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Abstract：
OBJECTIVE: The purpose of this study is to develop accurate and automated detection and segmentation methods for brain tumors, given their significant fatality rates, with aggressive malignant tumors like Glioblastoma Multiforme (GBM) having a five-year survival rate as low as 5 to 10%. This underscores the urgent need to improve diagnosis and treatment outcomes through innovative approaches in medical imaging and deep learning techniques.
METHODS: In this work, we propose a novel approach utilizing the two-headed UNetEfficientNets model for simultaneous segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) images. The model combines the strengths of EfficientNets and a modified two-headed Unet model. We utilized a publicly available dataset consisting of 3064 brain MR images classified into three tumor classes: Meningioma, Glioma, and Pituitary. To enhance the training process, we performed 12 types of data augmentation on the training dataset. We evaluated the methodology using six deep learning models, ranging from UNetEfficientNet-B0 to UNetEfficientNet-B5, optimizing the segmentation and classification heads using binary cross entropy (BCE) loss with Dice and BCE with focal loss, respectively. Post-processing techniques such as connected component labeling (CCL) and ensemble models were applied to improve segmentation outcomes.
RESULTS: The proposed UNetEfficientNet-B4 model achieved outstanding results, with an accuracy of 99.4% after postprocessing. Additionally, it obtained high scores for DICE (94.03%), precision (98.67%), and recall (99.00%) after post-processing. The ensemble technique further improved segmentation performance, with a global DICE score of 95.70% and Jaccard index of 91.20%.
CONCLUSIONS: Our study demonstrates the high efficiency and accuracy of the proposed UNetEfficientNet-B4 model in the automatic and parallel detection and segmentation of brain tumors from MRI images. This approach holds promise for improving diagnosis and treatment planning for patients with brain tumors, potentially leading to better outcomes and prognosis.
METHODS: In this work, we propose a novel approach utilizing the two-headed UNetEfficientNets model for simultaneous segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) images. The model combines the strengths of EfficientNets and a modified two-headed Unet model. We utilized a publicly available dataset consisting of 3064 brain MR images classified into three tumor classes: Meningioma, Glioma, and Pituitary. To enhance the training process, we performed 12 types of data augmentation on the training dataset. We evaluated the methodology using six deep learning models, ranging from UNetEfficientNet-B0 to UNetEfficientNet-B5, optimizing the segmentation and classification heads using binary cross entropy (BCE) loss with Dice and BCE with focal loss, respectively. Post-processing techniques such as connected component labeling (CCL) and ensemble models were applied to improve segmentation outcomes.
RESULTS: The proposed UNetEfficientNet-B4 model achieved outstanding results, with an accuracy of 99.4% after postprocessing. Additionally, it obtained high scores for DICE (94.03%), precision (98.67%), and recall (99.00%) after post-processing. The ensemble technique further improved segmentation performance, with a global DICE score of 95.70% and Jaccard index of 91.20%.
CONCLUSIONS: Our study demonstrates the high efficiency and accuracy of the proposed UNetEfficientNet-B4 model in the automatic and parallel detection and segmentation of brain tumors from MRI images. This approach holds promise for improving diagnosis and treatment planning for patients with brain tumors, potentially leading to better outcomes and prognosis.
摘要：
目的：这项研究的目的是开发针对脑肿瘤的准确，自动化的检测和分割方法，鉴于他们的死亡率很高，多形性胶质母细胞瘤（GBM）等侵袭性恶性肿瘤的五年生存率低至5％至10％。这强调了迫切需要通过医学成像和深度学习技术的创新方法来改善诊断和治疗结果。
方法：在这项工作中，我们提出了一种新的方法，利用双头UNetEfficientNets模型从磁共振成像（MRI）图像中同时分割和分类脑肿瘤。该模型结合了EfficientNets和改进的双头Unet模型的优势。我们利用了由3064张脑部MR图像组成的公开数据集，这些图像分为三类肿瘤：脑膜瘤，胶质瘤,和垂体。为了加强培训过程，我们对训练数据集进行了12种类型的数据增强.我们使用六种深度学习模型对方法进行了评估，从UNetEfficientNet-B0到UNetEfficientNet-B5，使用带有Dice的二进制交叉熵（BCE）损失和带有焦点损失的BCE优化分割和分类头，分别。应用了后处理技术，例如连接分量标记（CCL）和集成模型，以改善分割结果。
结果：提出的UNetEfficientNet-B4模型取得了出色的结果，后处理后的准确率为99.4%。此外,它获得了DICE的高分(94.03%)，精度（98.67%），后处理后召回（99.00%）。集成技术进一步提高了分割性能，全球DICE得分为95.70%，Jaccard指数为91.20%。
结论：我们的研究证明了所提出的UNetEfficientNet-B4模型在从MRI图像中自动并行检测和分割脑肿瘤方面的高效率和准确性。这种方法有望改善脑肿瘤患者的诊断和治疗计划。可能导致更好的结果和预后。
方法：在这项工作中，我们提出了一种新的方法，利用双头UNetEfficientNets模型从磁共振成像（MRI）图像中同时分割和分类脑肿瘤。该模型结合了EfficientNets和改进的双头Unet模型的优势。我们利用了由3064张脑部MR图像组成的公开数据集，这些图像分为三类肿瘤：脑膜瘤，胶质瘤,和垂体。为了加强培训过程，我们对训练数据集进行了12种类型的数据增强.我们使用六种深度学习模型对方法进行了评估，从UNetEfficientNet-B0到UNetEfficientNet-B5，使用带有Dice的二进制交叉熵（BCE）损失和带有焦点损失的BCE优化分割和分类头，分别。应用了后处理技术，例如连接分量标记（CCL）和集成模型，以改善分割结果。
结果：提出的UNetEfficientNet-B4模型取得了出色的结果，后处理后的准确率为99.4%。此外,它获得了DICE的高分(94.03%)，精度（98.67%），后处理后召回（99.00%）。集成技术进一步提高了分割性能，全球DICE得分为95.70%，Jaccard指数为91.20%。
结论：我们的研究证明了所提出的UNetEfficientNet-B4模型在从MRI图像中自动并行检测和分割脑肿瘤方面的高效率和准确性。这种方法有望改善脑肿瘤患者的诊断和治疗计划。可能导致更好的结果和预后。
