MIMO YOLO - A Multiple Input Multiple Output Model for Automatic Cell Counting

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11140%2F23%3A10471224" target="_blank" >RIV/00216208:11140/23:10471224 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/CBMS58004.2023.00327" target="_blank" >https://doi.org/10.1109/CBMS58004.2023.00327</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CBMS58004.2023.00327" target="_blank" >10.1109/CBMS58004.2023.00327</a>

Result language

angličtina

Original language name

MIMO YOLO - A Multiple Input Multiple Output Model for Automatic Cell Counting

Original language description

Across basic research studies, cell counting requires significant human time and expertise. Trained experts use thin focal plane scanning to count (click) cells in stained biological tissue. This computer-assisted process (optical disector) requires a well-trained human to select a unique best z-plane of focus for counting cells of interest. Though accurate, this approach typically requires an hour per case and is prone to inter- and intra-rater errors. Our group has previously proposed deep learning (DL)-based methods to automate these counts using cell segmentation at high magnification. Here we propose a novel You Only Look Once (YOLO) model that performs cell detection on multi-channel z-plane images (disector stack). This automated Multiple Input Multiple Output (MIMO) version of the optical disector method uses an entire z-stack of microscopy images as its input, and outputs cell detections (counts) with a bounding box of each cell and class corresponding to the z-plane where the cell appears in best focus. Compared to the previous segmentation methods, the proposed method does not require time- and labor-intensive ground truth segmentation masks for training, while producing comparable accuracy to current segmentation-based automatic counts. The MIMO-YOLO method was evaluated on systematic-random samples of NeuN-stained tissue sections through the neocortex of mouse brains (n=7). Using a cross validation scheme, this method showed the ability to correctly count total neuron numbers with accuracy close to human experts and with 100% repeatability (Test-Retest).

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

30100 - Basic medicine

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Article name in the collection

2023 IEEE 36TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, CBMS

ISBN

979-8-3503-1224-9

ISSN

2372-9198

e-ISSN

—

Number of pages

5

Pages from-to

827-831

Publisher name

IEEE COMPUTER SOC

Place of publication

LOS ALAMITOS

Event location

LAquila

Event date

Jun 22, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001037777900145