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Related Experiment Video

Updated: Jun 30, 2026

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
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MAE-UNETR++: Masked Autoencoder Pretraining for 3-D Lung Nodule Segmentation.

Vinayak Savant1, Yue Wang1, Jianhua Xuan1

  • 1Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA.

Biorxiv : the Preprint Server for Biology
|June 29, 2026
PubMed
Summary

Masked autoencoder (MAE) pretraining improves 3-D lung nodule segmentation accuracy, outperforming random initialization and standard transfer learning. This self-supervised learning approach enhances model performance, especially with limited labeled data.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Voxel-level annotation for 3-D medical imaging is costly and hard to scale.
  • Training high-capacity 3-D segmentation models is challenging due to data limitations.
  • Transfer learning (TL) can underperform when source and target domains differ, particularly for pulmonary nodules.

Purpose of the Study:

  • To propose and evaluate a masked autoencoder (MAE) pretraining approach for domain-specific self-supervised learning (SSL).
  • To address data efficiency challenges in 3-D lung nodule segmentation caused by domain differences.
  • To investigate the effectiveness of MAE pretraining compared to random initialization and existing TL methods.

Main Methods:

  • Implemented MAE pretraining on target-domain CT volumes for 3-D lung nodule segmentation.
Keywords:
Lung Nodule SegmentationMasked AutoencodersMedical Image SegmentationSelf-Supervised LearningUNETR++

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  • Evaluated MAE pretraining against random initialization using baseline models.
  • Compared MAE pretraining with Decathlon TL for the UNETR++ model and assessed its impact on a CNN baseline (V-Net).

    • Main Results:

    • MAE pretraining achieved a Dice Similarity Coefficient (DSC) of 0.307, surpassing random initialization (0.136) and Decathlon TL (0.257).
    • MAE pretraining significantly improved V-Net stability in low-data regimes, increasing DSC from 0.010 to 0.071.
    • Results demonstrate MAE's effectiveness in enhancing segmentation performance with limited labeled data.

    Conclusions:

    • MAE-based pretraining offers a practical and robust initialization strategy for volumetric segmentation tasks.
    • This self-supervised learning method effectively overcomes domain differences in medical imaging datasets.
    • MAE pretraining is particularly beneficial for 3-D lung nodule segmentation when labeled data are scarce.