论文标题
基于3D CT图像的肠道区域分割，可视化肠道以诊断为肠胃
Visualizing intestines for diagnostic assistance of ileus based on intestinal region segmentation from 3D CT images
论文作者
论文摘要
本文提出了一种可视化肠道（小肠和大肠）区域及其由CT体积引起的膨胀部位的可视化方法。由于非专家临床医生很难找到狭窄的部位，因此应直观地将肠道及其狭窄的部件视为可视化。此外，很难细分回肠病例的肠道区域。提出的方法段肠道区域由3D FCN（3D U-NET）。由于内部肠内充满了液体，因此在肠病例中很难分割肠道区域。这些流体在3D CT体积上具有相似的强度，并具有肠壁。我们通过使用通过弱注释方法训练的3D U-NET分割肠道区域。虚弱的通道使得有可能使用小肠手动跟踪的标签图像训练3D U-NET。这避免了我们准备长时间且曲折形状的肠道的许多注释标签。每个肠段均基于距端点渲染端点的距离进行体积渲染和颜色。在这种可视化中，可以轻松地识别狭窄的零件（肠段的分离点）。在实验中，我们表明狭窄的部分被直观地可视化为分割区域的终点，这些区域被红色或蓝色颜色。
This paper presents a visualization method of intestine (the small and large intestines) regions and their stenosed parts caused by ileus from CT volumes. Since it is difficult for non-expert clinicians to find stenosed parts, the intestine and its stenosed parts should be visualized intuitively. Furthermore, the intestine regions of ileus cases are quite hard to be segmented. The proposed method segments intestine regions by 3D FCN (3D U-Net). Intestine regions are quite difficult to be segmented in ileus cases since the inside the intestine is filled with fluids. These fluids have similar intensities with intestinal wall on 3D CT volumes. We segment the intestine regions by using 3D U-Net trained by a weak annotation approach. Weak-annotation makes possible to train the 3D U-Net with small manually-traced label images of the intestine. This avoids us to prepare many annotation labels of the intestine that has long and winding shape. Each intestine segment is volume-rendered and colored based on the distance from its endpoint in volume rendering. Stenosed parts (disjoint points of an intestine segment) can be easily identified on such visualization. In the experiments, we showed that stenosed parts were intuitively visualized as endpoints of segmented regions, which are colored by red or blue.
