# Contrast quality control for segmentation task based on deep learning models—Application to stroke lesion in CT imaging

## TL;DR

This paper introduces a method to assess image contrast quality in CT scans for stroke lesion segmentation, improving model training efficiency and performance.

## Contribution

A novel contrast quality control method using Fisher's ratio and ML to identify and exclude low-contrast images for better segmentation.

## Key findings

- A Fisher's ratio threshold of 0.05 was identified for effective stroke lesion segmentation in CT images.
- Excluding low-contrast images reduced training time by 30% while maintaining model performance.
- The model trained without low-contrast images performed equally well on a different database.

## Abstract

Although medical imaging plays a crucial role in stroke management, machine learning (ML) has been increasingly used in this field, particularly in lesion segmentation. Despite advances in acquisition technologies and segmentation architectures, one of the main challenges of subacute stroke lesion segmentation in computed tomography (CT) imaging is image contrast.

To address this issue, we propose a method to assess the contrast quality of an image dataset with a ML trained model for segmentation. This method identifies the critical contrast level below which the medical-imaging model fails to learn meaningful content from images. Contrast measurement relies on the Fisher's ratio, estimating how well the stroke lesion is contrasted from the background. The critical contrast is found-thanks to the following three methods: Performance, graphical, and clustering analysis. Defining this threshold improves dataset design and accelerates training by excluding low-contrast images.

Application of this method to brain lesion segmentation in CT imaging highlights a Fisher's ratio threshold value of 0.05, and training validation of a new model without these images confirms this with similar results with only 60% of the training data, resulting in an almost 30% reduction in initial training time. Moreover, the model trained without the low-contrast images performed equally well with all images when tested on another database.

This study opens discussion with clinicians concerning the limitations, areas for improvement, and strategies for enhancing datasets and training models. While the methodology was only applied to stroke lesion segmentation in CT images, it has the potential to be adapted to other tasks.