MIMO U-Net: Efficient Cell Segmentation and Counting in Microscopy Image Sequences

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11140%2F23%3A10469133" target="_blank" >RIV/00216208:11140/23:10469133 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1117/12.2655627" target="_blank" >https://doi.org/10.1117/12.2655627</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.2655627" target="_blank" >10.1117/12.2655627</a>

Jazyk výsledku

angličtina

Název v původním jazyce

MIMO U-Net: Efficient Cell Segmentation and Counting in Microscopy Image Sequences

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

MIMO U-Net: Efficient Cell Segmentation and Counting in Microscopy Image Sequences

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30100 - Basic medicine

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

MEDICAL IMAGING 2023

ISBN

978-1-5106-6047-2

ISSN

1605-7422

e-ISSN

—

Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

SPIE-INT SOC OPTICAL ENGINEERING

Místo vydání

BELLINGHAM

Místo konání akce

San Diego

Datum konání akce

19. 2. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001011463700025