831 citations · 1.4k across the 28 of their papers we have counts for
19 papers · 1 filter
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…
SPOCKMIP: Segmentation of Vessels in MRAs with Enhanced Continuity using Maximum Intensity Projection as Loss
Chethan Radhakrishna, Karthikesh Varma Chintalapati, Sri Chandana Hudukula Ram Kumar +5
Identification of vessel structures of different sizes in biomedical images is crucial in the diagnosis of many neurodegenerative diseases. However, the sparsity of good-quality an…
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…
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…
Primal-Dual UNet for Sparse View Cone Beam Computed Tomography Volume Reconstruction
Philipp Ernst, Soumick Chatterjee, Georg Rose +1
In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of…
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…
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…
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…
DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data
Soumick Chatterjee, Kartik Prabhu, Mahantesh Pattadkal +7
Blood vessels of the brain provide the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause s…
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.…