Mathis Ersted Rasmussen
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View article: Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided Radiotherapy
Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided Radiotherapy Open
Radiation therapy (RT) is a vital part of treatment for head and neck cancer, where accurate segmentation of gross tumor volume (GTV) is essential for effective treatment planning. This study investigates the use of pre-RT tumor regions an…
View article: RadDeploy: A framework for integrating in-house developed software and artificial intelligence models seamlessly into radiotherapy workflows
RadDeploy: A framework for integrating in-house developed software and artificial intelligence models seamlessly into radiotherapy workflows Open
View article: Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review
Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review Open
Background/purpose The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ)…
View article: Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality images
Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality images Open
Deep learning presents novel opportunities for the auto-segmentation of gross tumor volume (GTV) in head and neck cancer (HNC), yet fully automatic methods usually necessitate significant manual refinement. This study investigates the Segm…
View article: Raddeploy: A Framework for Integrating In-House Developed Models Seamlessly into Radiotherapy Workflows
Raddeploy: A Framework for Integrating In-House Developed Models Seamlessly into Radiotherapy Workflows Open
View article: Determining The Role Of Radiation Oncologist Demographic Factors On Segmentation Quality: Insights From A Crowd-Sourced Challenge Using Bayesian Estimation
Determining The Role Of Radiation Oncologist Demographic Factors On Segmentation Quality: Insights From A Crowd-Sourced Challenge Using Bayesian Estimation Open
BACKGROUND Medical image auto-segmentation is poised to revolutionize radiotherapy workflows. The quality of auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influen…
View article: A simple single-cycle interactive strategy to improve deep learning-based segmentation of organs-at-risk in head-and-neck cancer
A simple single-cycle interactive strategy to improve deep learning-based segmentation of organs-at-risk in head-and-neck cancer Open
Single-cycle interactive segmentation improved segmentation metrics when compared to the CT-only model and was clinically feasible from a technical and usability point of view. The study suggests that it may be cost-effective to add a smal…