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Deep learning-based medical image segmentation with limited labels.

Weicheng Chi^1,2, Lin Ma¹, Junjie Wu¹

¹Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.

Physics in Medicine and Biology

|October 21, 2020

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This study introduces a novel weakly supervised deep learning method for medical image segmentation, using deformable image registration (DIR) to generate labels from unlabeled data. This approach significantly improves organ delineation accuracy in radiotherapy with limited annotated datasets.

Area of Science:

Radiotherapy
Medical Imaging
Artificial Intelligence

Background:

Deep learning (DL) auto-segmentation requires extensive labeled data for accurate radiotherapy organ delineation.
Medical image annotation is labor-intensive, time-consuming, and requires specialized clinical expertise.
Unlabeled medical images are readily available, presenting an opportunity to leverage this data.

Purpose of the Study:

To develop a weakly supervised DL approach for medical image segmentation using limited labeled data.
To leverage abundant unlabeled data by generating pseudo-contours via deformable image registration (DIR).
To train a robust DL segmentation model that overcomes the limitations of scarce annotated medical images.

Main Methods:

Proposed a weakly supervised DL training strategy utilizing DIR-based annotations.

Keywords:

deep learning deformable image registration limited labels segmentation

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Generated pseudo-contours by propagating atlas contours to unlabeled CT scans using DIR.

Trained a DL segmentation model using a combination of limited labeled and abundant pseudo-labeled data.

Main Results:

Achieved high Dice similarity coefficients (e.g., 87.9% for mandible) on the TCIA test set.
Demonstrated competitive performance on institutional and third-party (PDDCA) datasets.
Outperformed traditional multi-atlas DIR methods and fully supervised approaches trained with limited data.

Conclusions:

The proposed weakly supervised DL method effectively utilizes unlabeled data for medical image segmentation.
This approach shows significant promise for DL-based applications in radiotherapy requiring organ delineation with limited annotated data.
The DIR-based pseudo-contour generation is a viable strategy to enhance DL model robustness and accuracy.