UNETRIS: transformer-based nuclear instance segmentation for three-dimensional fluorescence microscopy images

Alain Chen; Liming Wu; Seth Winfree; Kenneth W. Dunn; Paul Salama; Edward J. Delp

doi:10.1117/12.3005440

2 April 2024 UNETRIS: transformer-based nuclear instance segmentation for three-dimensional fluorescence microscopy images

Alain Chen, Liming Wu, Seth Winfree, Kenneth W. Dunn, Paul Salama, Edward J. Delp

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129261U (2024) https://doi.org/10.1117/12.3005440
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Automated cellular nuclei segmentation is often an important step for digital pathology and other analyses such as computer aided diagnosis. Most existing machine learning methods for microscopy image analysis require postprocessing such as watershed transform or connected component analysis to obtain instance segmentation from semantic segmentation results. This becomes prohibitively expensive computationally especially when used with 3D microscopy volumes. UNet Transformers for Instance Segmentation (UNETRIS) is proposed to eliminate the postprocessing steps necessary for nuclei instance segmentation in 3D microscopy images. UNETRIS, an extension of UNETR which utilizes a transformer as the encoder in the successful “U-shaped” network design for the encoder-decoder structure of U-Net, uses additional transformers to separate individual instances of cell nuclei directly during the inference step without the need for expensive postprocessing steps. UNETRIS does not require but can use manual ground truth annotations for training. UNETRIS was tested on a variety of microscopy volumes collected from multiple regions of organ tissues.

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

Citation Download Citation

Alain Chen, Liming Wu, Seth Winfree, Kenneth W. Dunn, Paul Salama, and Edward J. Delp "UNETRIS: transformer-based nuclear instance segmentation for three-dimensional fluorescence microscopy images", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129261U (2 April 2024); https://doi.org/10.1117/12.3005440

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available