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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Cross Modality Microscopy Segmentation via Adversarial Adaptation.

Yue Guo¹, Qian Wang², Oleh Krupa³

¹Renaissance Computing Institute, Chapel Hill, NC, USA.

Bioinformatics and Biomedical Engineering : 7Th International Work-Conference, IWBBIO 2019, Granada, Spain, May 8-10, 2019, Proceedings, Parts I and II. IWBBIO (Conference) (7Th : 2019 : Granada, Spain)

|March 11, 2020

Summary

This summary is machine-generated.

This study introduces an adversarial adaptation method for cell segmentation in microscopy images. It enables knowledge transfer between imaging types, reducing the need for extensive manual data labeling.

Keywords:

Generative adversarial networks Microscopy segmentation Transfer learning

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Area of Science:

* Computational Biology
* Image Analysis
* Microscopy

Background:

* Deep learning excels at cell segmentation in microscopy.
* Training deep learning models requires substantial manual annotation, which is labor-intensive.
* Transferring models to new imaging modalities is challenging due to data scarcity.

Purpose of the Study:

* To develop a method for transferring deep learning segmentation models between microscopy imaging modalities.
* To overcome the limitation of requiring large labeled datasets for new imaging techniques.
* To enable rapid development of segmentation solutions for emerging microscopy methods.

Main Methods:

* Implemented an adversarial adaptation technique.
* Adapted deep network knowledge from a source imaging modality (confocal microscopy) to a target modality (light sheet microscopy).
* Utilized limited or no labeled data in the target modality.

Main Results:

* Achieved promising cell segmentation results in the target imaging modality.
* Demonstrated the effectiveness of transfer learning for microscopy image segmentation.
* Validated the adversarial adaptation method for cross-modality knowledge transfer.

Conclusions:

* Adversarial adaptation is a viable strategy for cross-modality transfer learning in microscopy.
* This approach significantly reduces the effort needed for training segmentation models on new imaging techniques.
* Facilitates the application of deep learning to a wider range of microscopy data.