Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery.

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Hemalatha Kanakarajan, Wouter De Baene, Patrick Hanssens, Margriet Sitskoorn
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引用次数: 0

Abstract

Background and purpose: Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several DL algorithms focusing only on training and testing the models on the planning MRI only. The purpose of this study is to evaluate well-known DL approaches (nnU-Net and MedNeXt) for their performance on both planning and follow-up MRI.

Materials and methods: Pre-treatment brain MRIs were retrospectively collected for 255 patients at Elisabeth-TweeSteden Hospital (ETZ): 201 for training and 54 for testing, including follow-up MRIs for the test set. To increase heterogeneity, we added the publicly available MRI scans from the Mathematical oncology laboratory of 75 patients to the training data. The performance was compared between the two models, with and without the addition of the public data. To statistically compare the Dice Similarity Coefficient (DSC) of the two models trained on different datasets over multiple time points, we used Linear Mixed Models.

Results: All models obtained a good DSC (DSC > = 0.93) for planning MRI. MedNeXt trained with combined data provided the best DSC for follow-ups at 6, 15, and 21 months (DSC of 0.74, 0.74, and 0.70 respectively) and jointly the best DSC for follow-ups at three months with MedNeXt trained with ETZ data only (DSC of 0.78) and 12 months with nnU-Net trained with combined data (DSC of 0.71). On the other hand, nnU-Net trained with combined data provided the best sensitivity and FNR for most follow-ups. The statistical analysis showed that MedNeXt provides higher DSC for both datasets and the addition of public data to the training dataset results in a statistically significant increase in performance in both models.

Conclusion: The models achieved a good performance score for planning MRI. Though the models performed less effectively for follow-ups, the addition of public data enhanced their performance, providing a viable solution to improve their efficacy for the follow-ups. These algorithms hold promise as a valuable tool for clinicians for automated segmentation of planning and follow-up MRI scans during stereotactic radiosurgery treatment planning and response evaluations, respectively.

Clinical trial number: Not applicable.

立体定向放射手术前后t1加权对比增强MR图像脑转移的自动分割。
背景与目的:对放射科医生来说,磁共振成像(MRI)对脑转移瘤的准确分割既繁琐又耗时,可以通过深度学习(DL)进行优化。以前的研究评估了几种深度学习算法,这些算法只关注计划MRI上的模型训练和测试。本研究的目的是评估众所周知的深度学习方法(nnU-Net和MedNeXt)在计划和随访MRI方面的表现。材料与方法:回顾性收集Elisabeth-TweeSteden医院(ETZ) 255例患者的治疗前脑mri: 201例用于训练,54例用于测试,包括对测试组的随访mri。为了增加异质性,我们将来自数学肿瘤学实验室的75名患者的公开可用MRI扫描添加到训练数据中。在添加和不添加公共数据的情况下,比较了两种模型的性能。为了统计比较在不同数据集上训练的两个模型在多个时间点上的骰子相似系数(DSC),我们使用线性混合模型。结果:所有模型均获得较好的DSC (DSC > = 0.93),可用于规划MRI。使用联合数据训练的MedNeXt在6、15和21个月时提供了最佳的DSC (DSC分别为0.74、0.74和0.70),仅使用ETZ数据训练的MedNeXt在3个月时提供了最佳的DSC (DSC为0.78),使用联合数据训练的nnU-Net在12个月时提供了最佳的DSC (DSC为0.71)。另一方面,联合数据训练的nnU-Net在大多数随访中提供了最佳的灵敏度和FNR。统计分析表明,MedNeXt为两个数据集提供了更高的DSC,并且将公共数据添加到训练数据集导致两个模型的性能在统计上显着提高。结论:该模型在规划MRI时获得了较好的性能评分。虽然模型对随访的效果较差,但公共数据的加入提高了模型的效果,为提高模型对随访的效果提供了可行的解决方案。这些算法有望成为临床医生在立体定向放射外科治疗计划和反应评估期间自动分割计划和后续MRI扫描的有价值的工具。临床试验号:不适用。
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
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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