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Machine learning framework for maxillofacial preoperative planning

Botvidsson, Jakob LU (2022) EEML05 20221
Department of Biomedical Engineering
Abstract
Artificial intelligence in biomedical image processing is approaching human performance at object localization while saving immense amounts of time for the physicians. These AI algorithms have the potential to automatically segment anatomical structures for preoperative planning. However, there are currently no tools such tools on the market. This study propose a framework of generating effective machine learning algorithms, applicable on different anatomical structures, to be used to increase automation in virtual surgical planning software. In this study a limited data set consisting of 34 CT image volumes was used to generate labelled training data to a Convolutional Neural Network (CNN) called Unet. The networks were evaluated with... (More)
Artificial intelligence in biomedical image processing is approaching human performance at object localization while saving immense amounts of time for the physicians. These AI algorithms have the potential to automatically segment anatomical structures for preoperative planning. However, there are currently no tools such tools on the market. This study propose a framework of generating effective machine learning algorithms, applicable on different anatomical structures, to be used to increase automation in virtual surgical planning software. In this study a limited data set consisting of 34 CT image volumes was used to generate labelled training data to a Convolutional Neural Network (CNN) called Unet. The networks were evaluated with metric evaluation as well as visually evaluated. The framework produced two networks for automatic segmentation, one for the orbital bone and one for the mandibular bone. The orbital automation made useful segmentations ready for 3D printing while the mandible automation needs more work to be able to make printable segmentations. In conclusion this framework provides a viable approach of generating anatomical models for virtual surgical planning. (Less)
Please use this url to cite or link to this publication:
author
Botvidsson, Jakob LU
supervisor
organization
alternative title
Maskininlärningsramverk för maxillofacial preoperativ planering
course
EEML05 20221
year
type
M2 - Bachelor Degree
subject
keywords
Machine learning, AI, CNN, U-net
language
English
id
9094289
date added to LUP
2022-06-30 13:25:02
date last changed
2022-06-30 13:25:02
@misc{9094289,
  abstract     = {{Artificial intelligence in biomedical image processing is approaching human performance at object localization while saving immense amounts of time for the physicians. These AI algorithms have the potential to automatically segment anatomical structures for preoperative planning. However, there are currently no tools such tools on the market. This study propose a framework of generating effective machine learning algorithms, applicable on different anatomical structures, to be used to increase automation in virtual surgical planning software. In this study a limited data set consisting of 34 CT image volumes was used to generate labelled training data to a Convolutional Neural Network (CNN) called Unet. The networks were evaluated with metric evaluation as well as visually evaluated. The framework produced two networks for automatic segmentation, one for the orbital bone and one for the mandibular bone. The orbital automation made useful segmentations ready for 3D printing while the mandible automation needs more work to be able to make printable segmentations. In conclusion this framework provides a viable approach of generating anatomical models for virtual surgical planning.}},
  author       = {{Botvidsson, Jakob}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Machine learning framework for maxillofacial preoperative planning}},
  year         = {{2022}},
}