Tracking plankton using neural networks trained on simulated images
Examensarbete för masterexamen
Please use this identifier to cite or link to this item:
Bibliographical item details
|Type: ||Examensarbete för masterexamen|
|Title: ||Tracking plankton using neural networks trained on simulated images|
|Authors: ||Fransson, Agaton|
|Abstract: ||Softwares to track particles often use algorithmic approaches to detect particles and
to create tracks using the found positions, requiring human fine-tuning of parameters
to achieve sought-for results. This can be time consuming and difficult, while
also creating opportunities for human error and bias. With the developments of
computational power and machine learning techniques such as deep learning, data
driven approaches have made their way into many fields of science. Barriers preventing
advances of such methods are the lack of available training data within a field
and the level of proficiency required to create custom machine learning solutions.
DeepTrack 2.0 is a software providing us with means to simulate digital microscopy
images, build and train neural networks such as U-nets. In this paper DeepTrack 2.0
is utilized and built on to fit the needs of marine biologists when tracking plankton.
Here I show that DeepTrack 2.0 provides us with the tools necessary to detect and
track different types of plankton filmed in a variety of conditions with performance
on par with and with the potential to outperform conventional tracking softwares.
I also show that for plankton in a messy environment moving uniformly a network
trained to detect motion rather than a shape proves more successful. These results
demonstrate the versatility of deep learning methods and the potential of training
networks on simulations for applications on real data, as is the case for marine biologists
studying plankton. They also show the impact the structure of the training
data has on the nature of the network.|
|Keywords: ||deep learning;U-net;digital microscopy;deeptrack;fiji trackmate|
|Issue Date: ||2021|
|Publisher: ||Chalmers tekniska högskola / Institutionen för fysik|
|Collection:||Examensarbeten för masterexamen // Master Theses|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.