Morphology and Texture-Guided Deep Neural Network for Intracranial Aneurysm Segmentation in 3D TOF-MRA

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2024-09-11 DOI:10.1007/s12021-024-09683-5
Maysam Orouskhani, Negar Firoozeh, Huayu Wang, Yan Wang, Hanrui Shi, Weijing Li, Beibei Sun, Jianjian Zhang, Xiao Li, Huilin Zhao, Mahmud Mossa-Basha, Jenq-Neng Hwang, Chengcheng Zhu
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Abstract

This study concentrates on the segmentation of intracranial aneurysms, a pivotal aspect of diagnosis and treatment planning. We aim to overcome the inherent instance imbalance and morphological variability by introducing a novel morphology and texture loss reweighting approach. Our innovative method involves the incorporation of tailored weights within the loss function of deep neural networks. Specifically designed to account for aneurysm size, shape, and texture, this approach strategically guides the model to focus on capturing discriminative information from imbalanced features. The study conducted extensive experimentation utilizing ADAM and RENJI TOF-MRA datasets to validate the proposed approach. The results of our experimentation demonstrate the remarkable effectiveness of the introduced methodology in improving aneurysm segmentation accuracy. By dynamically adapting to the variances present in aneurysm features, our model showcases promising outcomes for accurate diagnostic insights. The nuanced consideration of morphological and textural nuances within the loss function proves instrumental in overcoming the challenge posed by instance imbalance. In conclusion, our study presents a nuanced solution to the intricate challenge of intracranial aneurysm segmentation. The proposed morphology and texture loss reweighting approach, with its tailored weights and dynamic adaptability, proves to be instrumental in enhancing segmentation precision. The promising outcomes from our experimentation suggest the potential for accurate diagnostic insights and informed treatment strategies, marking a significant advancement in this critical domain of medical imaging.

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用于三维 TOF-MRA 颅内动脉瘤分割的形态学和纹理引导的深度神经网络
本研究集中于颅内动脉瘤的分割,这是诊断和治疗计划的一个关键方面。我们旨在通过引入一种新颖的形态和纹理损失再加权方法来克服固有的实例不平衡和形态可变性。我们的创新方法是在深度神经网络的损失函数中加入量身定制的权重。这种方法专门针对动脉瘤的大小、形状和纹理而设计,可战略性地引导模型重点捕捉不平衡特征中的判别信息。研究利用 ADAM 和 RENJI TOF-MRA 数据集进行了广泛的实验,以验证所提出的方法。实验结果表明,所引入的方法在提高动脉瘤分割准确性方面效果显著。通过动态适应动脉瘤特征中存在的差异,我们的模型为准确诊断提供了可喜的成果。事实证明,在损失函数中对形态和纹理细微差别的细致考虑有助于克服实例不平衡带来的挑战。总之,我们的研究针对颅内动脉瘤分割这一错综复杂的难题提出了一种细致入微的解决方案。所提出的形态和纹理损失再加权方法具有量身定制的权重和动态适应性,被证明有助于提高分割精度。我们的实验取得了令人鼓舞的成果,这表明我们有可能获得准确的诊断见解和明智的治疗策略,这标志着医学成像这一关键领域取得了重大进展。
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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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