Abstract：
Accurate segmentation of liver tumors is a prerequisite for early diagnosis of liver cancer. Segmentation networks extract features continuously at the same scale, which cannot adapt to the variation of liver tumor volume in computed tomography (CT). Hence, a multi-scale feature attention network (MS-FANet) for liver tumor segmentation is proposed in this paper. The novel residual attention (RA) block and multi-scale atrous downsampling (MAD) are introduced in the encoder of MS-FANet to sufficiently learn variable tumor features and extract tumor features at different scales simultaneously. The dual-path feature (DF) filter and dense upsampling (DU) are introduced in the feature reduction process to reduce effective features for the accurate segmentation of liver tumors. On the public LiTS dataset and 3DIRCADb dataset, MS-FANet achieved 74.2% and 78.0% of average Dice, respectively, outperforming most state-of-the-art networks, this strongly proves the excellent liver tumor segmentation performance and the ability to learn features at different scales.
摘要：
肝脏肿瘤的准确分割是肝癌早期诊断的前提。分割网络以相同的尺度连续提取特征，不能适应计算机断层扫描（CT）中肝脏肿瘤体积的变化。因此，本文提出了一种用于肝脏肿瘤分割的多尺度特征注意网络(MS-FANet)。在MS-FANet的编码器中引入了新颖的残余注意力（RA）块和多尺度下采样（MAD），以充分学习可变的肿瘤特征并同时提取不同尺度的肿瘤特征。在特征缩减过程中引入了双路特征(DF)滤波器和密集上采样(DU),以减少有效特征,实现肝肿瘤的精确分割。在公共LiTS数据集和3DIRCADb数据集上，MS-FANet达到平均骰子的74.2%和78.0%，分别,优于大多数最先进的网络，这有力地证明了优秀的肝肿瘤分割性能和学习不同尺度特征的能力。
