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

TL;DR

This study presents a workflow that uses deep learning classification to route CT images to specialized segmentation models, improving accuracy in segmenting livers affected by different pathologies.

Contribution

It introduces an adaptive segmentation pipeline that automatically classifies images and applies pathology-specific models, enhancing segmentation accuracy over generic models.

Findings

Significant improvement in liver segmentation accuracy

Workflow effectively routes images to appropriate models

Applicable to diverse clinical scenarios

Abstract

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.