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

Deep Learning in Medical Image Analysis
Thirona
Studienstiftung des deutschen Volkes
Fraunhofer ICON program
Eurostars PIANO
Europees Fonds voor Regionale Ontwikkeling (EFRO). See press release (in Dutch).

EFRO Oost-Nederland

People

Bram van Ginneken

Professor, Scientific Co-Director

Alessa Hering

Assistant Professor

Mathias Prokop

Professor

Matthieu Rutten

Associate Professor, Radiologist

Kiran Vaidhya Venkadesh

Postdoctoral Researcher

Lena Philipp

PhD Candidate

Luc Builtjes

PhD Candidate

Sarah de Boer

PhD Candidate

Luuk Boulogne

PhD Candidate

Max de Grauw

PhD Candidate

Ernst Scholten

Senior Researcher

Publications

C. Jacobs, "Challenges and outlook in the management of pulmonary nodules detected on CT", European Radiology, 2023;34:247-249.
Abstract DOI PMID
W. Hendrix, M. Rutten, N. Hendrix, B. van Ginneken, C. Schaefer-Prokop, E. Scholten, M. Prokop and C. Jacobs, "Trends in the incidence of pulmonary nodules in chest computed tomography: 10-year results from two Dutch hospitals", European Radiology, 2023;33:8279-8288.
Abstract DOI PMID Cited by ~8
L. Philipp, "Body Composition Assessment in 3D CT Images", Master thesis, 2023.
Abstract
K. Venkadesh, T. Aleef, E. Scholten, Z. Saghir, M. Silva, N. Sverzellati, U. Pastorino, B. van Ginneken, M. Prokop and C. Jacobs, "Prior CT Improves Deep Learning for Malignancy Risk Estimation of Screening-detected Pulmonary Nodules", Radiology, 2023;308(2):e223308.
Abstract DOI PMID Algorithm Cited by ~3
M. Grauw and B. Ginneken, "Semi-supervised 3D universal lesion segmentation in CT thorax-abdomen scans", European Congress of Radiology, 2022.
Abstract
M. Grauw, B. Ginneken, B. Geisler, E. Smit, M. Rooij, S. Schalekamp and M. Prokop, "Deep learning universal lesion segmentation for automated RECIST measurements on CT: comparison to manual assessment by radiologists", European Congress of Radiology, 2022.
Abstract
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.
Abstract DOI PMID Algorithm Cited by ~64
A. Hering, S. Hager, 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.
Abstract DOI Algorithm Download Cited by ~41
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.
Abstract DOI PMID Algorithm Download Cited by ~100
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.
Abstract DOI PMID arXiv Download Cited by ~182
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.
Abstract DOI PMID Cited by ~31
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.
Abstract DOI PMID Algorithm Cited by ~23
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.
Abstract DOI Cited by ~4
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", Scientific Reports, 2018;8(1):15497.
Abstract DOI PMID Cited by ~171