论文标题
用于高质量脑肿瘤分割的计算有效的CNN系统
A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation
论文作者
论文摘要
本文介绍的工作是提出一个可靠的高质量卷积神经网络（CNN），用于脑肿瘤分割，并具有低计算要求。该系统由用于分割的主要处理的CNN组成，这是用于数据降低和CNN细化后块的CNN块。独特的CNN由7个卷积层组成，仅涉及108个内核和20308可训练的参数。根据提出的ASCNN（应用特定CNN）的范式进行自定义设计，以执行单模式和交叉模式特征提取，肿瘤定位和像素分类。每个层都符合分配给其的任务，通过（i）适用于其输入数据的适当归一化，（ii）为分配的任务正确卷积模式，以及（iii）合适的非线性转换以优化卷积结果。在这种特定的设计环境中，这7层中每一层中的内核数量都足以完成其任务，而不是在层上成倍增长，以提高信息密度并降低处理中的随机性。提出的激活功能全磁盘有助于使高通滤波卷积层中的内核数量减少，而不会降低处理质量。已经对BRATS2018数据集进行了大量实验，以测量所提出系统的处理质量和可重复性。结果表明，该系统在再培训后可靠地将几乎相同的输出重现为相同的输入。增强肿瘤，整个肿瘤和肿瘤核心的平均骰子得分分别为77.2％，89.2％和76.3％。拟议系统的简单结构和可靠的高处理质量将促进其实施和医疗应用。
The work presented in this paper is to propose a reliable high-quality system of Convolutional Neural Network (CNN) for brain tumor segmentation with a low computation requirement. The system consists of a CNN for the main processing for the segmentation, a pre-CNN block for data reduction and post-CNN refinement block. The unique CNN consists of 7 convolution layers involving only 108 kernels and 20308 trainable parameters. It is custom-designed, following the proposed paradigm of ASCNN (application specific CNN), to perform mono-modality and cross-modality feature extraction, tumor localization and pixel classification. Each layer fits the task assigned to it, by means of (i) appropriate normalization applied to its input data, (ii) correct convolution modes for the assigned task, and (iii) suitable nonlinear transformation to optimize the convolution results. In this specific design context, the number of kernels in each of the 7 layers is made to be just-sufficient for its task, instead of exponentially growing over the layers, to increase information density and to reduce randomness in the processing. The proposed activation function Full-ReLU helps to halve the number of kernels in convolution layers of high-pass filtering without degrading processing quality. A large number of experiments with BRATS2018 dataset have been conducted to measure the processing quality and reproducibility of the proposed system. The results demonstrate that the system reproduces reliably almost the same output to the same input after retraining. The mean dice scores for enhancing tumor, whole tumor and tumor core are 77.2%, 89.2% and 76.3%, respectively. The simple structure and reliable high processing quality of the proposed system will facilitate its implementation and medical applications.
