RRc-UNet 3D for Lung Tumor Segmentation from CT Scans of Non-Small Cell Lung Cancer Patients
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| hal.structure.identifier | BoRdeaux Institute in onCology [Inserm U1312 - BRIC] | |
| hal.structure.identifier | Institut Bergonié [Bordeaux] | |
| hal.structure.identifier | Modélisation Mathématique pour l'Oncologie [MONC] | |
| dc.contributor.author | LE, Van-Linh | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| hal.structure.identifier | Centre National de la Recherche Scientifique [CNRS] | |
| hal.structure.identifier | Modélisation Mathématique pour l'Oncologie [MONC] | |
| dc.contributor.author | SAUT, Olivier | |
| dc.date.accessioned | 2024-04-04T02:33:02Z | |
| dc.date.available | 2024-04-04T02:33:02Z | |
| dc.date.created | 2023 | |
| dc.date.issued | 2023 | |
| dc.date.conference | 2023-10-02 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/190439 | |
| dc.description.abstractEn | Lung cancer is a grave disease that accounts for more than one million deaths, and Non-Small Cell Lung Cancer (NSCLC) accounts for 85% of all lung cancers. Rapid detection of lung cancer could reduce the mortality rate and increase the patient's survival rate, in which tumor segmentation plays a significant role in the diagnosis and treatment of lung cancer. Nevertheless, manual segmentation by radiologists can be time-consuming and labor-intensive. In recent years, deep learning methods have achieved good results in medical image segmentation. In this paper, RRcUNet 3D, a variant of the Unet model, was proposed to perform tumor segmentation in Computed Tomography (CT) images of NSCLC patients. This network was trained end-to-end from a small set of CT scans of NSCLC patients, then the trained model was validated on another set of CT scans of NSCLC patients. The experimental results showed that our model can provide a highly accurate segmentation of tumors in the 3D volume of CT images. | |
| dc.language.iso | en | |
| dc.rights.uri | http://creativecommons.org/licenses/by/ | |
| dc.source.title | Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | |
| dc.subject.en | Lung tumor segmentation | |
| dc.subject.en | NSCLC | |
| dc.subject.en | Unet | |
| dc.subject.en | Deep learning | |
| dc.title.en | RRc-UNet 3D for Lung Tumor Segmentation from CT Scans of Non-Small Cell Lung Cancer Patients | |
| dc.type | Communication dans un congrès | |
| dc.subject.hal | Informatique [cs] | |
| bordeaux.page | 2316-2325 | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.conference.title | ICCV CVAMD 2023 - Workshop of International Conference on Computer Vision | |
| bordeaux.country | FR | |
| bordeaux.title.proceeding | Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | |
| bordeaux.conference.city | Paris | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-04236510 | |
| hal.version | 1 | |
| hal.invited | non | |
| hal.proceedings | oui | |
| hal.conference.end | 2023-10-02 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-04236510v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Proceedings%20of%20the%20IEEE/CVF%20International%20Conference%20on%20Computer%20Vision%20(ICCV)%20Workshops&rft.date=2023&rft.spage=2316-2325&rft.epage=2316-2325&rft.au=LE,%20Van-Linh&SAUT,%20Olivier&rft.genre=unknown |
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