Deep learning segmentation of endothelial cell images using an active learning paradigm with guided label corrections

Naomi Joseph; Ian Marshall; Elizabeth Fitzpatrick; Harry J. Menegay; Jonathan H. Lass; Beth Ann M. Benetz; David L. Wilson

doi:10.1117/1.JMI.11.1.014006

5 January 2024 Deep learning segmentation of endothelial cell images using an active learning paradigm with guided label corrections

Naomi Joseph, Ian Marshall, Elizabeth Fitzpatrick, Harry J. Menegay, Jonathan H. Lass, Beth Ann M. Benetz, David L. Wilson

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 1, 014006 (January 2024). https://doi.org/10.1117/1.JMI.11.1.014006

Abstract

Purpose

To create Guided Correction Software for informed manual editing of automatically generated corneal endothelial cell (EC) segmentations and apply it to an active learning paradigm to analyze a diverse set of post-keratoplasty EC images.

Approach

An original U-Net model trained on 130 manually labeled post-Descemet stripping automated endothelial keratoplasty (EK) images was applied to 841 post-Descemet membrane EK images generating “uncorrected” cell border segmentations. Segmentations were then manually edited using the Guided Correction Software to create corrected labels. This dataset was split into 741 training and 100 testing EC images. U-Net and DeepLabV3+ were trained on the EC images and the corresponding uncorrected and corrected labels. Model performance was evaluated in a cell-by-cell analysis. Evaluation metrics included the number of over-segmentations, under-segmentations, correctly identified new cells, and endothelial cell density (ECD).

Results

Utilizing corrected segmentations for training U-Net and DeepLabV3+ improved their performance. The average number of over- and under-segmentations per image was reduced from 23 to 11 with the corrected training set. Predicted ECD values generated by networks trained on the corrected labels were not significantly different than the ground truth counterparts (p=0.02, paired t-test). These models also correctly segmented a larger percentage of newly identified cells. The proposed Guided Correction Software and semi-automated approach reduced the time to accurately segment EC images from 15 to 30 to 5 min, an ∼80% decrease compared to manual editing.

Conclusions

Guided Correction Software can efficiently label new training data for improved deep learning performance and generalization between EC datasets.

Citation Download Citation

Naomi Joseph, Ian Marshall, Elizabeth Fitzpatrick, Harry J. Menegay, Jonathan H. Lass, Beth Ann M. Benetz, and David L. Wilson "Deep learning segmentation of endothelial cell images using an active learning paradigm with guided label corrections," Journal of Medical Imaging 11(1), 014006 (5 January 2024). https://doi.org/10.1117/1.JMI.11.1.014006

Received: 16 January 2023; Accepted: 11 December 2023; Published: 5 January 2024

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KEYWORDS

Image segmentation

Education and training

Deep learning

Active learning

Data modeling

Performance modeling

Image analysis

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