4 citations · 4 across the 3 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…
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…
Segmentation of Roots in Soil with U-Net
Abraham George Smith, Jens Petersen, Raghavendra Selvan +1
Plant root research can provide a way to attain stress-tolerant crops that produce greater yield in a diverse array of conditions. Phenotyping roots in soil is often challenging du…