Department of Computer Science and IT, The University of Lahore, Sargodha Campus, Sargodha 40100, Pakistan.
College of Computer Science and Software Engineering, Computer Vision Institute, Shenzhen University, Shenzhen 518060, Guangdong, China.
Comput Intell Neurosci. 2022 Mar 30;2022:7954333. doi: 10.1155/2022/7954333. eCollection 2022.
Liver segmentation and recognition from computed tomography (CT) images is a warm topic in image processing which is helpful for doctors and practitioners. Currently, many deep learning methods are used for liver segmentation that takes a long time to train the model which makes this task challenging and limited to larger hardware resources. In this research, we proposed a very lightweight convolutional neural network (CNN) to extract the liver region from CT scan images. The suggested CNN algorithm consists of 3 convolutional and 2 fully connected layers, where softmax is used to discriminate the liver from background. Random Gaussian distribution is used for weight initialization which achieved a distance-preserving-embedding of the information. The proposed network is known as Ga-CNN (Gaussian-weight initialization of CNN). General experiments are performed on three benchmark datasets including MICCAI SLiver'07, 3Dircadb01, and LiTS17. Experimental results show that the proposed method performed well on each benchmark dataset.
肝脏 CT 图像分割和识别是图像处理中的一个热门话题，有助于医生和从业人员进行诊断。目前，许多深度学习方法都被用于肝脏分割，这些方法需要很长时间来训练模型，这使得这项任务具有挑战性，并且受到较大硬件资源的限制。在这项研究中，我们提出了一种非常轻量级的卷积神经网络（CNN），用于从 CT 扫描图像中提取肝脏区域。所提出的 CNN 算法由 3 个卷积层和 2 个全连接层组成，其中使用 softmax 来区分肝脏和背景。随机高斯分布用于权重初始化，实现了信息的保距嵌入。该网络被称为 Ga-CNN（CNN 的高斯权重初始化）。在三个基准数据集上进行了一般实验，包括 MICCAI SLiver'07、3Dircadb01 和 LiTS17。实验结果表明，所提出的方法在每个基准数据集上都表现良好。
