Rohilla Saransh, Jain Shruti
Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India.
Curr Comput Aided Drug Des. 2025;21(1):15-27. doi: 10.2174/1573409920666230816090626.
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.
诊断和治疗规划在提高肿瘤患者生存率方面起着至关重要的作用。然而，肿瘤的形状、大小和结构存在很大差异，这使得自动分割变得困难。本文提出了用于脑肿瘤的自动且准确的检测和分割方法。
使用改进的ResNet50模型进行肿瘤检测，并提出了一种基于ResUNet模型的卷积神经网络用于分割。检测和分割是在同一个数据集上进行的，该数据集由从癌症成像存档中收集的110名患者的平扫、液体衰减反转恢复序列（FLAIR）和增强磁共振成像（MRI）图像组成。由于使用了残差网络，作者观察到评估参数有所改善，如肿瘤检测的准确率和肿瘤分割的骰子相似系数。
对于TCIA数据集，分割模型实现的肿瘤检测准确率和骰子相似系数分别为96.77%和0.893。基于手动分割和现有分割技术对结果进行了比较。还使用结构相似性指数（SSIM）值将肿瘤掩码与真实情况进行了单独比较。所提出的检测和分割模型在BraTS2015和BraTS2017数据集上得到了验证，结果是一致的。
在检测和分割模型中使用残差网络提高了准确率和DSC分数。与UNet模型相比，DSC分数提高了5.9%，测试集模型的准确率从92%提高到了96.77%。
