# Manual segmentation of opacities and consolidations on CT of long COVID patients from multiple annotators

## TL;DR

This paper introduces a new public dataset of lung CT scans from Long COVID patients, with manual annotations for opacities and consolidations by multiple experts.

## Contribution

The first public dataset of manual segmentations for ground glass opacities and consolidations in Long COVID patients is introduced.

## Key findings

- The dataset includes 90 axial slices with manual annotations from three independent experts.
- The dataset provides both consensus and individual annotations for a total of 360 slices.
- It serves as a resource for training and validating automated segmentation methods and studying interrater variability.

## Abstract

The field of supervised automated medical imaging segmentation suffers from relatively small datasets with ground truth labels. This is especially true for challenging segmentation problems that target structures with low contrast and ambiguous boundaries, such as ground glass opacities and consolidation in chest computed tomography images. In this work, we make available the first public dataset of ground glass opacity and consolidation in the lungs of Long COVID patients. The Long COVID Iowa-UNICAMP dataset (LongCIU) was built by three independent expert annotators, blindly segmenting the same 90 selected axial slices manually, without using any automated initialization. The public dataset includes the final consensus segmentation in addition to the individual segmentation from each annotator (360 slices total). This dataset is a valuable resource for training and validating new automated segmentation methods and for studying interrater uncertainty in the segmentation of lung opacities in computed tomography.