334 citations
- Otto-von-Guericke-Universität MagdeburgDE16 papers
- German Center for Neurodegenerative DiseasesDE12 papers
- Human TechnopoleIT5 papers
- Leibniz Institute for NeurobiologyDE3 papers
- Medizinische Hochschule HannoverDE3 papers
- University Hospital MagdeburgDE3 papers
- Helmholtz-Zentrum HereonDE2 papers
- Radboud University NijmegenNL2 papers
- UNSW SydneyAU2 papers
- AGH University of KrakowPL1 paper
- Apollo HospitalsIN1 paper
- BG Klinikum DuisburgDE1 paper
18 papers
Towards Segmenting the Invisible: An End-to-End Registration and Segmentation Framework for Weakly Supervised Tumour Analysis
Budhaditya Mukhopadhyay, Chirag Mandal, Pavan Tummala +3
Liver tumour ablation presents a significant clinical challenge: whilst tumours are clearly visible on pre-operative MRI, they are often effectively invisible on intra-operative CT…
PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation
Soumick Chatterjee, Franziska Gaidzik, Alessandro Sciarra +5
In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, pau…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Liver Segmentation in Time-resolved C-arm CT Volumes Reconstructed from Dynamic Perfusion Scans using Time Separation Technique
Soumick Chatterjee, Hana Haseljić, Robert Frysch +9
Perfusion imaging is a valuable tool for diagnosing and treatment planning for liver tumours. The time separation technique (TST) has been successfully used for modelling C-arm con…
Complex Network for Complex Problems: A comparative study of CNN and Complex-valued CNN
Soumick Chatterjee, Pavan Tummala, Oliver Speck +1
Neural networks, especially convolutional neural networks (CNN), are one of the most common tools these days used in computer vision. Most of these networks work with real-valued d…
Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging
Hana Haseljić, Soumick Chatterjee, Robert Frysch +9
Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT…