Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images

Autor: Aashish C. Gupta, Guillaume Cazoulat, Mais Al Taie, Sireesha Yedururi, Bastien Rigaud, Austin Castelo, John Wood, Cenji Yu, Caleb O’Connor, Usama Salem, Jessica Albuquerque Marques Silva, Aaron Kyle Jones, Molly McCulloch, Bruno C. Odisio, Eugene J. Koay, Kristy K. Brock

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)

Abstract Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to

Externí odkaz: https://doaj.org/article/6f8b71221bc04ad193b13173d4a5714c

Automated segmentation of colorectal liver metastasis and liver ablation on contrast-enhanced CT images

Autor: Brian M. Anderson, Bastien Rigaud, Yuan-Mao Lin, A. Kyle Jones, HynSeon Christine Kang, Bruno C. Odisio, Kristy K. Brock

Publikováno v: Frontiers in Oncology, Vol 12 (2022)

ObjectivesColorectal cancer (CRC), the third most common cancer in the USA, is a leading cause of cancer-related death worldwide. Up to 60% of patients develop liver metastasis (CRLM). Treatments like radiation and ablation therapies require disease

Externí odkaz: https://doaj.org/article/1eac90c390ab4389a8443032d95ae862

Contour‐guided deep learning based deformable image registration for dose monitoring during CBCT‐guided radiotherapy of prostate cancer

Autor: Cédric Hemon, Bastien Rigaud, Anais Barateau, Florian Tilquin, Vincent Noblet, David Sarrut, Philippe Meyer, Julien Bert, Renaud De Crevoisier, Antoine Simon

Publikováno v: Journal of Applied Clinical Medical Physics
Journal of Applied Clinical Medical Physics, 2023, pp.e13991. ⟨10.1002/acm2.13991⟩

Purpose: To evaluate deep learning (DL)-based deformable image registration (DIR) for dose accumulation during radiotherapy of prostate cancer patients.Methods and materials: Data including 341 CBCTs (209 daily, 132 weekly) and 23 planning CTs from 2

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::526175c525ce00d4a1cf22be4bfdd856
https://doi.org/10.1002/acm2.13991

Automatic segmentation for plan-of-the-day selection in CBCT-guided adaptive radiation therapy of cervical cancer

Autor: Chen Zhang, Caroline Lafond, Anaïs Barateau, Julie Leseur, Bastien Rigaud, Diane Barbara Chan Sock Line, Guanyu Yang, Huazhong Shu, Jean-Louis Dillenseger, Renaud de Crevoisier, Antoine Simon

Publikováno v: Physics in Medicine and Biology
Physics in Medicine and Biology, 2022, 67 (24), pp.245020. ⟨10.1088/1361-6560/aca5e5⟩

Objective. Plan-of-the-day (PoD) adaptive radiation therapy (ART) is based on a library of treatment plans, among which, at each treatment fraction, the PoD is selected using daily images. However, this strategy is limited by PoD selection uncertaint

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f72167c45c1d261629c959ce24f762e
https://hal.science/hal-03882318

Predicting the impact of standard and hypofractionated schedules in prostate cancer radiotherapy with a mechanistic model

Autor: Carlos Sosa-Marrero, Aurelien Briens, Pierre Fontaine, Bastien Rigaud, Renaud de Crevoisier, Oscar Acosta

Publikováno v: 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2022
2022 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2022, Sep 2022, Ioannina, Greece. ⟨10.1109/BHI56158.2022.9926845⟩

International audience; Prostate cancer has been typically treated with a total radiation dose of 74-80 Gy administered in 2 Gy fractions. However, about 20% of patients suffer biochemical recurrence. Hypofractionated treatments may have a positive e

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9937bec7e26041343c2ca1439f5a80da
https://hal.science/hal-03931047