论文标题
高质量自动脑肿瘤分段的性能一致和计算有效的CNN系统
A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation
论文作者
论文摘要
开发基于CNN的完全自动化的脑肿瘤分段系统的研究已迅速发展。为了使系统适用于实践中，良好的研究迅速发展了基于CNN的完全自动化的脑形分段系统。对于实践中适用的系统，必须具有良好的处理质量和可靠性。此外，对于此类系统的广泛应用，需要最小化计算复杂性，这也可能导致计算中随机性的最小化，从而可以更高的性能一致性。为此，提议的系统中的CNN具有一个独特的结构，具有2个杰出字符。首先，其特征提取块的三个路径旨在从多模式输入，单模式，配对模式和跨模式数据的全面特征信息中提取。此外，它具有一个特定的三个分支分类块，可以识别4个类的像素。每个分支都经过分别训练，以便将参数专门使用目标肿瘤区域的相应地面真实数据进行更新。系统的卷积层是针对特定目的定制设计的，总共产生了61,843个参数的非常简单的配置。提出的系统通过BRATS2018和BRATS2019数据集进行了广泛的测试。从BRATS2018验证样品上的十个实验中获得的平均骰子得分为0.787+0.003，0.886+0.002，0.801+0.007，分别用于增强肿瘤，全肿瘤和肿瘤核心，以及0.751+0.751+0.007，0.885+0.002，0.885+0.002，0.77776+0.776+0.776+0.004 on Brats2099。测试结果表明，所提出的系统能够以一致的方式执行高质量的分割。此外，其极低的计算复杂性将促进其在各种环境中的实现/应用。
The research on developing CNN-based fully-automated Brain-Tumor-Segmentation systems has been progressed rapidly. For the systems to be applicable in practice, a good The research on developing CNN-based fully-automated Brain-Tumor-Segmentation systems has been progressed rapidly. For the systems to be applicable in practice, a good processing quality and reliability are the must. Moreover, for wide applications of such systems, a minimization of computation complexity is desirable, which can also result in a minimization of randomness in computation and, consequently, a better performance consistency. To this end, the CNN in the proposed system has a unique structure with 2 distinguished characters. Firstly, the three paths of its feature extraction block are designed to extract, from the multi-modality input, comprehensive feature information of mono-modality, paired-modality and cross-modality data, respectively. Also, it has a particular three-branch classification block to identify the pixels of 4 classes. Each branch is trained separately so that the parameters are updated specifically with the corresponding ground truth data of a target tumor areas. The convolution layers of the system are custom-designed with specific purposes, resulting in a very simple config of 61,843 parameters in total. The proposed system is tested extensively with BraTS2018 and BraTS2019 datasets. The mean Dice scores, obtained from the ten experiments on BraTS2018 validation samples, are 0.787+0.003, 0.886+0.002, 0.801+0.007, for enhancing tumor, whole tumor and tumor core, respectively, and 0.751+0.007, 0.885+0.002, 0.776+0.004 on BraTS2019. The test results demonstrate that the proposed system is able to perform high-quality segmentation in a consistent manner. Furthermore, its extremely low computation complexity will facilitate its implementation/application in various environments.
