Toward more accurate diagnosis of multiple sclerosis: Automated lesion segmentation in brain magnetic resonance image using modified U-Net model

IF 3 4区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Imaging Systems and Technology Pub Date : 2023-07-28 DOI:10.1002/ima.22941

Bakhtiar Amaludin, Seifedine Kadry, Fung Fung Ting, David Taniar

引用次数: 0

Abstract

Early diagnosis of multiple sclerosis (MS) through the delineation of lesions in the brain magnetic resonance imaging is important in preventing the deteriorating condition of MS. This study aims to develop a modified U-Net model for automating lesions segmentation in MS more accurately. The proposed modified U-Net uses residual dense blocks to replace the standard convolutional stacks and incorporates three axes (axial, sagittal, and coronal) of 2D slice images as input. Furthermore, a custom fusion method is also introduced for merging the predicted lesions from different axes. The model was implemented on ISBI2015 and OpenMS data sets. On ISBI2015, the proposed model achieves the best overall score of 93.090% and DSC of 0.857 on the OpenMS data set.

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为更准确地诊断多发性硬化症而努力：利用改进的 U-Net 模型自动分割脑磁共振图像中的病灶

通过脑磁共振成像中的病灶划分对多发性硬化症（MS）进行早期诊断，对于防止多发性硬化症病情恶化非常重要。本研究旨在开发一种改进的 U-Net 模型，用于更准确地自动分割多发性硬化症的病灶。所提出的改进型 U-Net 使用残余密集块取代标准卷积堆栈，并将二维切片图像的三个轴（轴向、矢状和冠状）作为输入。此外，还引入了一种自定义融合方法，用于合并来自不同轴的预测病变。该模型在 ISBI2015 和 OpenMS 数据集上实现。在 ISBI2015 上，所提出的模型获得了 93.090% 的最佳总分，在 OpenMS 数据集上的 DSC 为 0.857。

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

International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术

CiteScore

6.90

自引率

6.10%

发文量

138

审稿时长

3 months

期刊介绍： The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.