no code implementations • 11 Jul 2024 • Chethan Radhakrishna, Karthikesh Varma Chintalapati, Sri Chandana Hudukula Ram Kumar, Raviteja Sutrave, Hendrik Mattern, Oliver Speck, Andreas Nürnberger, Soumick Chatterjee
The proposed MIP-based methods produce segmentations with improved vessel continuity, which is evident in visual examinations of ROIs.
2 code implementations • 25 Dec 2023 • Soumick Chatterjee, Franziska Gaidzik, Alessandro Sciarra, Hendrik Mattern, Gábor Janiga, Oliver Speck, Andreas Nürnberger, Sahani Pathiraja
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, paucity of labelled data and class imbalanced datasets.
1 code implementation • 30 Aug 2023 • Jianning Li, Zongwei Zhou, Jiancheng Yang, Antonio Pepe, Christina Gsaxner, Gijs Luijten, Chongyu Qu, Tiezheng Zhang, Xiaoxi Chen, Wenxuan Li, Marek Wodzinski, Paul Friedrich, Kangxian Xie, Yuan Jin, Narmada Ambigapathy, Enrico Nasca, Naida Solak, Gian Marco Melito, Viet Duc Vu, Afaque R. Memon, Christopher Schlachta, Sandrine de Ribaupierre, Rajnikant Patel, Roy Eagleson, Xiaojun Chen, Heinrich Mächler, Jan Stefan Kirschke, Ezequiel de la Rosa, Patrick Ferdinand Christ, Hongwei Bran Li, David G. Ellis, Michele R. Aizenberg, Sergios Gatidis, Thomas Küstner, Nadya Shusharina, Nicholas Heller, Vincent Andrearczyk, Adrien Depeursinge, Mathieu Hatt, Anjany Sekuboyina, Maximilian Löffler, Hans Liebl, Reuben Dorent, Tom Vercauteren, Jonathan Shapey, Aaron Kujawa, Stefan Cornelissen, Patrick Langenhuizen, Achraf Ben-Hamadou, Ahmed Rekik, Sergi Pujades, Edmond Boyer, Federico Bolelli, Costantino Grana, Luca Lumetti, Hamidreza Salehi, Jun Ma, Yao Zhang, Ramtin Gharleghi, Susann Beier, Arcot Sowmya, Eduardo A. Garza-Villarreal, Thania Balducci, Diego Angeles-Valdez, Roberto Souza, Leticia Rittner, Richard Frayne, Yuanfeng Ji, Vincenzo Ferrari, Soumick Chatterjee, Florian Dubost, Stefanie Schreiber, Hendrik Mattern, Oliver Speck, Daniel Haehn, Christoph John, Andreas Nürnberger, João Pedrosa, Carlos Ferreira, Guilherme Aresta, António Cunha, Aurélio Campilho, Yannick Suter, Jose Garcia, Alain Lalande, Vicky Vandenbossche, Aline Van Oevelen, Kate Duquesne, Hamza Mekhzoum, Jef Vandemeulebroucke, Emmanuel Audenaert, Claudia Krebs, Timo Van Leeuwen, Evie Vereecke, Hauke Heidemeyer, Rainer Röhrig, Frank Hölzle, Vahid Badeli, Kathrin Krieger, Matthias Gunzer, Jianxu Chen, Timo van Meegdenburg, Amin Dada, Miriam Balzer, Jana Fragemann, Frederic Jonske, Moritz Rempe, Stanislav Malorodov, Fin H. Bahnsen, Constantin Seibold, Alexander Jaus, Zdravko Marinov, Paul F. Jaeger, Rainer Stiefelhagen, Ana Sofia Santos, Mariana Lindo, André Ferreira, Victor Alves, Michael Kamp, Amr Abourayya, Felix Nensa, Fabian Hörst, Alexander Brehmer, Lukas Heine, Yannik Hanusrichter, Martin Weßling, Marcel Dudda, Lars E. Podleska, Matthias A. Fink, Julius Keyl, Konstantinos Tserpes, Moon-Sung Kim, Shireen Elhabian, Hans Lamecker, Dženan Zukić, Beatriz Paniagua, Christian Wachinger, Martin Urschler, Luc Duong, Jakob Wasserthal, Peter F. Hoyer, Oliver Basu, Thomas Maal, Max J. H. Witjes, Gregor Schiele, Ti-chiun Chang, Seyed-Ahmad Ahmadi, Ping Luo, Bjoern Menze, Mauricio Reyes, Thomas M. Deserno, Christos Davatzikos, Behrus Puladi, Pascal Fua, Alan L. Yuille, Jens Kleesiek, Jan Egger
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
1 code implementation • 13 May 2023 • Domenico Iuso, Soumick Chatterjee, Sven Cornelissen, Dries Verhees, Jan De Beenhouwer, Jan Sijbers
The findings were cross-validated in a 5-fold fashion and include: a performance benchmark of the DL models, an evaluation of the post-processing algorithm, an evaluation of the effect of training supervised models with the output of unsupervised models.
