Figure: Three selected patient’s original CT, PET, MRI (T2, T1) images, deep learning predicted contours are compared from different image combinations. (B): GTV size is 5.6 cm3 , a case with small tumor size. (C): GTV size is 54.1 cm3 , a large tumor case. (D): GTV size is 35.5 cm3 , a case with planning CT artifacts.

We use computational methods such as machine learning, data mining and inverse optimisation, to challenge existing paradigms in radiotherapy for development of novel treatment planning methods. We use advanced analysis of medical images including CT, PET and MR scans, in large patient cohorts, with focus on head and neck cancer and breast cancer. We specifically address the problems that give rise to the largest sources of uncertainty in radiotherapy planning; namely target definition, dose optimisation, and anatomical changes during treatment.

Target delineation is a time-consuming process, which is also prone to large uncertainties and subjective assessment by the delineating clinician. We use deep learning to automatically segment the gross tumour volume in multimodality images, for use in interactive delineation tools. By introducing a deep learning generated segmentation in an interactive user interface, we aim to speed up the delineation process while at the same time reducing the uncertainty related to the delineation for a more accurate treatment.

We investigate the implementation of risk models for tumour spread, which can be used for probabilistic optimisation of dose distribution around the tumour. This approach will give the potential to reduce high radiation doses to volumes of tissue where the risk of finding cancer cells is low, and hence reduce the risk of side effects.

Other projects include individualized robust optimization to address anatomical changes during treatment, data mining for modelling patient individual risk factors, and development of automated treatment planning methods.  
Overall, we aim to enhance tailoring of radiotherapy to the individual patient through adaptation to patient specific risk factors from medical images. Moreover, we expect that the introduction of automated and computational methods will lead to an overall decreased level of uncertainty in the treatment and increased treatment quality at both individual and population basis.


Stine Korreman_80x120_sorthvid.pngProfessor of medical physics
Stine Sofia Korreman

Further information.

Camilla Skinnerup Byskov
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PhD student
Emma Riis Skarsø 
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PhD student
Lasse Hindhede Refsgaard
PURE, Aarhus University. Further information.

MathisRasmussen_80x120.pngPhD student
Mathis Ersted Rasmussen 
PURE, Aarhus University.

Nadine Vatterodt_80x120.pngPhD student
Nadine Vatterodt

KristofferMoos_80x120.pngPhD student
Kristoffer Moos

PhD student
Ihsan Bahij

Collaborating researchers

Jasper Nijkamp_80x120_sorthvid.pngAssociate professor of medical physics
Jasper Albertus Nijkamp

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Kenneth Jensen_80x120_sh.pngConsultant, Associate Professor of Clinical Medicine
Kenneth Jensen

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Associate professor
Jesper Folsted Kallehauge

Further information.

PhD student
Zixiang Wei
PURE, Aarhus University.

Medical physicist, PhD
Ulrik Vindelev Elstrøm
PURE, Aarhus University.

Medical Physicist, Dep. of Oncology, Aarhus University Hospital
Anne Ivalu Sander Holm

Professor, MD, Dep of Oncology, Aarhus University Hospital
Jesper Grau Eriksen

Professor, MD, Dep. of Oncology, Aarhus University Hospital
Birgitte Offersen