A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI Data

TL;DR

This paper introduces an open-source toolkit for efficient foreground and anonymization area segmentation in 3D CT and MRI images, enhancing data preprocessing for self-supervised learning by improving privacy and computational efficiency.

Contribution

The study presents a unified segmentation framework and open-source toolkit that improves data sampling and anonymization detection for SSL in 3D medical imaging.

Findings

Achieves mean Dice scores over 98.5% for anonymization segmentation.

Surpasses 99.5% Dice score for foreground segmentation.

Demonstrates robustness across various anonymization methods.

Abstract

This study presents an open-source toolkit to address critical challenges in preprocessing data for self-supervised learning (SSL) for 3D medical imaging, focusing on data privacy and computational efficiency. The toolkit comprises two main components: a segmentation network that delineates foreground regions to optimize data sampling and thus reduce training time, and a segmentation network that identifies anonymized regions, preventing erroneous supervision in reconstruction-based SSL methods. Experimental results demonstrate high robustness, with mean Dice scores exceeding 98.5 across all anonymization methods and surpassing 99.5 for foreground segmentation tasks, highlighting the efficacy of the toolkit in supporting SSL applications in 3D medical imaging for both CT and MRI images. The weights and code is available at https://github.com/MIC-DKFZ/Foreground-and-Anonymization-Area-Segmentation.