831 citations · 1.2k across the 29 of their papers we have counts for
30 papers
SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms
Soumick Chatterjee, Hendrik Mattern, Marc Dörner +45
The human brain receives nutrients and oxygen through an intricate network of blood vessels. Pathology affecting small vessels, at the mesoscopic scale, represents a critical vulne…
CEAR: Automatic construction of a knowledge graph of chemical entities and roles from scientific literature
Stefan Langer, Fabian Neuhaus, Andreas Nürnberger
Ontologies are formal representations of knowledge in specific domains that provide a structured framework for organizing and understanding complex information. Creating ontologies…
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…
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…
Complex-valued neural networks to speed-up MR Thermometry during Hyperthermia using Fourier PD and PDUNet
Rupali Khatun, Soumick Chatterjee, Christoph Bert +6
Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treatment for distinct solid tumour entities. In HT, tumour tissue is exogenously hea…
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…
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…
Dual Branch Prior-SegNet: CNN for Interventional CBCT using Planning Scan and Auxiliary Segmentation Loss
Philipp Ernst, Suhita Ghosh, Georg Rose +1
This paper proposes an extension to the Dual Branch Prior-Net for sparse view interventional CBCT reconstruction incorporating a high quality planning scan. An additional head lear…
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…
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…
Dual Skip Connections Minimize the False Positive Rate of Lung Nodule Detection in CT images
Jiahua Xu, Philipp Ernst, Tung Lung Liu +1
Pulmonary cancer is one of the most commonly diagnosed and fatal cancers and is often diagnosed by incidental findings on computed tomography. Automated pulmonary nodule detection…
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…
Manually Annotated Spelling Error Corpus for Amharic
Andargachew Mekonnen Gezmu, Tirufat Tesifaye Lema, Binyam Ephrem Seyoum +1
This paper presents a manually annotated spelling error corpus for Amharic, lingua franca in Ethiopia. The corpus is designed to be used for the evaluation of spelling error detect…
Contemporary Amharic Corpus: Automatically Morpho-Syntactically Tagged Amharic Corpus
Andargachew Mekonnen Gezmu, Binyam Ephrem Seyoum, Michael Gasser +1
We introduced the contemporary Amharic corpus, which is automatically tagged for morpho-syntactic information. Texts are collected from 25,199 documents from different domains and…
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…
Extended Parallel Corpus for Amharic-English Machine Translation
Andargachew Mekonnen Gezmu, Andreas Nürnberger, Tesfaye Bayu Bati
This paper describes the acquisition, preprocessing, segmentation, and alignment of an Amharic-English parallel corpus. It will be helpful for machine translation of a low-resource…
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…