Abstract
Accurate three-dimensional delineation of liver tumors on contrast-enhanced CT is a prerequisite for treatment planning, navigation and response assessment, yet manual contouring is slow, observer-dependent and difficult to standardise across centres. Automatic segmentation is complicated by low lesion-parenchyma contrast, blurred or incomplete boundaries, heterogeneous enhancement patterns, and confounding structures such as vessels and adjacent organs. We propose a hybrid framework that couples an attention-enhanced cascaded U-Net with handcrafted radiomics and voxel-wise 3D CNN refinement for joint liver and liver-tumor segmentation. First, a 2.5D two-stage network with a densely connected encoder, sub-pixel convolution decoders and multi-scale attention gates produces initial liver and tumor probability maps from short stacks of axial slices. Inter-slice temporal consistency is then enforced by a simple three-slice refinement rule along the cranio-caudal direction, which restores thin and tiny lesions while suppressing isolated noise. Next, 728 radiomic descriptors spanning intensity, texture, shape, boundary and wavelet feature groups are extracted from candidate lesions and reduced to 20 stable, highly informative features via multi-strategy feature selection; a random forest classifier uses these features to reject false-positive regions. Finally, a compact 3D patch-based CNN derived from AlexNet operates in a narrow band around the tumor boundary to perform voxel-level relabelling and contour smoothing.
Abstract (translated)
在对比增强CT（CECT）上精确描绘肝脏肿瘤的三维轮廓是治疗计划、导航和反应评估的前提条件，然而手动勾画过程缓慢、依赖观察者且难以跨中心标准化。自动分割则因低病变-肝实质对比度、模糊或不完全边界、异质性强化模式以及血管和其他相邻器官等混淆结构而变得复杂。 我们提出了一种结合注意力增强的级联U-Net和手工设计的放射组学特征及基于体素的3D卷积神经网络（CNN）精细调整的混合框架，用于同时进行肝脏和肝脏肿瘤分割。首先，一个2.5D两阶段网络使用密集连接编码器、亚像素卷积解码器以及多尺度注意力门从短轴切片堆栈中生成初步的肝脏和肿瘤概率图。 接着，在头尾方向上通过简单的三片精细规则强制执行片间时间一致性，以恢复细微且小的病灶同时抑制孤立噪声。接下来，从候选病灶中提取728个放射组学描述符（包括强度、纹理、形状、边界以及小波特征分组），并通过多策略特征选择减少到20个稳定而高度信息量丰富的特征；随机森林分类器使用这些特征来拒绝假阳性区域。 最后，基于AlexNet的紧凑3D补丁网络在肿瘤边界的狭窄带内执行体素级别的重新标记和轮廓平滑操作。
URL
https://arxiv.org/abs/2512.07574
