Automated segmentation tools often encounter accuracy and adaptability issues
when applied to images of different pathology. The purpose of this study is to
explore the feasibility of building a workflow to efficiently route images to
specifically trained segmentation models. By implementing a deep learning
classifier to automatically classify the images and route them to appropriate
segmentation models, we hope that our workflow can segment the images with
different pathology accurately. The data we used in this study are 350 CT
images from patients affected by polycystic liver disease and 350 CT images
from patients presenting with liver metastases from colorectal cancer. All
images had the liver manually segmented by trained imaging analysts. Our
proposed adaptive segmentation workflow achieved a statistically significant
improvement for the task of total liver segmentation compared to the generic
single segmentation model (non-parametric Wilcoxon signed rank test, n=100,
p-value << 0.001). This approach is applicable in a wide range of scenarios and
should prove useful in clinical implementations of segmentation pipelines.

通过使用深度学习分类器将图像自动分类并将其路由到适当的分割模型，本研究旨在探索构建一个工作流的可行性，以高效地处理不同病理图像。我们希望通过我们的工作流能够准确地分割具有不同病理学的图像。我们的自适应分割工作流在总肝脏分割任务上与通用单一分割模型相比取得了显著的统计改进（非参数 Wilcoxon 符号秩检验，n=100，p 值 <<0.001）。这种方法适用于广泛的情景，并在分割流水线的临床实施中应该是有用的。

基于分类的自适应分割流水线：多囊肝和结直肠癌 CT 图像转移的可行性研究

A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study  Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT  Images