MIMO U-Net: efficient cell segmentation and counting in microscopy image sequences

Palak Dave; Yaroslav Kolinko; Hunter Morera; Kurtis Allen; Saeed Alahmari; Dmitry Goldgof; Lawrence O. Hall; Peter R. Mouton

doi:10.1117/12.2655627

6 April 2023 MIMO U-Net: efficient cell segmentation and counting in microscopy image sequences

Palak Dave, Yaroslav Kolinko, Hunter Morera, Kurtis Allen, Saeed Alahmari, Dmitry Goldgof, Lawrence O. Hall, Peter R. Mouton

Author Affiliations +

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 124710R (2023) https://doi.org/10.1117/12.2655627
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Automatic cell quantification in microscopy images can accelerate biomedical research. There has been significant progress in the 3D segmentation of neurons in fluorescence microscopy. However, it remains a challenge in bright-field microscopy due to the low Signal-to-Noise Ratio and signals from out-of-focus neurons. Automatic neuron counting in bright-field z-stacks is often performed on Extended Depth of Field images or on only one thick focal plane image. However, resolving overlapping cells that are located at different z-depths is a challenge. The overlap can be resolved by counting every neuron in its best focus z-plane because of their separation on the z-axis. Unbiased stereology is the state-of-the-art for total cell number estimation. The segmentation boundary for cells is required in order to incorporate the unbiased counting rule for stereology application. Hence, we perform counting via segmentation. We propose to achieve neuron segmentation in the optimal focal plane by posing the binary segmentation task as a multi-class multi-label task. Also, we propose to efficiently use a 2D U-Net for inter-image feature learning in a Multiple Input Multiple Output system that poses a binary segmentation task as a multi-class multi-label segmentation task. We demonstrate the accuracy and efficiency of the MIMO approach using a bright-field microscopy z-stack dataset locally prepared by an expert. The proposed MIMO approach is also validated on a dataset from the Cell Tracking Challenge achieving comparable results to a compared method equipped with memory units. Our z-stack dataset is available at https://tinyurl.com/wncfxn9m.

Conference Presentation

Citation Download Citation

Palak Dave, Yaroslav Kolinko, Hunter Morera, Kurtis Allen, Saeed Alahmari, Dmitry Goldgof, Lawrence O. Hall, and Peter R. Mouton "MIMO U-Net: efficient cell segmentation and counting in microscopy image sequences", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 124710R (6 April 2023); https://doi.org/10.1117/12.2655627

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