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.

翻译：自动化分割工具在处理不同病理类型的图像时，常面临准确性与适应性问题。本研究旨在探索构建一种工作流，以实现将图像高效路由至针对性训练的分割模型的可行性。通过部署深度学习分类器自动对图像进行分类并路由至相应分割模型，我们期望该工作流能准确分割不同病理类型的图像。本研究采用的数据包括350例多囊性肝病患者的CT图像，以及350例结直肠癌肝转移患者的CT图像。所有图像的肝脏区域均由经过专业培训的影像分析人员手动标注。与通用单一分割模型相比，我们提出的自适应分割工作流在全肝分割任务中实现了统计学意义上的显著提升（非参数Wilcoxon符号秩检验，n=100，p值<<0.001）。该方法可适用于多种场景，有望在分割流程的临床应用中发挥重要作用。