Search Results

Deep learning-based segmentation of prostatic urethra on computed tomography scans for treatment planning

• Authors : Cubero, Lucía; García-Elcano, Laura; Mylona, Eugenia ...

Subjects: Deep learning segmentation; Intraprostatic urethra; OAR segmentation

• Source: ISSN: 2405-6316 ; Physics and Imaging in Radiation Oncology ; https://hal.science/hal-04088339 ; Physics and Imaging in Radiation Oncology, 2023, 26, pp.100431.

Record details

Deep learning-based segmentation of prostatic urethra on computed tomography scans for treatment planning

• Authors : Cubero Gutierrez, Lucia; García Elcano, Laura; Mylona, Eugenia ...

Subjects: Prostate Cancer Radiotherapy; Intraprostatic Urethra; Deep Learning Segmentation

Record details

Deep learning-based segmentation of prostatic urethra on computed tomography scans for treatment planning

• Authors : Cubero, Lucía; García-Elcano, Laura; Mylona, Eugenia ...

Subjects: Deep learning segmentation; Intraprostatic urethra; OAR segmentation

• Source: ISSN: 2405-6316 ; Physics and Imaging in Radiation Oncology ; https://hal.science/hal-04088339 ; Physics and Imaging in Radiation Oncology, 2023, 26, pp.100431.

Record details

Planning With Patient-Specific Rectal Sub-Region Constraints Decreases Probability of Toxicity in Prostate Cancer Radiotherapy

• Authors : Lafond, Caroline; Barateau, Anais; N'Guessan, Joël ...

Subjects: organ-at-risk sparing; plan optimization; prostate cancer radiotherapy

• Source: ISSN: 2234-943X ; Frontiers in Oncology ; https://hal.science/hal-02972470 ; Frontiers in Oncology, 2020, 10, pp.1597. ⟨10.3389/fonc.2020.01597⟩.

Record details

Planning With Patient-Specific Rectal Sub-Region Constraints Decreases Probability of Toxicity in Prostate Cancer Radiotherapy

• Authors : Lafond, Caroline; Barateau, Anais; N'Guessan, Joël ...

Subjects: organ-at-risk sparing; plan optimization; prostate cancer radiotherapy

• Source: ISSN: 2234-943X ; Frontiers in Oncology ; https://hal.archives-ouvertes.fr/hal-02972470 ; Frontiers in Oncology, Frontiers, 2020, 10, pp.1597. ⟨10.3389/fonc.2020.01597⟩.

Record details

Planning With Patient-Specific Rectal Sub-Region Constraints Decreases Probability of Toxicity in Prostate Cancer Radiotherapy

• Authors : Lafond, Caroline; Barateau, Anais; N'Guessan, Joël ...

Subjects: organ-at-risk sparing; plan optimization; prostate cancer radiotherapy

• Source: ISSN: 2234-943X ; Frontiers in Oncology ; https://hal.science/hal-02972470 ; Frontiers in Oncology, 2020, 10, pp.1597. ⟨10.3389/fonc.2020.01597⟩.

Record details

Planning With Patient-Specific Rectal Sub-Region Constraints Decreases Probability of Toxicity in Prostate Cancer Radiotherapy

• Authors : Lafond, Caroline; Barateau, Anais; N'Guessan, Joël ...

Subjects: organ-at-risk sparing; plan optimization; prostate cancer radiotherapy

• Source: ISSN: 2234-943X ; Frontiers in Oncology ; https://hal.science/hal-02972470 ; Frontiers in Oncology, 2020, 10, pp.1597. ⟨10.3389/fonc.2020.01597⟩.

Record details

Population model of bladder motion and deformation based on dominant eigenmodes and mixed-effects models in prostate cancer radiotherapy

• Authors : Ríos, Richard; de Crevoisier, Renaud; Ospina, Juan D. ...

Subjects: Mixed-effects models; Motion/deformation models; Principal Component Analysis

• Source: ISSN: 1361-8415.

Record details

Multi-atlas-based segmentation of prostatic urethra from planning CT imaging to quantify dose distribution in prostate cancer radiotherapy

• Authors : Acosta, Oscar; Mylona, Eugenia; Le Dain, Mathieu ...

Subjects: Urinary toxicity; Urethra segmentation; Atlas-based segmentation

• Source: ISSN: 0167-8140 ; Radiotherapy & Oncology ; https://univ-rennes.hal.science/hal-01671693 ; Radiotherapy & Oncology, 2017, 125 (3), pp.492 - 499. ⟨10.1016/j.radonc.2017.09.015⟩.

Record details

Population model of bladder motion and deformation based on dominant eigenmodes and mixed-effects models in prostate cancer radiotherapy

• Authors : Ríos, Richard; de Crevoisier, Renaud; Ospina, Juan D. ...

Subjects: Mixed-effects models; Motion/deformation models; Principal Component Analysis

• Source: ISSN: 1361-8415.

Record details