no code implementations • 9 Feb 2023 • Soumick Chatterjee, Hana Haseljić, Robert Frysch, Vojtěch Kulvait, Vladimir Semshchikov, Bennet Hensen, Frank Wacker, Inga Brüschx, Thomas Werncke, Oliver Speck, Andreas Nürnberger, Georg Rose
Perfusion imaging is a valuable tool for diagnosing and treatment planning for liver tumours.
1 code implementation • 9 Feb 2023 • Soumick Chatterjee, Pavan Tummala, Oliver Speck, Andreas Nürnberger
Although some comparisons of CNNs and CV-CNNs for different tasks have been performed in the past, a large-scale investigation comparing different models operating on different tasks has not been conducted.
1 code implementation • 20 Jul 2022 • Hana Haseljić, Soumick Chatterjee, Robert Frysch, Vojtěch Kulvait, Vladimir Semshchikov, Bennet Hensen, Frank Wacker, Inga Brüsch, Thomas Werncke, Oliver Speck, Andreas Nürnberger, Georg Rose
This paper shows the potential of segmenting the liver from CT, CBCT, and CBCT TST, learning from the available limited training data, which can possibly be used in the future for the visualisation and evaluation of the perfusion maps for the treatment evaluation of liver diseases.
1 code implementation • 14 Jun 2022 • Alessandro Sciarra, Soumick Chatterjee, Max Dünnwald, Giuseppe Placidi, Andreas Nürnberger, Oliver Speck, Steffen Oeltze-Jafra
An automated image quality assessment based on the structural similarity index (SSIM) regression through a residual neural network is proposed in this work.
1 code implementation • 10 Jun 2022 • Soumick Chatterjee, Hadya Yassin, Florian Dubost, Andreas Nürnberger, Oliver Speck
The models are trained by using the input image and only the classification labels as ground-truth in a supervised fashion - without using any information about the location of the region of interest (i. e. the segmentation labels), making the segmentation training of the models weakly-supervised through classification labels.
1 code implementation • 11 May 2022 • Philipp Ernst, Soumick Chatterjee, Georg Rose, Andreas Nürnberger
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 slices.
1 code implementation • 8 Mar 2022 • Soumick Chatterjee, Himanshi Bajaj, Istiyak H. Siddiquee, Nandish Bandi Subbarayappa, Steve Simon, Suraj Bangalore Shashidhar, Oliver Speck, Andreas Nürnberge
Deep learning based techniques have been applied successfully to tackle various complex medical image processing problems, including medical image registration.
1 code implementation • 10 Feb 2022 • Soumick Chatterjee, Chompunuch Sarasaen, Georg Rose, Andreas Nürnberger, Oliver Speck
However, such MRI acquisition with high temporal resolution suffers from limited spatial resolution - also known as the spatio-temporal trade-off of dynamic MRI.
1 code implementation • 31 Jan 2022 • Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald, Pavan Tummala, Shubham Kumar Agrawal, Aishwarya Jauhari, Aman Kalra, Steffen Oeltze-Jafra, Oliver Speck, Andreas Nürnberger
Such a technique can then be used to detect anomalies - lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology.
1 code implementation • 26 Dec 2021 • Philipp Ernst, Soumick Chatterjee, Georg Rose, Oliver Speck, Andreas Nürnberger
However, the undersampled reconstruction problem for these two modalities was always considered as two different problems and tackled separately by different research works.
