Anatomy-guided Pathology Segmentation

TL;DR

This paper introduces a novel anatomy-guided segmentation model that jointly considers anatomical structures and pathological features, significantly improving pathology segmentation accuracy in medical imaging.

Contribution

We propose the APEx training framework with a query-based transformer that integrates anatomical and pathological information for enhanced segmentation performance.

Findings

Achieved state-of-the-art results on FDG-PET-CT and Chest X-Ray datasets.

Improved segmentation accuracy by up to 3.3% over strong baselines.

Demonstrated the effectiveness of anatomy-informed pathology predictions.

Abstract

Pathological structures in medical images are typically deviations from the expected anatomy of a patient. While clinicians consider this interplay between anatomy and pathology, recent deep learning algorithms specialize in recognizing either one of the two, rarely considering the patient's body from such a joint perspective. In this paper, we develop a generalist segmentation model that combines anatomical and pathological information, aiming to enhance the segmentation accuracy of pathological features. Our Anatomy-Pathology Exchange (APEx) training utilizes a query-based segmentation transformer which decodes a joint feature space into query-representations for human anatomy and interleaves them via a mixing strategy into the pathology-decoder for anatomy-informed pathology predictions. In doing so, we are able to report the best results across the board on FDG-PET-CT and Chest X-Ray pathology segmentation tasks with a margin of up to 3.3% as compared to strong baseline methods. Code and models will be publicly available at github.com/alexanderjaus/APEx.