Machine Learning Methods for Vessel Segmentation in Organs
| dc.contributor.advisor | Riviere, Beatrice | en_US |
| dc.contributor.advisor | Fuentes, David | en_US |
| dc.creator | Tzolova, Bilyana | en_US |
| dc.date.accessioned | 2022-10-05T20:57:04Z | en_US |
| dc.date.available | 2022-10-05T20:57:04Z | en_US |
| dc.date.created | 2022-05 | en_US |
| dc.date.issued | 2022-04-19 | en_US |
| dc.date.submitted | May 2022 | en_US |
| dc.date.updated | 2022-10-05T20:57:04Z | en_US |
| dc.description.abstract | The vascular system plays a crucial role in diagnostics, treatment, and surgical planning in a wide array of diseases. Recently, there has been a growing interest in automating the manual vessel segmentation process to save time. We aim to efficiently and effectively segment the vascular system in the liver organ using deep learning techniques in order to improve on current manual methods. We propose a 3D DenseNet using PocketNet paradigm with binary and ternary classifications that has less parameters to train than the state of the art methods. We explore the impact of various preprocessing techniques on the accuracy of the neural network. We are able to reduce training times and increase accuracy per training parameter in medical imaging segmentation of the liver vessels. Finally, we assess the accuracy of our model predictions using the dice score coefficient. We find that successful preprocessing filters and neural network parameters are necessary for consistently high dice scores. | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Tzolova, Bilyana. "Machine Learning Methods for Vessel Segmentation in Organs." (2022) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/113513">https://hdl.handle.net/1911/113513</a>. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/113513 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.subject | Vessel segmentation | en_US |
| dc.subject | neural network | en_US |
| dc.subject | 3D DenseNet | en_US |
| dc.subject | 3D UNet | en_US |
| dc.title | Machine Learning Methods for Vessel Segmentation in Organs | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Computational and Applied Mathematics | en_US |
| thesis.degree.discipline | Engineering | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Arts | en_US |
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