Abstract：
OBJECTIVE: In patients having naïve glioblastoma multiforme (GBM), this study aims to assess the efficacy of Deep Learning algorithms in automating the segmentation of brain magnetic resonance (MR) images to accurately determine 3D masks for 4 distinct regions: enhanced tumor, peritumoral edema, non-enhanced/necrotic tumor, and total tumor.
METHODS: A 3D U-Net neural network algorithm was developed for semantic segmentation of GBM. The training dataset was manually delineated by a group of expert neuroradiologists on MR images from the Brain Tumor Segmentation Challenge 2021 (BraTS2021) image repository, as ground truth labels for diverse glioma (GBM and low-grade glioma) subregions across four MR sequences (T1w, T1w-contrast enhanced, T2w, and FLAIR) in 1251 patients. The in-house test was performed on 50 GBM patients from our cohort (PerProGlio project). By exploring various hyperparameters, the network\'s performance was optimized, and the most optimal parameter configuration was identified. The assessment of the optimized network\'s performance utilized Dice scores, precision, and sensitivity metrics.
RESULTS: Our adaptation of the 3D U-net with additional residual blocks demonstrated reliable performance on both the BraTS2021 dataset and the in-house PerProGlio cohort, employing only T1w-ce sequences for enhancement and non-enhanced/necrotic tumor models and T1w-ce + T2w + FLAIR for peritumoral edema and total tumor. The mean Dice scores (training and test) were 0.89 and 0.75; 0.75 and 0.64; 0.79 and 0.71; and 0.60 and 0.55, for total tumor, edema, enhanced tumor, and non-enhanced/necrotic tumor, respectively.
CONCLUSIONS: The results underscore the high precision with which our network can effectively segment GBM tumors and their distinct subregions. The level of accuracy achieved agrees with the coefficients recorded in previous GBM studies. In particular, our approach allows model specialization for each of the different tumor subregions employing only those MR sequences that provide value for segmentation.
METHODS: A 3D U-Net neural network algorithm was developed for semantic segmentation of GBM. The training dataset was manually delineated by a group of expert neuroradiologists on MR images from the Brain Tumor Segmentation Challenge 2021 (BraTS2021) image repository, as ground truth labels for diverse glioma (GBM and low-grade glioma) subregions across four MR sequences (T1w, T1w-contrast enhanced, T2w, and FLAIR) in 1251 patients. The in-house test was performed on 50 GBM patients from our cohort (PerProGlio project). By exploring various hyperparameters, the network\'s performance was optimized, and the most optimal parameter configuration was identified. The assessment of the optimized network\'s performance utilized Dice scores, precision, and sensitivity metrics.
RESULTS: Our adaptation of the 3D U-net with additional residual blocks demonstrated reliable performance on both the BraTS2021 dataset and the in-house PerProGlio cohort, employing only T1w-ce sequences for enhancement and non-enhanced/necrotic tumor models and T1w-ce + T2w + FLAIR for peritumoral edema and total tumor. The mean Dice scores (training and test) were 0.89 and 0.75; 0.75 and 0.64; 0.79 and 0.71; and 0.60 and 0.55, for total tumor, edema, enhanced tumor, and non-enhanced/necrotic tumor, respectively.
CONCLUSIONS: The results underscore the high precision with which our network can effectively segment GBM tumors and their distinct subregions. The level of accuracy achieved agrees with the coefficients recorded in previous GBM studies. In particular, our approach allows model specialization for each of the different tumor subregions employing only those MR sequences that provide value for segmentation.
摘要：
目的：在患有多形性胶质母细胞瘤（GBM）的患者中，这项研究旨在评估深度学习算法在自动化脑磁共振（MR）图像分割中的功效，以准确确定4个不同区域的3D掩模：增强的肿瘤，瘤周水肿，非增强/坏死性肿瘤，和总肿瘤。
方法：开发了一种用于GBM语义分割的3DU-Net神经网络算法。训练数据集由一组专家神经放射科医生对来自脑肿瘤分割挑战2021（BraTS2021）图像库的MR图像进行手动描绘，作为四个MR序列(T1w，T1w对比度增强,T2w,和FLAIR）在1251名患者中。对我们队列中的50名GBM患者进行了内部测试(PerProGlio项目)。通过探索各种超参数，网络的性能得到了优化，并确定了最优参数配置。利用Dice分数对优化网络性能的评估，精度,和敏感度指标。
结果：我们对3DU网的调整以及额外的残差块在BraTS2021数据集和内部PerProGlio队列上都表现出可靠的性能，仅使用T1w-ce序列用于增强和非增强/坏死肿瘤模型，使用T1w-ceT2wFLAIR用于肿瘤周围水肿和总肿瘤。平均Dice评分（训练和测试）为0.89和0.75;0.75和0.64;0.79和0.71;和0.60和0.55，对于总肿瘤，水肿,增强的肿瘤，和非增强/坏死性肿瘤，分别。
结论：结果强调了我们的网络可以有效地分割GBM肿瘤及其不同亚区域的高精度。达到的准确性水平与以前的GBM研究中记录的系数一致。特别是,我们的方法允许针对每个不同肿瘤子区域的模型特化,仅使用那些为分割提供价值的MR序列.
方法：开发了一种用于GBM语义分割的3DU-Net神经网络算法。训练数据集由一组专家神经放射科医生对来自脑肿瘤分割挑战2021（BraTS2021）图像库的MR图像进行手动描绘，作为四个MR序列(T1w，T1w对比度增强,T2w,和FLAIR）在1251名患者中。对我们队列中的50名GBM患者进行了内部测试(PerProGlio项目)。通过探索各种超参数，网络的性能得到了优化，并确定了最优参数配置。利用Dice分数对优化网络性能的评估，精度,和敏感度指标。
结果：我们对3DU网的调整以及额外的残差块在BraTS2021数据集和内部PerProGlio队列上都表现出可靠的性能，仅使用T1w-ce序列用于增强和非增强/坏死肿瘤模型，使用T1w-ceT2wFLAIR用于肿瘤周围水肿和总肿瘤。平均Dice评分（训练和测试）为0.89和0.75;0.75和0.64;0.79和0.71;和0.60和0.55，对于总肿瘤，水肿,增强的肿瘤，和非增强/坏死性肿瘤，分别。
结论：结果强调了我们的网络可以有效地分割GBM肿瘤及其不同亚区域的高精度。达到的准确性水平与以前的GBM研究中记录的系数一致。特别是,我们的方法允许针对每个不同肿瘤子区域的模型特化,仅使用那些为分割提供价值的MR序列.
