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


November 7, 2019

Automated assessment of lymph node status from colorectal and breast cancer resections using deep neural networks

Péter Bándi

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Abstract: In this presentation, Péter will briefly summarize all the projects he has covered during his PhD at DIAG, including the CAMELYON17 challenge, the CAMELYON dataset, his work within the AMI project, whole-slide image registration, the digital pathology library, and resolution-agnostic whole-slide image segmentation. Additionally, he will explain in detail his last project, where he explores the possibilities of transfer learning in the field of cancer metastasis detection in lymph nodes.

June 5, 2019

Dense Segmentation in Selected Dimensions: Application to Retinal Optical Coherence Tomography

Bart Liefers

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Abstract: During this week's Open DDH shall Bart present a novel convolutional neural network architecture designed for dense segmentation in a subset of the dimensions of the input data. The architecture takes an N-dimensional image as input, and produces a label for every pixel in M output dimensions, where 0 < M < N. Large context is incorporated by an encoder-decoder structure, while funneling shortcut subnetworks provide precise localization. Bart and colleagues demonstrate applicability of the architecture on two problems in retinal optical coherence tomography: segmentation of geographic atrophy and segmentation of retinal layers. Performance is compared against two baseline methods, that leave out either the encoder-decoder structure or the shortcut subnetworks. For segmentation of geographic atrophy, an average Dice score of 0.49 ± 0.21 was obtained, compared to 0.46 ± 0.22 and 0.28 ± 0.19 for the baseline methods, respectively. For the layer-segmentation task, the proposed architecture achieved a mean absolute error of 1.305 ± 0.547 pixels compared to 1.967 ± 0.841 and 2.166 ± 0.886± for the baseline methods. This work is included in the Proceedings of Machine Learning Research and shall be presented at MIDL 2019.

May 8, 2019

New treatment paradigms for improved cancer treatment with radiotherapy

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

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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.

View slides.