+ 
-
+
Training a CNN-based single particle tracker using simulated data
+ Training a CNN-based single particle tracker using simulated data
- Unsupervised training of a single particle tracker using LodeSTAR
+ Unsupervised training of a single particle tracker using LodeSTAR
- Training LodeSTAR to detect multiple cells from a single image
+ Training LodeSTAR to detect multiple cells from a single image
Training a UNet-based multi-particle tracker using simulated data
+ Training a UNet-based multi-particle tracker using simulated data
- Training MAGIK to trace migrating cells
+ Training MAGIK to trace migrating cells
# Basics to learn DeepTrack 2.1
@@ -91,21 +91,12 @@ Everybody learns in different ways! Depending on your preferences, and what you
## Getting-started guides
-We have two separate series of notebooks which aims to teach you all you need to know to use DeepTrack to its fullest. The first is a set of six notebooks with a focus on the application.
+We have a set of notebooks which aims to teach you all you need to know to use DeepTrack to its fullest, which consists of four notebooks with a focus on the application.
-1. deeptrack_introduction_tutorial gives an overview of how to use DeepTrack 2.1.
-2. tracking_particle_cnn_tutorial demonstrates how to track a point particle with a convolutional neural network (CNN).
-3. tracking_particle_unet_tutorial demonstrates how to track multiple particles using a U-net.
-4. characterizing_aberrations_tutorial demonstrates how to add and characterize aberrations of an optical device.
-5. distinguishing_particles_in_brightfield_tutorial demonstrates how to use a U-net to track and distinguish particles of different sizes in brightfield microscopy.
-6. analyzing_video_tutorial demonstrates how to create videos and how to train a neural network to analyze them.
-
-
-The second series focuses on individual topics, introducing them in a natural order.
-
-1. Introducing how to create simulation pipelines and train models.
-2. Demonstrating data generators.
-3. Demonstrating how to customize models using layer-blocks.
+1. tracking_particle_cnn_tutorial demonstrates how to track a point particle with a convolutional neural network (CNN).
+2. tracking_particle_unet_tutorial demonstrates how to track multiple particles using a U-net.
+3. distinguishing_particles_in_brightfield_tutorial demonstrates how to use a U-net to track and distinguish particles of different sizes in brightfield microscopy.
+4. Introducing how to create simulation pipelines and train models.
## DeepTrack 2.1 in action