Liver Tumor Segmentation Using Classical Algorithms & Deep Learning
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Biomedical engineering (MPBME), MSc
Publicerad
2023
Författare
Allgöwer, Sofie
Ljungdahl, Sofia
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Liver cancer is a common condition that traditionally required open surgery, posing
a high risk of complications. Laparoscopic surgery has become increasingly popular,
but comes with navigation challenges. The MedTech start-up Navari Surgical has
developed a visualization solution using augmented reality, and this project aims to
suggest a tumor segmentation method to support this solution.
Previous studies have inspired this work to explore tumor segmentation utilizing
different approaches, such as thresholding algorithms, active contour models, and a
deep learning model utilizing the U-Net architecture. Thresholding methods uses
pixel intensities, active contour models focuses on minimizing image energy, and
U-Net models learn image features through training. For the U-Net models, variations in the learning rate, augmented data quantity, and loss functions are explored.
The study utilizes the open-source LiTS dataset. The methods employ either liversegmented or cropped tumor images as inputs. Evaluation metrics include dice’s
similarity coefficient (DSC) and recall, with a dataset of 107 images for evaluation
of the classical algorithms, and 696 test images for the U-Net models.
The obtained results demonstrate that thresholding algorithms with cropped input
yield the highest DSC and recall values for the classical algorithms. The best performance was observed with cropped Multi Otsu (DSC: 0.435, recall: 0.605). For the
U-Net models, increased augmented data, reduced learning rate, and more epochs
resulted in improved performance. The best U-Net model achieved a DSC of 0.766
and a recall of 0.796.
The discussion highlights challenges with algorithms designed for single tumor detection when evaluating datasets containing multiple tumors per image. Classical
algorithms show a need for individualization for each scan, impacting automation
and efficiency. Overfitting is a concern for the U-Net models, suggesting room for
improvement. Further enhancements include pre and post-processing techniques,
parameter variation, exploration of modified architectures, and utilization of 3D input data.
In conclusion, U-Net demonstrated the best performance among the methods explored. However, its performance is not yet suitable for practical use, requiring
further improvements. The recommendation for Navari is to continue to explore
deep learning and U-Net for future advancements in tumor segmentation.
Beskrivning
Ämne/nyckelord
Liver tumor, augmented reality, image segmentation, LiTS dataset, thresholding, active contour models, U-Net, dice similarity coefficient, recall value