Publications of Sil van de Leemput

Papers in international journals

1. S. van de Leemput, M. Prokop, B. van Ginneken and R. Manniesing, "Stacked Bidirectional Convolutional LSTMs for Deriving 3D Non-contrast CT from Spatiotemporal 4D CT", IEEE Transactions on Medical Imaging, 2020;39(4):985-996.
   Abstract DOI PMID Download Cited by ~18
2. M. Meijs, S. Pegge, M. Vos, A. Patel, S. van de Leemput, K. Koschmieder, M. Prokop, F. Meijer and R. Manniesing, "Cerebral Artery and Vein Segmentation in Fourdimensional CT Angiography Using Convolutional Neural Networks", Radiology: Artificial Intelligence, 2020;2(4):e190178.
   Abstract DOI Cited by ~9
3. A. Patel, S. van de Leemput, M. Prokop, B. van Ginneken and R. Manniesing, "Image Level Training and Prediction: Intracranial Hemorrhage Identification in 3D Non-Contrast CT", IEEE Access, 2019;7(1):92355-92364.
   Abstract DOI Cited by ~45
4. S. van de Leemput, J. Teuwen, B. van Ginneken and R. Manniesing, "MemCNN: A Python/PyTorch package for creating memory-efficient invertible neural networks", Journal of Open Source Software, 2019;4(39):1576.
   Abstract DOI Code Cited by ~13
5. S. van de Leemput, M. Meijs, A. Patel, F. Meijer, B. van Ginneken and R. Manniesing, "Multiclass Brain Tissue Segmentation in 4D CT using Convolutional Neural Networks", IEEE Access, 2019;7(1):51557-51569.
   Abstract DOI Cited by ~13
6. M. Meijs, A. Patel, S. van de Leemput, M. Prokop, E. van Dijk, F. de Leeuw, F. Meijer, B. van Ginneken and R. Manniesing, "Robust Segmentation of the Full Cerebral Vasculature in 4D CT Images of Suspected Stroke Patients", Scientific Reports, 2017;7.
   Abstract DOI PMID Cited by ~40

Papers in conference proceedings

1. S. van de Leemput, M. Prokop, B. van Ginneken and R. Manniesing, "Stacked Bidirectional Convolutional LSTMs for 3D Non-contrast CT Reconstruction from Spatiotemporal 4D CT", Medical Imaging with Deep Learning, 2018.
   Abstract Url Cited by ~2
2. S. van de Leemput, J. Teuwen and R. Manniesing, "MemCNN: a Framework for Developing Memory Efficient Deep Invertible Networks", International Conference on Learning Representations, 2018.
   Abstract Url Cited by ~11
3. S. van de Leemput, A. Patel and R. Manniesing, "Full Volumetric Brain Tissue Segmentation in Non-contrast CT using Memory Efficient Convolutional LSTMs", Medical Imaging meets NeurIPS, 2018.
   Abstract Url Cited by ~1
4. A. Patel, S. van de Leemput, M. Prokop, B. van Ginneken and R. Manniesing, "Automatic Cerebrospinal Fluid Segmentation in Non-Contrast CT Images Using a 3D Convolutional Network", Medical Imaging, 2017;10134.
   Abstract DOI Cited by ~8
5. S. van de Leemput, F. Dorssers and B. Ehteshami Bejnordi, "A novel spherical shell filter for reducing false positives in automatic detection of pulmonary nodules in thoracic CT scans", Medical Imaging, 2015;9414:94142P.
   Abstract DOI

Abstracts

1. M. Meijs, A. Patel, S. van de Leemput, B. van Ginneken, M. Prokop and R. Manniesing, "Fast, Robust and Accurate Segmentation of the Complete Cerebral Vasculature in 4D-CTA using Deep Learning", Annual Meeting of the Radiological Society of North America, 2018.
   Abstract
2. S. van de Leemput, F. Meijer, M. Prokop and R. Manniesing, "Cerebral white matter, gray matter and cerebrospinal fluid segmentation in CT using VCAST: a volumetric cluster annotation and segmentation tool", European Congress of Radiology, 2017.
   Abstract
3. R. Manniesing, S. van de Leemput, M. Prokop and B. van Ginneken, "White Matter and Gray Matter Segmentation in 4D CT Images of Acute Ischemic Stroke Patients: a Feasibility Study", Annual Meeting of the Radiological Society of North America, 2016.
   Abstract

PhD theses

1. S. van de Leemput, "Efficient neural network training for 4D-CTA stroke imaging", PhD thesis, 2024.
   Abstract Url