Introduction: The present study on the development and evaluation of an
automated brain tumor segmentation technique based on deep learning using the
3D U-Net model. Objectives: The objective is to leverage state-of-the-art
convolutional neural networks (CNNs) on a large dataset of brain MRI scans for
segmentation. Methods: The proposed methodology applies pre-processing
techniques for enhanced performance and generalizability. Results: Extensive
validation on an independent dataset confirms the model's robustness and
potential for integration into clinical workflows. The study emphasizes the
importance of data pre-processing and explores various hyperparameters to
optimize the model's performance. The 3D U-Net, has given IoUs for training and
validation dataset have been 0.8181 and 0.66 respectively. Conclusion:
Ultimately, this comprehensive framework showcases the efficacy of deep
learning in automating brain tumour detection, offering valuable support in
clinical practice.

该研究介绍了基于深度学习的自动化脑肿瘤分割技术，利用 3D U-Net 模型，通过大规模的脑 MRI 扫描数据集进行分割，并强调了数据预处理的重要性以及模型性能的优化。该综合框架展示了深度学习在自动化脑肿瘤检测中的功效，为临床实践提供有价值的支持。

基于深度学习的大脑图像分割技术用于自动肿瘤检测

Deep Learning-Based Brain Image Segmentation for Automated Tumour  Detection

Tumor segmentation from multi-modal brain MRI images is a challenging task
due to the limited samples, high variance in shapes and uneven distribution of
tumor morphology. The performance of automated medical image segmentation has
been significant improvement by the recent advances in deep learning. However,
the model predictions have not yet reached the desired level for clinical use
in terms of accuracy and generalizability. In order to address the distinct
problems presented in Challenges 1, 2, and 3 of BraTS 2023, we have constructed
an optimization framework based on a 3D U-Net model for brain tumor
segmentation. This framework incorporates a range of techniques, including
various pre-processing and post-processing techniques, and transfer learning.
On the validation datasets, this multi-modality brain tumor segmentation
framework achieves an average lesion-wise Dice score of 0.79, 0.72, 0.74 on
Challenges 1, 2, 3 respectively.

基于 3D U-Net 模型的多模态脑肿瘤分割框架在验证数据集上实现了 Challenges 1、2 和 3 分别为 0.79、0.72、0.74 的平均病灶 Dice 分数。