Examensarbeten för masterexamen // Master Theses
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Browsar Examensarbeten för masterexamen // Master Theses efter Program "Biomedical engineering (MPBME), MSc"
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- PostLiver Tumor Segmentation Using Classical Algorithms & Deep Learning(2023) Allgöwer, Sofie; Ljungdahl, Sofia; Chalmers tekniska högskola / Institutionen för matematiska vetenskaper; Modin, Klas; Bodin, CarlLiver 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.
- PostNon-functional requirements testing in the Medtech industry(2021) Martinsson, Marcus; Nordeman, Per; Chalmers tekniska högskola / Institutionen för matematiska vetenskaper; Lundh, Torbjörn; Torkar, Richard; Stein, CarlAlthough software testing is widely used during the software development phase of medical technologies (Medtech), there is no common ground regarding identification, separation, and testing of functional and non-functional requirements within the development cycle. The objective of this thesis was to implement a taxonomy for non-functional requirements within the Medtech industry, and to build an automated test framework including tests for non-functional requirements linked to user experience. The methodology used for the thesis was action research, iterated through a research, construction and a simulation phase for each test. Information gathered regarding test development was collected through interviews, documents, experience, and prior work. A taxonomy for identifying, separating, and prioritizing software requirements was developed. Further, an automated test framework was developed which included automated tests evaluating the reliability, performance, scalability, and portability of the software system. Two reliability tests were developed to evaluate the stability and placement of medical tools within the simulation. An additional test was developed evaluating the performance, scalability, and portability of the software system. It was shown that automated tests can detect and notify software developers and project managers with information regarding non-functional requirements of their software system. Although non-functional requirements often can be difficult to comprehend, the result within this thesis suggests that there is great value in identifying, classifying, and testing non-functional requirements within the Medtech software development cycle to secure a satisfied end-user.