A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation

In this paper, a Convolutional Neural Network (CNN) system is proposed for brain tumor segmentation. The system consists of three parts, a pre-processing block to reduce the data volume, an application-specific CNN(ASCNN) to segment tumor areas precisely, and a refinement block to detect/remove false positive pixels. The CNN, designed specifically for the task, has 7 convolution layers, 16 channels per layer, requiring only 11716 parameters. The convolutions combined with max-pooling in the first half of the CNN are performed to localize tumor areas. Two convolution modes, namely depthwise convolution and standard convolution, are performed in parallel in the first 2 layers to extract elementary features efficiently. For a fine classification of pixel-wise precision in the second half of the CNN, the feature maps are modulated by adding the individually weighted local feature maps generated in the first half of the CNN. The performance of the proposed system has been evaluated by an online platform with dataset of Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) 2018. Requiring a very low computation volume, the proposed system delivers a high segmentation quality indicated by its average Dice scores of 0.75, 0.88 and 0.76 for enhancing tumor, whole tumor and tumor core, respectively, and also by the median Dice scores of 0.85, 0.92, and 0.86. The consistency in system performance has also been measured, demonstrating that the system is able to reproduce almost the same output to the same input after retraining. The simple structure of the proposed system facilitates its implementation in computation restricted environment, and a wide range of applications can thus be expected

A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation

Brain tumor diagnosis is an important issue in health care. Automated brain tumor segmentation can help timely diagnosis. It is, however, very challenging to achieve high-quality segmentation results, because the shapes, sizes, textures and locations of brain tumors vary from patient to patient. To develop a Convolutional Neural Network (CNN) system for a high-quality brain tumor segmentation at the lowest computation cost, the CNN should be custom-designed to extract efficiently sufficient critical features particularly related to the tumors from brain images for the multi-class segmentation of tumor areas. In this thesis, a CNN system is proposed for brain tumor segmentation. The system consists of three parts, a pre-processing block to reduce the data volume, an application-specific CNN (ASCNN) to segment tumor areas precisely, and a refinement block to detect false positive voxels. The CNN, designed specifically for the task, has 7 convolution layers, and the number of output channels per layer is no more than 16. The convolutions combined with max-pooling in the first half of the CNN are performed to localize brain tumor areas. Two convolution modes, namely depthwise convolution and standard convolution, are performed in parallel in the first 2 layers to extract elementary features efficiently. In the second half of the CNN, the convolutions combined with upsampling are to segment different tumor areas. For a fine classification of pixel-wise precision, the feature maps are modulated by adding the weighted local feature maps generated in the first half of the CNN. The system has only 11716 parameters to be trained and, for a patient case of (240x240x155 x3) voxels, it requires only 21.14G Flops to complete the test. Hence, it is likely the simplest CNN system, so far reported, for brain tumor segmentation. The performance of the proposed system has been evaluated by means of CBICA Image Processing Portal with samples from dataset BRATS2018. Requiring a very low computation volume, the proposed system delivers a high segmentation quality indicated by its average Dice scores of 0.75, 0.88 and 0.76 for enhancing tumor, whole tumor and tumor core, respectively, and the median Dice scores of 0.85, 0.92, and 0.86. Its processing quality is comparable to the best ones so far reported. The consistency in system performance has also been measured, and the results have demonstrated that the system is able to reproduce almost the same output to the same input after retraining. In conclusion, the proposed CNN system has been designed to meet the specific needs to segment brain tumors or other kinds of tumors in medical images. In this way, the redundancy in computation can be minimized, the information density in data flow increased, and the computation efficiency/quality improved. This design demonstrates that a CNN system can be made to perform a high-quality processing, at a very low computation cost, for a specific application. Hence, ASCNN is an effective approach to lower the barrier of computation resource requirement of CNN systems in order to make them more implementable and applicable for general public