Oscar Acosta

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Associate Professor

Université de Rennes 1

France

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Oscar Acosta is an Associate Professor at Université de Rennes 1, France. His research areas include the application of deep learning and artificial intelligence in medical imaging, particularly in head and neck cancer and renal cell carcinoma. His recent contributions involve innovative approaches to dysphagia characterization, organ segmentation in radiotherapy, and synthetic imaging techniques.

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Oscar Acosta

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Articles (20)

Automated dysphagia characterization in head and neck cancer patients using videofluoroscopic swallowing studies

Deep learning-based segmentation of head and neck organs at risk on CBCT images with dosimetric assessment for radiotherapy

Impact of contrast enhancement phase on CT-based radiomics analysis for predicting post-surgical recurrence in renal cell carcinoma

3D Unsupervised deep learning method for magnetic resonance imaging-to-computed tomography synthesis in prostate radiotherapy

Clinical Application of Digital and Computational Pathology in Renal Cell Carcinoma: A Systematic Review

Defining the potential for sexual structures-sparing for prostate cancer external beam radiotherapy: A dosimetric study,Faisabilité de l’épargne des structures sexuelles pour la radiothérapie prostatique : une étude dosimétrique

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

Multi T1-weighted contrast imaging and T1 mapping with compressed sensing FLAWS at 3 T

Computed tomography synthesis from magnetic resonance imaging using cycle Generative Adversarial Networks with multicenter learning

A deep learning model to generate synthetic CT for prostate MR-only radiotherapy dose planning: a multicenter study

Application of Machine Learning Models to Predict Recurrence After Surgical Resection of Nonmetastatic Renal Cell Carcinoma

Artificial Intelligence-Based Opportunities in Liver Pathology—A Systematic Review

Cleaning radiotherapy contours for radiomics studies, is it worth it? A head and neck cancer study

A high-performance method of deep learning for prostate MR-only radiotherapy planning using an optimized Pix2Pix architecture

Quality assurance for MRI-only radiation therapy: A voxel-wise population-based methodology for image and dose assessment of synthetic CT generation methods

Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data

Spatial descriptions of radiotherapy dose: normal tissue complication models and statistical associations.

Towards a reduced in silico model predicting biochemical recurrence after radiotherapy in prostate cancer.

Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review

High-resolution multi-T&lt;sub&gt;1&lt;/sub&gt; -weighted contrast and T&lt;sub&gt;1&lt;/sub&gt; mapping with low B1&gt;+ sensitivity using the fluid and white matter suppression (FLAWS) sequence at 7T

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Daniel Abler

Jean-Claude Nunes

Adrien Depeursinge

Javier Pascau

Sarah Gulliford

francois Paris

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High-resolution multi-T<sub>1</sub> -weighted contrast and T<sub>1</sub> mapping with low B1>+ sensitivity using the fluid and white matter suppression (FLAWS) sequence at 7T