Open DDH

The Open DDH talks are in the St.Annakamer (Route 767) at the Radboud University Medical Centre and start at 14.30.

Upcoming talks

May 8, 2019

Title will follow

Jonas Teuwen

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Abstract: Cancer is the second leading cause of death worldwide. Radiation therapy, or radiotherapy (RT) for short, plays a pivotal role in the treatment of many cancers, where approximately 50% of cancer patients can benefit from RT in the management of their disease. During radiotherapy ionizing radiation, generally produced by a linear accelerator (linac) is delivered with the intent of killing malignant cells. To limit side effects, and to exploit the higher repair capacity of normal tissue to compared to tumor cells, the total radiation dose is typically delivered in smaller daily portions over a period of several weeks.​

​Historically radiotherapy has been one of the most technologically advanced sub fields of medicine with a strong interplay between clinicians and physicists where there is a lot of unlocked potential for the application of deep learning. ​In this talk I will give an overview of what radiotherapy is, what a typical workflow looks like and how deep learning can help in improving the care for cancer patients. In particular I will talk about the use of segmentation for treatment planning, deep learning for image improvement and reconstruction and what I will be working on the next couple of years by using deep learning to enable new treatment paradigms to improve treatment with radiotherapy.


April 3, 2019

Learning-based vertebra segmentation, identification and partitioning

Nikolas Lessmann

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Abstract: The spine is visualized on many CT and MR exams, including thorax and abdomen scans that were originally not intended for spine imaging. Because these often cover several but not all vertebrae, it is difficult to make strong assumptions for automatic analysis. Challenges are therefore the unknown number of target structures (vertebrae) in the image, their anatomical identification (which vertebrae are visible? must not assign the same label to two vertebrae) and that some biomarkers are related only to part of the vertebrae, often the vertebral body. This talk covers an instance segmentation approach for vertebra detection, segmentation, and anatomical identification, and a partitioning approach to separate vertebral body and arch based on thin-plate spline surfaces positioned by a convolutional neural network.

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March 27, 2019

From Researcher to “Defective and Diseased Alien”

Bram Platel

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Bram Platel received his doctorate from the Eindhoven University of Technology in 2007. He started to work at DIAG in 2010 with Nico Karssemeijer, mostly working on CAD for mammography; later-on Bram’s focus shifted towards image analysis for neurological disorders. Following strange symptoms at the end of 2013, he was diagnosed with Multiple Sclerosis (MS). After two years, Bram was forced to reduce his working hours to a minimum. In February of 2016, on a routine MRI-scan for his MS, multiple brain tumors were found. To treat this unique form of lymphoma, Bram received various chemotherapy treatments during a period of half a year. The treatment ended with a blood stem cell transplantation. Coincidentally, abroad a similar therapy is recently being used to treat aggressive forms of multiple sclerosis. Since the treatment, Bram's lymphoma is in complete remission, and his MS hasn't progressed. In this non-scientific presentation, Bram will talk about this period, his new foundation MS in beeld, and his emigration to the Philippines next month.

March 6, 2019

Inverse problems in medical imaging

Nikita Moriakov

Nikita Moriakov.jpg

Abstract: Inverse problem is the type of problems in natural sciences when one has to infer from a set of observations the causal factors that produced them. In medical imaging, important examples of inverse problems would be reconstruction in CT and MRI, where the volumetric representation of an object is computed from the projection and Fourier space data respectively. In a classical approach, one relies on domain specific knowledge contained in physical-analytical models to develop a reconstruction algorithm, which is often given by a certain iterative refinement procedure. Recent research in inverse problems seeks to develop a mathematically coherent foundation for combining data driven models, based on deep learning, with the analytical knowledge contained in the classical reconstruction procedures. In this talk we will give a brief overview of these developments and then focus on particular applications in Digital Breast Tomosynthesis and MRI reconstruction.

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