基于卷积神经网络的多发性硬化症脑损伤自动分割。

IF 2.3 4区 医学 Q3 CLINICAL NEUROLOGY
Emma Dereskewicz, Francesco La Rosa, Jonadab Dos Santos Silva, Edward Sizer, Amit Kohli, Maxence Wynen, William A. Mullins, Pietro Maggi, Sarah Levy, Kamso Onyemeh, Batuhan Ayci, Andrew J. Solomon, Jakob Assländer, Omar Al-Louzi, Daniel S. Reich, James Sumowski, Erin S. Beck
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引用次数: 0

摘要

背景与目的:脑磁共振成像(MRI)对多发性硬化症(MS)的研究至关重要。手动分割是费时且不一致的。我们的目标是开发一种用于t2加权液体衰减反转恢复(FLAIR) MRI的自动MS病变分割算法。方法:我们开发了一种基于nnunet 3D全分辨率U-Net的基于深度学习的多发性硬化症病变分割算法(FLAIR病灶分析),该算法基于668个FLAIR 1.5和3tesla扫描,来自多发性硬化症患者,在三个外部数据集上进行评估:MSSEG-2 (n = 14)、MSLesSeg (n = 51)和临床队列(n = 10),并与SAMSEG、LST-LPA和LST-AI进行比较。性能由两位盲法专家进行定性评估,并通过使用标准分割指标比较自动和地面真值病变掩模进行定量评估。结果:在20次扫描的盲法定性回顾中,两名评分者在15例中选择了火焰作为最准确的分割,在另外两例中,一名评分者倾向于火焰。在所有测试数据集中,flame的平均Dice得分为0.74,真阳性率为0.84,F1得分为0.78,始终优于基准方法。对于其他指标,包括阳性预测值、相对体积差和假阳性率,flame的表现与基准方法相似或优于基准方法。大多数火焰遗漏的病灶小于10 mm3,而基准方法除了遗漏较小的病灶外,还遗漏了较大的病灶。结论:flame是一种准确、稳健的MS病变分割方法,优于其他公开可用的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Novel Convolutional Neural Network for Automated Multiple Sclerosis Brain Lesion Segmentation

A Novel Convolutional Neural Network for Automated Multiple Sclerosis Brain Lesion Segmentation

Background and Purpose

Assessment of brain lesions on magnetic resonance imaging (MRI) is crucial for research in multiple sclerosis (MS). Manual segmentation is time-consuming and inconsistent. We aimed to develop an automated MS lesion segmentation algorithm for T2-weighted fluid-attenuated inversion recovery (FLAIR) MRI.

Methods

We developed FLAIR Lesion Analysis in Multiple Sclerosis (FLAMeS), a deep learning-based MS lesion segmentation algorithm based on the nnU-Net 3D full-resolution U-Net and trained on 668 FLAIR 1.5 and 3 tesla scans from persons with MS. FLAMeS was evaluated on three external datasets: MSSEG-2 (n = 14), MSLesSeg (n = 51), and a clinical cohort (n = 10), and compared to SAMSEG, LST-LPA, and LST-AI. Performance was assessed qualitatively by two blinded experts and quantitatively by comparing automated and ground truth lesion masks using standard segmentation metrics.

Results

In a blinded qualitative review of 20 scans, both raters selected FLAMeS as the most accurate segmentation in 15 cases, with one rater favoring FLAMeS in two additional cases. Across all testing datasets, FLAMeS achieved a mean Dice score of 0.74, a true positive rate of 0.84, and an F1 score of 0.78, consistently outperforming the benchmark methods. For other metrics, including positive predictive value, relative volume difference, and false positive rate, FLAMeS performed similarly to or better than benchmark methods. Most lesions missed by FLAMeS were smaller than 10 mm3, whereas the benchmark methods missed larger lesions in addition to smaller ones.

Conclusions

FLAMeS is an accurate, robust method for MS lesion segmentation that outperforms other publicly available methods.

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来源期刊
Journal of Neuroimaging
Journal of Neuroimaging 医学-核医学
CiteScore
4.70
自引率
0.00%
发文量
117
审稿时长
6-12 weeks
期刊介绍: Start reading the Journal of Neuroimaging to learn the latest neurological imaging techniques. The peer-reviewed research is written in a practical clinical context, giving you the information you need on: MRI CT Carotid Ultrasound and TCD SPECT PET Endovascular Surgical Neuroradiology Functional MRI Xenon CT and other new and upcoming neuroscientific modalities.The Journal of Neuroimaging addresses the full spectrum of human nervous system disease, including stroke, neoplasia, degenerating and demyelinating disease, epilepsy, tumors, lesions, infectious disease, cerebral vascular arterial diseases, toxic-metabolic disease, psychoses, dementias, heredo-familial disease, and trauma.Offering original research, review articles, case reports, neuroimaging CPCs, and evaluations of instruments and technology relevant to the nervous system, the Journal of Neuroimaging focuses on useful clinical developments and applications, tested techniques and interpretations, patient care, diagnostics, and therapeutics. Start reading today!
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