A new approach combining 3D imaging, computer vision and parametric modelling, named GRACE, is introduced, enabling adaptive and context-aware 3D printing of structures around features ranging from ce...

Search and explore AI products in Radiotherapy

Search and explore AI products in Radiotherapy

SynthRAD is the first challenge on automatic generation of synthetic computed tomography (sCT) for radiotherapy

MRISegmenter provided accurate and robust segmentation of 62 organs and structures, including 15 main organs, seven vessels, eight muscles, and 32 bones at T1-weighted abdominal MRI.

The European Union Radiation, Education, Staffing & Training (EU-REST) study aimed to collect and analyse workforce availability, education, and training needs to ensure quality and safety asp...

Four-dimensional imaging (4D-imaging) plays a critical role in achieving precise motion management in radiation therapy. However, challenges remain in 4D-imaging such as a long imaging time, suboptima...

🚀 Join us for the Deep Learning in Radiotherapy (DLinRT) Workshops! Dear colleagues, We are excited to invite you to two upcoming Deep Learning in…

🚀 Join us for the Deep Learning in Radiotherapy (DLinRT) Workshops! Dear colleagues, We are excited to invite you to two upcoming Deep Learning in…

Data, notebooks, and articles associated with the RSNA AI Deep Learning Lab at RSNA 2024 - RSNA/AI-Deep-Learning-Lab-2024

Magnetic resonance imaging (MRI) is commonly used in healthcare for its ability to generate diverse tissue contrasts without ionizing radiation. However, this flexibility complicates downstream analys...

SynthRAD is the first challenge on automatic generation of synthetic computed tomography (sCT) for radiotherapy

Purposes: This study aimed to develop a computed tomography (CT)-based multi-organ segmentation model for delineating organs-at-risk (OARs) in pediatric upper abdominal tumors and evaluate its robustn...

“You’ve taken this idea way too far,” a mentor told Prof. Fei-Fei Li.

Researchers present the first dosimetric evaluation comparing VMAT and IMRT on the MR-linac

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Curious to get an overview of #artificialintelligence/#DeepLearning -based #commercial solutions? I am happy to share a presentation on the topic, hoping it…

Background: Respiratory-resolved four-dimensional magnetic resonance imaging (4D-MRI) provides essential motion information for accurate radiation treatments of mobile tumors. However, obtaining...

Beyond automatic medical image segmentation—the spectrum between fully manual and fully automatic delineation, Michael J Trimpl, Sergey Primakov, Philippe Lambin, Eleanor P J Stride, Katherine A Vallis, Mark J Gooding

Nature Machine Intelligence - Saliency methods are used to localize areas of medical images that influence machine learning model predictions, but their accuracy and reliability require...

We’re open-sourcing AITemplate, a unified inference system for both AMD and NVIDIA GPUs. It delivers performance improvements up to 12X on NVIDIA GPUs and 4X on AMD GPUs compared to eager-mode within Pytorch.

A durable bioadhesive hydrogel-elastomer enables continuous imaging of internal organs and tissues.

Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency, Ana Barragán-Montero, Adrien Bibal, Margerie Huet Dastarac, Camille Draguet, Gilmer Valdés, Dan Nguyen, Siri Willems, Liesbeth Vandewinckele, Mats Holmström, Fredrik Löfman, Kevin Souris, Edmond Sterpin, John A Lee