4 citations · 7 across the 4 of their papers we have counts for
4 papers
Prediction of post-radiotherapy recurrence volumes in head and neck squamous cell carcinoma using 3D U-Net segmentation
Denis Kutnár, Ivan R Vogelius, Katrin Elisabet Håkansson +7
Locoregional recurrences (LRR) are still a frequent site of treatment failure for head and neck squamous cell carcinoma (HNSCC) patients. Identification of high risk subvolumes bas…
Localise to segment: crop to improve organ at risk segmentation accuracy
Abraham George Smith, Denis Kutnár, Ivan Richter Vogelius +2
Increased organ at risk segmentation accuracy is required to reduce cost and complications for patients receiving radiotherapy treatment. Some deep learning methods for the segment…
Explicit Temporal Embedding in Deep Generative Latent Models for Longitudinal Medical Image Synthesis
Julian Schön, Raghavendra Selvan, Lotte Nygård +2
Medical imaging plays a vital role in modern diagnostics and treatment. The temporal nature of disease or treatment progression often results in longitudinal data. Due to the cost…
RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy
Abraham George Smith, Jens Petersen, Cynthia Terrones-Campos +5
Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data. We investigate th…