基于分类的自适应分割流水线：利用多囊肝病与结直肠癌肝转移CT图像的可行性研究
A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images
AI总结：
本研究提出基于深度学习分类器的自适应分割流水线，将不同病理CT图像自动路由至专门训练的分割模型，并在多囊肝病与结直肠癌肝转移数据上验证其相较单一模型显著提升肝脏分割精度。
摘要：
自动化分割工具在应用于不同病理图像时，常常面临准确性和适应性方面的问题。本研究旨在探索构建一种工作流程的可行性，该流程能够高效地将图像路由至专门训练的分割模型。通过部署一个深度学习分类器来自动对图像进行分类，并将其路由至相应的分割模型，我们希望所提出的工作流程能够准确分割具有不同病理表现的图像。本研究使用的数据包括350张来自多囊肝病患者的CT图像，以及350张来自结直肠癌肝转移患者的CT图像。所有图像中的肝脏均由训练有素的影像分析人员手动分割。与通用的单一分割模型相比，我们提出的自适应分割工作流程在总体肝脏分割任务上取得了统计学上显著的改进（非参数Wilcoxon符号秩检验，n=100，p值远小于0.001）。该方法适用于广泛的场景，并有望在分割流水线的临床实施中发挥重要作用。
英文摘要：
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.
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