Self-supervised learning without annotations to improve lung chest x-ray segmentation

Jin Kim; Matthew S. Brown; Dan Ruan

doi:10.1117/12.3008582

3 April 2024 Self-supervised learning without annotations to improve lung chest x-ray segmentation

Jin Kim, Matthew S. Brown, Dan Ruan

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292737 (2024) https://doi.org/10.1117/12.3008582
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

The segmentation of chest X-ray (CXR) anatomy is a crucial yet challenging task, often subject to human errors and demanding extensive time investments. While deep learning has shown promise in medical image segmentation, its efficacy is constrained by the necessity for large annotated datasets, which are difficult and costly to generate in the medical domain. Addressing the growing need for models capable of learning from limited data, this study explores the application of Self-Supervised Learning (SSL), specifically adapting the DINO self-supervised learning framework, to leverage unannotated CXR images. Our approach, which does not rely on supervised fine-tuning, is particularly relevant for scenarios where only a small amount of annotated data is available, as is often the case at the onset of continuous learning initiatives. Employing a ConvNeXt-based architecture, our method demonstrates the potential of SSL in enhancing CXR segmentation by utilizing unannotated data, thereby reducing dependency on extensive annotated datasets. In the multilabel segmentation task, we observed increases in the average Dice Similarity Coefficient (DSC) from 0.24±0.19 to 0.56±0.22 for 3 annotated training cases. This research contributes to the evolving landscape of medical imaging analysis, offering insights into the efficient application of SSL for improving segmentation tasks in CXR images and potentially other medical imaging modalities.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jin Kim, Matthew S. Brown, and Dan Ruan "Self-supervised learning without annotations to improve lung chest x-ray segmentation", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292737 (3 April 2024); https://doi.org/10.1117/12.3008582

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