论文标题
在MRI图像中使用U-NET网络进行有效的脑肿瘤分割
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images
论文作者
论文摘要
磁共振成像（MRI）是用于医学图像采集的最常用的非侵入性技术。脑肿瘤分割是算法鉴定脑MRI扫描中肿瘤的过程。尽管在文献中提出了许多用于脑肿瘤分割的方法，但本文提出了U-NET的轻量级实施。除了提供MRI扫描的实时细分外，所提出的架构不需要大量数据来训练拟议的轻型U-NET。此外，不需要其他数据增强步骤。轻巧的U-NET在BITE数据集上显示出非常有希望的结果，并且在超过标准基准算法的同时，其平均交叉点（IOU）为89％。此外，这项工作证明了三个透视平面的有效使用，而不是原始的三维体积图像，以简化脑肿瘤分割。
Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusive technique for medical image acquisition. Brain tumor segmentation is the process of algorithmically identifying tumors in brain MRI scans. While many approaches have been proposed in the literature for brain tumor segmentation, this paper proposes a lightweight implementation of U-Net. Apart from providing real-time segmentation of MRI scans, the proposed architecture does not need large amount of data to train the proposed lightweight U-Net. Moreover, no additional data augmentation step is required. The lightweight U-Net shows very promising results on BITE dataset and it achieves a mean intersection-over-union (IoU) of 89% while outperforming the standard benchmark algorithms. Additionally, this work demonstrates an effective use of the three perspective planes, instead of the original three-dimensional volumetric images, for simplified brain tumor segmentation.
