Real Time Multi Organ Classification on Computed Tomography Images

TL;DR

This paper introduces a real-time, classifier-based method for organ identification in CT images that offers faster performance than traditional segmentation techniques, suitable for clinical automation.

Contribution

The study presents a novel classifier approach that enables rapid organ labeling and full segmentation at any resolution, improving efficiency over existing methods.

Findings

Outperforms existing segmentation methods in speed

Provides real-time organ classification in CT images

Enables flexible resolution querying for segmentation

Abstract

Organ segmentation is a fundamental task in medical imaging since it is useful for many clinical automation pipelines. However, some tasks do not require full segmentation. Instead, a classifier can identify the selected organ without segmenting the entire volume. In this study, we demonstrate a classifier based method to obtain organ labels in real time by using a large context size with a sparse data sampling strategy. Although our method operates as an independent classifier at query locations, it can generate full segmentations by querying grid locations at any resolution, offering faster performance than segmentation algorithms. We compared our method with existing segmentation techniques, demonstrating its superior runtime potential for practical applications in medical imaging.