In-depth Assessment of an Interactive Graph-based Approach for the Segmentation for Pancreatic Metastasis in Ultrasound Acquisitions of the Liver with two Specialists in Internal Medicine

TL;DR

This study evaluates an interactive graph-based algorithm for segmenting pancreatic metastasis in liver ultrasound images, demonstrating high accuracy and speed, and supporting clinical practice with two internal medicine specialists.

Contribution

It introduces and assesses a novel graph-based segmentation method for liver metastases in ultrasound, showing its effectiveness compared to manual outlining.

Findings

Up to 90% agreement with expert manual segmentation

Median DSC over 80% indicating high accuracy

Significantly faster than manual outlining

Abstract

The manual outlining of hepatic metastasis in (US) ultrasound acquisitions from patients suffering from pancreatic cancer is common practice. However, such pure manual measurements are often very time consuming, and the results repeatedly differ between the raters. In this contribution, we study the in-depth assessment of an interactive graph-based approach for the segmentation for pancreatic metastasis in US images of the liver with two specialists in Internal Medicine. Thereby, evaluating the approach with over one hundred different acquisitions of metastases. The two physicians or the algorithm had never assessed the acquisitions before the evaluation. In summary, the physicians first performed a pure manual outlining followed by an algorithmic segmentation over one month later. As a result, the experts satisfied in up to ninety percent of algorithmic segmentation results. Furthermore, the algorithmic segmentation was much faster than manual outlining and achieved a median Dice Similarity Coefficient (DSC) of over eighty percent. Ultimately, the algorithm enables a fast and accurate segmentation of liver metastasis in clinical US images, which can support the manual outlining in daily practice.