Jiang Linfeng, Ou Jiajie, Liu Ruihua, Zou Yangyang, Xie Ting, Xiao Hanguang, Bai Ting
School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China.
School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China.
Comput Biol Med. 2023 May;158:106838. doi: 10.1016/j.compbiomed.2023.106838. Epub 2023 Mar 28.
Liver cancer is one of the leading causes of cancer-related deaths worldwide. Automatic liver and tumor segmentation are of great value in clinical practice as they can reduce surgeons' workload and increase the probability of success in surgery. Liver and tumor segmentation is a challenging task because of the different sizes, shapes, blurred boundaries of livers and lesions, and low-intensity contrast between organs within patients. To address the problem of fuzzy livers and small tumors, we propose a novel Residual Multi-scale Attention U-Net (RMAU-Net) for liver and tumor segmentation by introducing two modules, i.e., Res-SE-Block and MAB. The Res-SE-Block can mitigate the problem of gradient disappearance by residual connection and enhance the quality of representations by explicitly modeling the interdependencies and feature recalibration between the channels of features. The MAB can exploit rich multi-scale feature information and capture inter-channel and inter-spatial relationships of features simultaneously. In addition, a hybrid loss function, that combines focal loss and dice loss, is designed to improve segmentation accuracy and speed up convergence. We evaluated the proposed method on two publicly available datasets, i.e., LiTS and 3D-IRCADb. Our proposed method achieved better performance than the other state-of-the-art methods, with dice scores of 0.9552 and 0.9697 for LiTS and 3D-IRCABb liver segmentation, and dice scores of 0.7616 and 0.8307 for LiTS and 3D-IRCABb liver tumor segmentation.
肝癌是全球癌症相关死亡的主要原因之一。肝脏和肿瘤的自动分割在临床实践中具有重要价值，因为它们可以减轻外科医生的工作量并提高手术成功率。肝脏和肿瘤分割是一项具有挑战性的任务，因为肝脏和病变的大小、形状各异，边界模糊，且患者体内器官之间的强度对比度较低。为了解决肝脏模糊和小肿瘤的问题，我们提出了一种新颖的残差多尺度注意力U-Net（RMAU-Net）用于肝脏和肿瘤分割，通过引入两个模块，即Res-SE-Block和MAB。Res-SE-Block可以通过残差连接缓解梯度消失问题，并通过显式建模特征通道之间的相互依赖关系和特征重新校准来提高表征质量。MAB可以利用丰富的多尺度特征信息并同时捕获特征的通道间和空间间关系。此外，设计了一种结合焦点损失和骰子损失的混合损失函数，以提高分割精度并加速收敛。我们在两个公开可用的数据集LiTS和3D-IRCADb上评估了所提出的方法。我们提出的方法比其他现有方法表现更好，在LiTS和3D-IRCABb肝脏分割中骰子分数分别为0.9552和0.9697，在LiTS和3D-IRCABb肝脏肿瘤分割中骰子分数分别为0.7616和0.8307。
