831 citations · 1.1k across the 19 of their papers we have counts for
20 papers
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…
Magnetic Resonance Imaging study of sheared granular matter
Jing Wang, Zohreh Farmani, Joshua Dijksman +3
We introduce a Magnetic Resonance Imaging technique to study the geometry of shear zones of soft, low-frictional and hard, frictional granular materials and their mixtures. Hydroge…
Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images
Alessandro Sciarra, Soumick Chatterjee, Max Dünnwald +4
Motion artefacts in magnetic resonance brain images can have a strong impact on diagnostic confidence. The assessment of MR image quality is fundamental before proceeding with the…
Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification
Soumick Chatterjee, Hadya Yassin, Florian Dubost +2
Deep learning has demonstrated significant potential in medical imaging; however, the opacity of "black-box" models hinders clinical trust, while segmentation tasks typically neces…
MICDIR: Multi-scale Inverse-consistent Deformable Image Registration using UNetMSS with Self-Constructing Graph Latent
Soumick Chatterjee, Himanshi Bajaj, Istiyak H. Siddiquee +5
Image registration is the process of bringing different images into a common coordinate system - a technique widely used in various applications of computer vision, such as remote…
DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRI
Soumick Chatterjee, Chompunuch Sarasaen, Georg Rose +2
Magnetic resonance imaging (MRI) provides high spatial resolution and excellent soft-tissue contrast without using harmful ionising radiation. Dynamic MRI is an essential tool for…
StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder
Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald +7
Expert interpretation of anatomical images of the human brain is the central part of neuro-radiology. Several machine learning-based techniques have been proposed to assist in the…
Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstruction
Philipp Ernst, Soumick Chatterjee, Georg Rose +2
Computed tomography and magnetic resonance imaging are two widely used clinical imaging modalities for non-invasive diagnosis. However, both of these modalities come with certain p…
TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models
Soumick Chatterjee, Arnab Das, Chirag Mandal +7
Clinicians are often very sceptical about applying automatic image processing approaches, especially deep learning based methods, in practice. One main reason for this is the black…
Classification of Brain Tumours in MR Images using Deep Spatiospatial Models
Soumick Chatterjee, Faraz Ahmed Nizamani, Andreas Nürnberger +1
A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and…
ReconResNet: Regularised Residual Learning for MR Image Reconstruction of Undersampled Cartesian and Radial Data
Soumick Chatterjee, Mario Breitkopf, Chompunuch Sarasaen +4
MRI is an inherently slow process, which leads to long scan time for high-resolution imaging. The speed of acquisition can be increased by ignoring parts of the data (undersampling…
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning
Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald +7
Diffusion-weighted magnetic resonance imaging (DW-MRI) can be used to characterise the microstructure of the nervous tissue, e.g. to delineate brain white matter connections in a n…
Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge
Chompunuch Sarasaen, Soumick Chatterjee, Mario Breitkopf +3
Dynamic imaging is a beneficial tool for interventions to assess physiological changes. Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial reso…
Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning
Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald +3
In MRI, motion artefacts are among the most common types of artefacts. They can degrade images and render them unusable for accurate diagnosis. Traditional methods, such as prospec…
Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation
Dhanunjaya Mitta, Soumick Chatterjee, Oliver Speck +1
Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors. Manual…
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
Soumick Chatterjee, Fatima Saad, Chompunuch Sarasaen +10
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the e…
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
A. Emre Kavur, N. Sinem Gezer, Mustafa Barış +24
Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have intr…
Breathing deformation model -- application to multi-resolution abdominal MRI
Chompunuch Sarasaen, Soumick Chatterjee, Mario Breitkopf +3
Dynamic MRI is a technique of acquiring a series of images continuously to follow the physiological changes over time. However, such fast imaging results in low resolution images.…