1 code implementation • 16 Oct 2021 • Soumick Chatterjee, Arnab Das, Chirag Mandal, Budhaditya Mukhopadhyay, Manish Vipinraj, Aniruddh Shukla, Rajatha Nagaraja Rao, Chompunuch Sarasaen, Oliver Speck, Andreas Nürnberger
Moreover, this research presents a unified framework, TorchEsegeta, for applying various interpretability and explainability techniques for deep learning models and generate visual interpretations and explanations for clinicians to corroborate their clinical findings.
1 code implementation • 28 May 2021 • Soumick Chatterjee, Faraz Ahmed Nizamani, Andreas Nürnberger, Oliver Speck
Finally, Pre-trained ResNet Mixed Convolution was observed to be the best model in these experiments, achieving a macro F1-score of 0. 93 and a test accuracy of 96. 98\%, while at the same time being the model with the least computational cost.
4 code implementations • 16 Mar 2021 • Soumick Chatterjee, Mario Breitkopf, Chompunuch Sarasaen, Hadya Yassin, Georg Rose, Andreas Nürnberger, Oliver Speck
It has been shown that the proposed framework can successfully reconstruct even for an acceleration factor of 20 for Cartesian (0. 968$\pm$0. 005) and 17 for radially (0. 962$\pm$0. 012) sampled data.
no code implementations • 25 Feb 2021 • Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald, Raghava Vinaykanth Mushunuri, Ranadheer Podishetti, Rajatha Nagaraja Rao, Geetha Doddapaneni Gopinath, Steffen Oeltze-Jafra, Oliver Speck, Andreas Nürnberger
In this research, a deep learning based super-resolution technique is proposed and has been applied for DW-MRI.
1 code implementation • 4 Feb 2021 • Chompunuch Sarasaen, Soumick Chatterjee, Mario Breitkopf, Georg Rose, Andreas Nürnberger, Oliver Speck
Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial resolution is compromised.
1 code implementation • 28 Nov 2020 • Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald, Steffen Oeltze-Jafra, Andreas Nürnberger, Oliver Speck
Traditional methods, such as prospective or retrospective motion correction, have been proposed to avoid or alleviate motion artefacts.
1 code implementation • 20 Nov 2020 • Dhanunjaya Mitta, Soumick Chatterjee, Oliver Speck, Andreas Nürnberger
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.
3 code implementations • 18 Jun 2020 • Soumick Chatterjee, Kartik Prabhu, Mahantesh Pattadkal, Gerda Bortsova, Chompunuch Sarasaen, Florian Dubost, Hendrik Mattern, Marleen de Bruijne, Oliver Speck, Andreas Nürnberger
The deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and was made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance.
2 code implementations • 3 Jun 2020 • Soumick Chatterjee, Fatima Saad, Chompunuch Sarasaen, Suhita Ghosh, Valerie Krug, Rupali Khatun, Rahul Mishra, Nirja Desai, Petia Radeva, Georg Rose, Sebastian Stober, Oliver Speck, Andreas Nürnberger
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors.
1 code implementation • 17 Jan 2020 • A. Emre Kavur, N. Sinem Gezer, Mustafa Barış, Sinem Aslan, Pierre-Henri Conze, Vladimir Groza, Duc Duy Pham, Soumick Chatterjee, Philipp Ernst, Savaş Özkan, Bora Baydar, Dmitry Lachinov, Shuo Han, Josef Pauli, Fabian Isensee, Matthias Perkonigg, Rachana Sathish, Ronnie Rajan, Debdoot Sheet, Gurbandurdy Dovletov, Oliver Speck, Andreas Nürnberger, Klaus H. Maier-Hein, Gözde BOZDAĞI AKAR, Gözde Ünal, Oğuz Dicle, M. Alper Selver
The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0. 98 $\pm$ 0. 00 / 0. 95 $\pm$ 0. 01) but the best MSSD performance remain limited (21. 89 $\pm$ 13. 94 / 20. 85 $\pm$ 10. 63 mm).
no code implementations • 10 Oct 2019 • Chompunuch Sarasaen, Soumick Chatterjee, Mario Breitkopf, Domenico Iuso, Georg Rose, Oliver Speck
This deformation model was then applied to the high resolution images to obtain high resolution images of different breathing phases.
1 code implementation • 15 May 2019 • Rupali Khatun, Soumick Chatterjee
A multilevel random forest technique in a hierarchical way is proposed.
BIG-bench Machine Learning Colorectal Gland Segmentation: +2