用多平面2D-U-Net+空间位置图，提升腹部器官3DCT分割精度
Multi-planar 2D-U-Net Segmentation of 3D-CT Abdominal Organs augmented by Spatial Occurrence Maps
- 通过多平面2D-U-Net结合空间出现图，增强器官定位
- 在80例数据上，分割Dice平均提升4%，最高达4%
- 适合需要高精度腹部器官分割的临床影像分析
本文提出一种轻量级基于2D-U-Net的框架，用于大规模视野3D CT扫描中五个腹部器官的分割。方法结合粗到精分割、多解剖平面预测以及额外的模糊3D空间出现图，提供解剖位置线索以提升精度。流程分为两阶段：第一阶段通过轴向遍历整个扫描，使用2D-U-Net确定目标器官的最小与最大x-y-z坐标范围；第二阶段在前一阶段划定的范围内，利用空间出现图增强多平面2D-U-Net架构。在来自多个公开来源的80例CT扫描上评估，相比不使用空间出现图的同模型，Dice系数最高提升约4%。
原文摘要 · Abstract (English)
This work proposes a lightweight 2D-U-Net-based framework for segmenting five abdominal organs in large field-of-view 3D CT scans. The method combines coarse-to-fine segmentation, predictions from multiple anatomical planes, and additional fuzzy 3D spatial maps that provide anatomical location cues to improve segmentation accuracy. We combine multi-planar 2D-U-Net models augmented by a spatial occurrence map. The approach involves two main stages. First, the abdominal volume of interest region is detected by traversing the whole scan axially with a 2D-U-Net and determining the x-y-z-minimum and -maximum extents of the 5 abdominal organs of interest. Second, we use spatial occurrence maps to enhance our multi-planar 2D-U-net architecture inside the bounds from the former stage. The method is evaluated on 80 CT scans from various public sources. The results show Dice improvements of about 4% at maximum compared to the same model trained without spatial occurrence maps.
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