Abstract：
Diagnosis and treatment planning play a very vital role in improving the survival of oncological patients. However, there is high variability in the shape, size, and structure of the tumor, making automatic segmentation difficult. The automatic and accurate detection and segmentation methods for Brain tumors are proposed in this paper.
A modified ResNet50 model was used for tumor detection, and a ResUNetmodel-based convolutional neural network for segmentation is proposed in this paper. The detection and segmentation were performed on the same dataset consisting of pre-contrast, FLAIR, and postcontrast MRI images of 110 patients collected from the Cancer Imaging Archive. Due to the use of Residual Networks, the authors observed improvement in evaluation parameters, such as accuracy for tumor detection and dice similarity coefficient for tumor segmentation.
The accuracy of tumor detection and Dice Similarity Coefficient achieved by the segmentation model were 96.77% and 0.893, respectively, for the TCIA dataset. The results were compared based on manual segmentation and existing segmentation techniques. The tumor mask was also individually compared to the ground truth using the SSIM value. The proposed detection and segmentation models were validated on BraTS2015 and BraTS2017 datasets, and the results were consensus.
The use of residual networks in both the detection and the segmentation model resulted in improved accuracy and DSC score. DSC score was increased by 5.9% compared to the UNet model, and the accuracy of the model was increased from 92% to 96.77% for the test set.
A modified ResNet50 model was used for tumor detection, and a ResUNetmodel-based convolutional neural network for segmentation is proposed in this paper. The detection and segmentation were performed on the same dataset consisting of pre-contrast, FLAIR, and postcontrast MRI images of 110 patients collected from the Cancer Imaging Archive. Due to the use of Residual Networks, the authors observed improvement in evaluation parameters, such as accuracy for tumor detection and dice similarity coefficient for tumor segmentation.
The accuracy of tumor detection and Dice Similarity Coefficient achieved by the segmentation model were 96.77% and 0.893, respectively, for the TCIA dataset. The results were compared based on manual segmentation and existing segmentation techniques. The tumor mask was also individually compared to the ground truth using the SSIM value. The proposed detection and segmentation models were validated on BraTS2015 and BraTS2017 datasets, and the results were consensus.
The use of residual networks in both the detection and the segmentation model resulted in improved accuracy and DSC score. DSC score was increased by 5.9% compared to the UNet model, and the accuracy of the model was increased from 92% to 96.77% for the test set.
摘要：
背景：诊断和治疗计划在提高肿瘤患者的生存率中起着非常重要的作用。然而,形状有很高的可变性，尺寸,和肿瘤的结构，使自动分割变得困难。本文提出了一种自动准确的脑肿瘤检测和分割方法。
方法：使用改良的ResNet50模型进行肿瘤检测，本文提出了一种基于ResUNetmodel的卷积神经网络分割方法。在相同的数据集上进行检测和分割，FLAIR,以及从癌症影像档案中收集的110例患者的造影后MRI图像。由于使用了残差网络，作者观察到评估参数的改善，例如肿瘤检测的准确性和肿瘤分割的骰子相似系数。
结果：通过分割模型实现的肿瘤检测和Dice相似系数的准确率分别为96.77%和0.893，对于TCIA数据集。基于手动分割和现有分割技术对结果进行了比较。还使用SSIM值将肿瘤掩模与地面实况进行了单独比较。所提出的检测和分割模型在BraTS2015和BraTS2017数据集上进行了验证，结果是共识。
结论：在检测和分割模型中使用残差网络可提高准确性和DSC评分。与UNet模型相比，DSC评分提高了5.9%，模型的准确度从92%提高到96.77%。
方法：使用改良的ResNet50模型进行肿瘤检测，本文提出了一种基于ResUNetmodel的卷积神经网络分割方法。在相同的数据集上进行检测和分割，FLAIR,以及从癌症影像档案中收集的110例患者的造影后MRI图像。由于使用了残差网络，作者观察到评估参数的改善，例如肿瘤检测的准确性和肿瘤分割的骰子相似系数。
结果：通过分割模型实现的肿瘤检测和Dice相似系数的准确率分别为96.77%和0.893，对于TCIA数据集。基于手动分割和现有分割技术对结果进行了比较。还使用SSIM值将肿瘤掩模与地面实况进行了单独比较。所提出的检测和分割模型在BraTS2015和BraTS2017数据集上进行了验证，结果是共识。
结论：在检测和分割模型中使用残差网络可提高准确性和DSC评分。与UNet模型相比，DSC评分提高了5.9%，模型的准确度从92%提高到96.77%。
