Johannes Ulén

Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases.

Lars Edenbrandt, Johannes Ulén, Olof Enqvist, 2019, European journal of radiology.

Deep learning‐based quantification of PET/CT prostate gland uptake: association with overall survival

Olof Enqvist, L. Edenbrandt, M. Ohlsson, 2019, Clinical physiology and functional imaging.

Auto-segmentations by convolutional neural network in cervical and anorectal cancer with clinical structure sets as the ground truth

Olof Enqvist, Johannes Ulén, E. Trägårdh, 2020, Clinical and translational radiation oncology.

Artificial intelligence‐based versus manual assessment of prostate cancer in the prostate gland: a method comparison study

Olof Enqvist, L. Edenbrandt, Johannes Ulén, 2019, Clinical physiology and functional imaging.

Artificial intelligence-aided CT segmentation for body composition analysis: a validation study

Olof Enqvist, L. Edenbrandt, Reza Kaboteh, 2021, European Radiology Experimental.

Automated analysis of PSMA-PET/CT studies using convolutional neural networks

Olof Enqvist, L. Edenbrandt, Johannes Ulén, 2021, medRxiv.