Segmentation of Liver Tumours Using Artificial Intelligence
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Authors
Type
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Programme
Model builders
Journal Title
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Volume Title
Publisher
Abstract
Liver cancer is a serious health condition affecting approximately 800,000 people
annually, with around 700,000 deaths worldwide each year from this disease. One
of the primary treatment methods for liver cancer is the surgical removal of the
tumour. Keyhole surgery, which uses small incisions instead of a large cut, offers
several benefits, including shorter hospital stays, reduced risk of complications, and
cosmetic advantages such as smaller scars. However, despite its advantages, keyhole
surgery is less commonly performed. Navari Surgical, a Medtech startup company
founded in 2021, aims to address this challenge by developing a visual aid specifically
for keyhole surgeries involving liver tumour removal, benefiting both patients
and the healthcare system by improving outcomes and efficiency.
This Master’s thesis investigates deep learning networks for semantic segmentation
of liver tumours in CT images. This includes comparing the performance of transfer
learning networks with the U-Net architecture. This analysis highlights the
importance of careful dataset preparation, thoughtful model selection, and hyperparameter
tuning to optimise model performance.
In conclusion, U-Net demonstrated the best performance compared to the transfer
learning networks, especially when prioritising the Dice score over recall. The
recommendation for Navari is to further develop the U-Net architecture for future
advancements in tumour segmentation. Alternatively, make small adjustments to
the transfer learning networks that may lead to better performance.
Description
Keywords
Liver tumour, Navari, Deep learning, LiTS dataset, U-Net, transfer learning, YOLO, SAM, VGG16