SGSeg: Enabling Text-free Inference in Language-guided Segmentation of Chest X-rays via Self-guidance

TL;DR

This paper introduces SGSeg, a novel framework that enables effective lung X-ray segmentation using language guidance during training but operates without text during inference, improving clinical decision support workflows.

Contribution

SGSeg is the first method to allow text-free inference in language-guided segmentation by leveraging a self-guidance approach with a novel localization-enhanced report generation module.

Findings

SGSeg outperforms existing uni-modal segmentation methods.

SGSeg closely matches multi-modal language-guided segmentation performance.

Extensive experiments validate the effectiveness of SGSeg on the QaTa-COV19 dataset.

Abstract

Segmentation of infected areas in chest X-rays is pivotal for facilitating the accurate delineation of pulmonary structures and pathological anomalies. Recently, multi-modal language-guided image segmentation methods have emerged as a promising solution for chest X-rays where the clinical text reports, depicting the assessment of the images, are used as guidance. Nevertheless, existing language-guided methods require clinical reports alongside the images, and hence, they are not applicable for use in image segmentation in a decision support context, but rather limited to retrospective image analysis after clinical reporting has been completed. In this study, we propose a self-guided segmentation framework (SGSeg) that leverages language guidance for training (multi-modal) while enabling text-free inference (uni-modal), which is the first that enables text-free inference in language-guided segmentation. We exploit the critical location information of both pulmonary and pathological structures depicted in the text reports and introduce a novel localization-enhanced report generation (LERG) module to generate clinical reports for self-guidance. Our LERG integrates an object detector and a location-based attention aggregator, weakly-supervised by a location-aware pseudo-label extraction module. Extensive experiments on a well-benchmarked QaTa-COV19 dataset demonstrate that our SGSeg achieved superior performance than existing uni-modal segmentation methods and closely matched the state-of-the-art performance of multi-modal language-guided segmentation methods.