生成式对抗网络在增强脑肿瘤中的潜在作用。

IF 3.3 Q2 ONCOLOGY
Amr Muhammed, Rafaat A Bakheet, Karam Kenawy, Ahmed M A Ahmed, Muhammed Abdelhamid, Walaa Gamal Soliman
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引用次数: 0

摘要

目的:对比度增强对于脑肿瘤的可视化、诊断和治疗非常必要。通过这项研究,我们旨在研究通用对抗神经网络在使用轻量级模型生成基于人工智能的肿瘤增强方面的潜在作用:我们对 2020 年至 2023 年期间确诊的脑肿瘤患者的磁共振成像扫描结果进行了回顾性研究。研究建立了一个生成对抗神经网络,以生成模拟这些肿瘤真实对比度增强的图像。神经网络的性能通过 VGG-16、ResNet、二元交叉熵损失、平均绝对误差、平均平方误差和结构相似性指数等指标进行定量评估。在定性评估方面,从测试集中随机选取了九个病例,并利用这些病例为有经验的医学专业人员制作了一份简短的满意度调查表:从医院数据库中找出了 129 名患者,共 156 次扫描。数据被随机分成训练集和验证集(90%)以及测试集(10%)。训练集、验证集和测试集的 VGG 损失函数分别为 2,049.8、2,632.6 和 4,276.9。此外,结构相似性指数分别为 0.366、0.356 和 0.3192。在提交文章时,共有 23 位医疗专业人士对调查做出了回应。总体满意度的中位数为 7 分(满分 10 分):我们的网络将为使用轻量级模型进行人工对比度增强打开一扇大门。要达到临床实用性,还需要在这一领域开展进一步的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Potential Role of Generative Adversarial Networks in Enhancing Brain Tumors.

Purpose: Contrast enhancement is necessary for visualizing, diagnosing, and treating brain tumors. Through this study, we aimed to examine the potential role of general adversarial neural networks in generating artificial intelligence-based enhancement of tumors using a lightweight model.

Patients and methods: A retrospective study was conducted on magnetic resonance imaging scans of patients diagnosed with brain tumors between 2020 and 2023. A generative adversarial neural network was built to generate images that would mimic the real contrast enhancement of these tumors. The performance of the neural network was evaluated quantitatively by VGG-16, ResNet, binary cross-entropy loss, mean absolute error, mean squared error, and structural similarity index measures. Regarding the qualitative evaluation, nine cases were randomly selected from the test set and were used to build a short satisfaction survey for experienced medical professionals.

Results: One hundred twenty-nine patients with 156 scans were identified from the hospital database. The data were randomly split into a training set and validation set (90%) and a test set (10%). The VGG loss function for training, validation, and test sets were 2,049.8, 2,632.6, and 4,276.9, respectively. Additionally, the structural similarity index measured 0.366, 0.356, and 0.3192, respectively. At the time of submitting the article, 23 medical professionals responded to the survey. The median overall satisfaction score was 7 of 10.

Conclusion: Our network would open the door for using lightweight models in performing artificial contrast enhancement. Further research is necessary in this field to reach the point of clinical practicality.

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来源期刊
CiteScore
6.20
自引率
4.80%
发文量
190
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