other standards; workflow and process modeling and simulation; quality assurance; archive integrity and security; teleradiology; digital mammography; and radiological informatics education.