State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228, China.
School of Information and Communication Engineering, Hainan University, Haikou, 570228, China.
Sci Rep. 2023 Aug 7;13(1):12779. doi: 10.1038/s41598-023-39240-0.
As one of the malignant tumors with high mortality, the initial symptoms of liver cancer are not obvious. In addition, the liver is the largest internal organ of the human body, and its structure and distribution are relatively complex. Therefore, in order to help doctors judge liver cancer more accurately, this paper proposes a variant model based on Unet network. Before segmentation, the image is preprocessed, and Pulse Coupled Neural Network (PCNN) algorithm is used to filter the image adaptively to make the image clearer. For the segmentation model, the SE module is used as the input of the residual network, and then its output is connected to the Unet model through bilinear interpolation to perform the down-sampling and up-sampling operations. The dataset is a combination of Hainan Provincial People's Hospital and some public datasets Lits. The results show that this method has better segmentation performance and accuracy than the original Unet method, and the dice coefficient, mIou and other evaluation indicators have increased by at least 2.1%, which is a method that can be applied to cancer segmentation.
肝癌是死亡率较高的恶性肿瘤之一，其早期症状并不明显。此外，肝脏是人体最大的内部器官，其结构和分布较为复杂。因此，为了帮助医生更准确地判断肝癌，本文提出了一种基于 U 型网络的变体模型。在分割前，对图像进行预处理，采用脉冲耦合神经网络（PCNN）算法自适应地对图像进行滤波，使图像更加清晰。对于分割模型，SE 模块作为残差网络的输入，然后通过双线性插值将其输出连接到 U 型网络，以执行下采样和上采样操作。数据集是海南省人民医院和一些公共数据集 Lits 的组合。结果表明，该方法的分割性能和准确性均优于原始 U 型网络方法，其骰子系数、mIou 等评价指标至少提高了 2.1%，是一种可应用于癌症分割的方法。
