Oncological reporting of radiology exams

Radiology departments make large amounts of CT scans of the abdomen and the thorax. Reporting those scans is time-consuming and difficult. We develop tools to speed up and improve this process.

Funding

EFRO Oost-Nederland

People

Nikolas Lessmann

Nikolas Lessmann

Assistant Professor

Colin Jacobs

Colin Jacobs

Assistant Professor

Mathias Prokop

Mathias Prokop

Professor

Radboudumc

Matthieu Rutten

Matthieu Rutten

Associate Professor, Radiologist

Luuk Boulogne

Luuk Boulogne

PhD Candidate

Cheryl Sital

Cheryl Sital

PhD Candidate

Alessa Hering

Alessa Hering

PhD Candidate

Grzegorz Chlebus

Grzegorz Chlebus

PhD Candidate

Ernst Scholten

Ernst Scholten

Radiologist

Weiyi Xie

Weiyi Xie

PhD Candidate

Publications

  • K. Venkadesh, A. Setio, A. Schreuder, E. Scholten, K. Chung, M. W Wille, Z. Saghir, B. van Ginneken, M. Prokop and C. Jacobs, "Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT.", Radiology, 2021;300(2):438-447.
  • A. Hering, S. Häger, J. Moltz, N. Lessmann, S. Heldmann and B. van Ginneken, "CNN-based Lung CT Registration with Multiple Anatomical Constraints", Medical Image Analysis, 2021;72:102139.
  • W. Xie, C. Jacobs, J. Charbonnier and B. van Ginneken, "Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans", IEEE Transactions on Medical Imaging, 2020;39(8):2664-2675.
  • N. Lessmann, B. van Ginneken, P. de Jong and I. Išgum, "Iterative fully convolutional neural networks for automatic vertebra segmentation and identification", Medical Image Analysis, 2019;53:142-155.
  • G. Humpire Mamani, A. Setio, B. van Ginneken and C. Jacobs, "Efficient organ localization using multi-label convolutional neural networks in thorax-abdomen CT scans", Physics in Medicine and Biology, 2018;63(8):085003.
  • G. Humpire Mamani, J. Bukala, E. Scholten, M. Prokop, B. van Ginneken and C. Jacobs, "Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning", Radiology: Artificial Intelligence, 2020;2(4):e190102.
  • H. Altun, G. Chlebus, C. Jacobs, H. Meine, B. van Ginneken and H. Hahn, "Feasibility of End-To-End Trainable Two-Stage U-Net for Detection of Axillary Lymph Nodes in Contrast-Enhanced CT Based Scans on Sparse Annotations", Medical Imaging, 2020:113141C.
  • G. Chlebus, A. Schenk, J. Moltz, B. van Ginneken, H. Hahn and H. Meine, "Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing", Nature Scientific Reports, 2018;8(1):15497.