Beyond the Baseline: Investigating Failed Segmentations within the Universal Lesion Segmentation Challenge ‘23
| dc.contributor.advisor | -, - | |
| dc.contributor.author | Bok, Karlijn | |
| dc.date.issued | 2024-07-21 | |
| dc.description.abstract | Radiologists are already under significant pressure, which is expected to increase even further in coming years due to the predicted rise in the global cancer burden [20]. Cancer patients have to undergo many scans during treatment to track the progress of the cancer, which all need to be analysed by radiologists [18]. Additionally, radiologists have to manually measure longitudinal measurements of the tumours to follow clinical guidelines such as the Response Evaluation Criteria In Solid Tumors (RECIST) [10]. Manually measuring these long- and short-axis lesion measurements in CT scans is not only time-consuming but also has a large inter-observer variability [2]. Automatic segmentation models can help relieve some of the pressure by automatically segmenting the lesions from CT scans, which in turn can be used to automatically compute the longitudinal axis measurements. By standardizing the segmentation process, these models ensure consistency in measurements, thereby reducing inter-observer variability. Consequently, automatic lesion segmentation models can alleviate the workload on radiologists and aid in decreasing inter-observer variability [16]. | |
| dc.identifier.uri | https://theses.ubn.ru.nl/handle/123456789/18894 | |
| dc.language.iso | en | |
| dc.thesis.faculty | Faculteit der Sociale Wetenschappen | |
| dc.thesis.specialisation | specialisations::Faculteit der Sociale Wetenschappen::Artificial Intelligence::Master Artificial Intelligence | |
| dc.thesis.studyprogramme | studyprogrammes::Faculteit der Sociale Wetenschappen::Artificial Intelligence | |
| dc.thesis.type | Master | |
| dc.title | Beyond the Baseline: Investigating Failed Segmentations within the Universal Lesion Segmentation Challenge ‘23 |
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