Why Use Position Features in Liver Segmentation Performed by Convolutional Neural Network
| dc.contributor.author | Jiřík, Miroslav | |
| dc.contributor.author | Hácha, Filip | |
| dc.contributor.author | Gruber, Ivan | |
| dc.contributor.author | Pálek, Richard | |
| dc.contributor.author | Mírka, Hynek | |
| dc.contributor.author | Železný, Miloš | |
| dc.contributor.author | Liška, Václav | |
| dc.date.accessioned | 2022-02-28T11:00:22Z | |
| dc.date.available | 2022-02-28T11:00:22Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract-translated | The calculation of liver volume is primarily based on Computed Tomography. Unfortunately, automatic segmentation algorithms based on handcrafted features tend to leak segmented objects into surrounding tissues like the heart or the spleen. Currently, convolutional neural networks are widely used in various applications of computer vision including image segmentation, while providing very promising results. In our work, we utilize robustly segmentable structures like the spine, body surface, and sagittal plane. They are used as key points for position estimation inside the body. The signed distance fields derived from these structures are calculated and used as an additional channel on the input of our convolutional neural network, to be more specific U-Net, which is widely used in medical image segmentation tasks. Our work shows that this additional position information improves the results of the segmentation. We test our approach in two experiments on two public datasets of Computed Tomography images. To evaluate the results, we use the Accuracy, the Hausdorff distance, and the Dice coefficient. Code is publicly available at: https://gitlab.com/hachaf/liver-segmentation.git. | en |
| dc.format | 9 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | JIŘÍK, M. HÁCHA, F. GRUBER, I. PÁLEK, R. MÍRKA, H. ŽELEZNÝ, M. LIŠKA, V. Why Use Position Features in Liver Segmentation Performed by Convolutional Neural Network. Frontiers in Physiology, 2021, roč. 12, č. October 2021, s. nestránkováno. ISSN: 1664-042X | cs |
| dc.identifier.document-number | 710484200001 | |
| dc.identifier.doi | 10.3389/fphys.2021.734217 | |
| dc.identifier.issn | 1664-042X | |
| dc.identifier.obd | 43933801 | |
| dc.identifier.uri | 2-s2.0-85117256040 | |
| dc.identifier.uri | http://hdl.handle.net/11025/47013 | |
| dc.language.iso | en | en |
| dc.project.ID | LO1506/PUNTIS - Podpora udržitelnosti centra NTIS - Nové technologie pro informační společnost | cs |
| dc.project.ID | LM2015042/E-infrastruktura CESNET | cs |
| dc.publisher | Frontiers Media S.A. | en |
| dc.relation.ispartofseries | Frontiers in Physiology | en |
| dc.rights | © authors | en |
| dc.rights.access | openAccess | en |
| dc.subject.translated | liver volumetry | en |
| dc.subject.translated | semantic segmentation | en |
| dc.subject.translated | machine learning | en |
| dc.subject.translated | convolutional neural network | en |
| dc.subject.translated | medical imaging | en |
| dc.subject.translated | position features | en |
| dc.title | Why Use Position Features in Liver Segmentation Performed by Convolutional Neural Network | en |
| dc.title.alternative | Proč vvyužívat polohové příznaky při segmentaci jater s využitím konvolučních neuronových sítí | cs |
| dc.type | článek | cs |
| dc.type | article | en |
| dc.type.status | Peer-reviewed | en |
| dc.type.version | publishedVersion | en |