From 532f934c9ee7094340d5c32182d245ade6b92f6f Mon Sep 17 00:00:00 2001
From: Maxime Deforet <53012360+maxdeff@users.noreply.github.com>
Date: Sun, 31 Mar 2024 00:01:23 +0100
Subject: [PATCH] Update README.md

add reference to tutorial
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 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 71f946b..dc34201 100644
--- a/README.md
+++ b/README.md
@@ -4,6 +4,8 @@ This repository contains python code for training the neural network.
 
 [Link to preprint](https://arxiv.org/abs/2310.19641)
 
+[Link to tutorial](https://github.com/jeanollion/bacmman/wiki/DistNet2D)
+
 Jean Ollion, Martin Maliet, Caroline Giuglaris, Elise Vacher, Maxime Deforet
 
-Extracting long tracks and lineages from videomicroscopy requires an extremely low error rate, which is challenging on complex datasets of dense or deforming cells. Leveraging temporal context is key to overcoming this challenge. We propose DistNet2D, a new deep neural network (DNN) architecture for 2D cell segmentation and tracking that leverages both mid- and long-term temporal information. DistNet2D considers seven frames at the input and uses a post-processing procedure that exploits information from the entire video to correct segmentation errors. DistNet2D outperforms two recent methods on two experimental datasets, one containing densely packed bacterial cells and the other containing eukaryotic cells. It is integrated into an ImageJ-based graphical user interface for 2D data visualization, curation, and training. Finally, we demonstrate the performance of DistNet2D on correlating the size and shape of cells with their transport properties over large statistics, for both bacterial and eukaryotic cells.
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+Extracting long tracks and lineages from videomicroscopy requires an extremely low error rate, which is challenging on complex datasets of dense or deforming cells. Leveraging temporal context is key to overcoming this challenge. We propose DistNet2D, a new deep neural network (DNN) architecture for 2D cell segmentation and tracking that leverages both mid- and long-term temporal information. DistNet2D considers seven frames at the input and uses a post-processing procedure that exploits information from the entire video to correct segmentation errors. DistNet2D outperforms two recent methods on two experimental datasets, one containing densely packed bacterial cells and the other containing eukaryotic cells. It is integrated into an ImageJ-based graphical user interface for 2D data visualization, curation, and training. Finally, we demonstrate the performance of DistNet2D on correlating the size and shape of cells with their transport properties over large statistics, for both bacterial and eukaryotic cells.