CBCTLiTS: A Synthetic, Paired CBCT/CT Dataset For Segmentation And Style Transfer
| dc.contributor.author | Tschuchnig, Maximilian Ernst | en_US |
| dc.contributor.author | Steininger, Philipp | en_US |
| dc.contributor.author | Gadermayr, Michael | en_US |
| dc.contributor.editor | Garrison, Laura | en_US |
| dc.contributor.editor | Jönsson, Daniel | en_US |
| dc.date.accessioned | 2024-09-17T06:06:40Z | |
| dc.date.available | 2024-09-17T06:06:40Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Medical imaging is vital in computer assisted intervention. Particularly cone beam computed tomography (CBCT) with defacto real time and mobility capabilities plays an important role. However, CBCT images often suffer from artifacts, which pose challenges for accurate interpretation, motivating research in advanced algorithms for more effective use in clinical practice. In this work we present CBCTLiTS, a synthetically generated, labelled CBCT dataset for segmentation with paired and aligned, high quality computed tomography data. The CBCT data is provided in five levels of quality, reaching from a large number of projections with high visual quality and mild artifacts to a small number of projections with severe artifacts. This allows thorough investigations with the quality as a degree of freedom. We also provide baselines for several possible research scenarios like uni- and multimodal segmentation, multitask learning and style transfer followed by segmentation of relatively simple liver to complex liver tumor segmentation. CBCTLiTS is accesssible via https://www.kaggle.com/datasets/ maximiliantschuchnig/cbct-liver-and-liver-tumor-segmentation-train-data. | en_US |
| dc.description.sectionheaders | Medical Visualization and Surgical Assistance | |
| dc.description.seriesinformation | Eurographics Workshop on Visual Computing for Biology and Medicine | |
| dc.identifier.doi | 10.2312/vcbm.20241183 | |
| dc.identifier.isbn | 978-3-03868-244-8 | |
| dc.identifier.issn | 2070-5786 | |
| dc.identifier.pages | 5 pages | |
| dc.identifier.uri | https://doi.org/10.2312/vcbm.20241183 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/vcbm20241183 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Computing methodologies → 3D imaging; Image segmentation; Reconstruction; Matching | |
| dc.subject | Computing methodologies → 3D imaging | |
| dc.subject | Image segmentation | |
| dc.subject | Reconstruction | |
| dc.subject | Matching | |
| dc.title | CBCTLiTS: A Synthetic, Paired CBCT/CT Dataset For Segmentation And Style Transfer | en_US |