Search Results for author: Suprosanna Shit

Found 41 papers, 26 papers with code

TopCoW: Benchmarking Topology-Aware Anatomical Segmentation of the Circle of Willis (CoW) for CTA and MRA

1 code implementation29 Dec 2023 Kaiyuan Yang, Fabio Musio, Yihui Ma, Norman Juchler, Johannes C. Paetzold, Rami Al-Maskari, Luciano Höher, Hongwei Bran Li, Ibrahim Ethem Hamamci, Anjany Sekuboyina, Suprosanna Shit, Houjing Huang, Diana Waldmannstetter, Florian Kofler, Fernando Navarro, Martin Menten, Ivan Ezhov, Daniel Rueckert, Iris Vos, Ynte Ruigrok, Birgitta Velthuis, Hugo Kuijf, Julien Hämmerli, Catherine Wurster, Philippe Bijlenga, Laura Westphal, Jeroen Bisschop, Elisa Colombo, Hakim Baazaoui, Andrew Makmur, James Hallinan, Bene Wiestler, Jan S. Kirschke, Roland Wiest, Emmanuel Montagnon, Laurent Letourneau-Guillon, Adrian Galdran, Francesco Galati, Daniele Falcetta, Maria A. Zuluaga, Chaolong Lin, Haoran Zhao, Zehan Zhang, Sinyoung Ra, Jongyun Hwang, HyunJin Park, Junqiang Chen, Marek Wodzinski, Henning Müller, Pengcheng Shi, Wei Liu, Ting Ma, Cansu Yalçin, Rachika E. Hamadache, Joaquim Salvi, Xavier Llado, Uma Maria Lal-Trehan Estrada, Valeriia Abramova, Luca Giancardo, Arnau Oliver, Jialu Liu, Haibin Huang, Yue Cui, Zehang Lin, Yusheng Liu, Shunzhi Zhu, Tatsat R. Patel, Vincent M. Tutino, Maysam Orouskhani, Huayu Wang, Mahmud Mossa-Basha, Chengcheng Zhu, Maximilian R. Rokuss, Yannick Kirchhoff, Nico Disch, Julius Holzschuh, Fabian Isensee, Klaus Maier-Hein, Yuki Sato, Sven Hirsch, Susanne Wegener, Bjoern Menze

The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology.

Anatomy Benchmarking +1

A skeletonization algorithm for gradient-based optimization

1 code implementation ICCV 2023 Martin J. Menten, Johannes C. Paetzold, Veronika A. Zimmer, Suprosanna Shit, Ivan Ezhov, Robbie Holland, Monika Probst, Julia A. Schnabel, Daniel Rueckert

Finally, we demonstrate the utility of our algorithm by integrating it with two medical image processing applications that use gradient-based optimization: deep-learning-based blood vessel segmentation, and multimodal registration of the mandible in computed tomography and magnetic resonance images.


Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations

1 code implementation27 Mar 2023 Julian McGinnis, Suprosanna Shit, Hongwei Bran Li, Vasiliki Sideri-Lampretsa, Robert Graf, Maik Dannecker, Jiazhen Pan, Nil Stolt Ansó, Mark Mühlau, Jan S. Kirschke, Daniel Rueckert, Benedikt Wiestler

Clinical routine and retrospective cohorts commonly include multi-parametric Magnetic Resonance Imaging; however, they are mostly acquired in different anisotropic 2D views due to signal-to-noise-ratio and scan-time constraints.


Identifying chromophore fingerprints of brain tumor tissue on hyperspectral imaging using principal component analysis

no code implementations12 Jan 2023 Ivan Ezhov, Luca Giannoni, Suprosanna Shit, Frederic Lange, Florian Kofler, Bjoern Menze, Ilias Tachtsidis, Daniel Rueckert

In this article, we perform a statistical analysis of the brain tumor patients' HSI scans from the HELICoiD dataset with the aim of identifying the correlation between reflectance spectra and absorption spectra of tissue chromophores.

A Domain-specific Perceptual Metric via Contrastive Self-supervised Representation: Applications on Natural and Medical Images

no code implementations3 Dec 2022 Hongwei Bran Li, Chinmay Prabhakar, Suprosanna Shit, Johannes Paetzold, Tamaz Amiranashvili, JianGuo Zhang, Daniel Rueckert, Juan Eugenio Iglesias, Benedikt Wiestler, Bjoern Menze

We find that in the natural image domain, CSR behaves on par with the supervised one on several perceptual tests as a metric, and in the medical domain, CSR better quantifies perceptual similarity concerning the experts' ratings.

Image Generation

Topologically faithful image segmentation via induced matching of persistence barcodes

1 code implementation28 Nov 2022 Nico Stucki, Johannes C. Paetzold, Suprosanna Shit, Bjoern Menze, Ulrich Bauer

In this work, we propose the first topologically and feature-wise accurate metric and loss function for supervised image segmentation, which we term Betti matching.

Image Segmentation Segmentation +1

Focused Decoding Enables 3D Anatomical Detection by Transformers

1 code implementation21 Jul 2022 Bastian Wittmann, Fernando Navarro, Suprosanna Shit, Bjoern Menze

To tackle this issue and facilitate the advent of medical Detection Transformers, we propose a novel Detection Transformer for 3D anatomical structure detection, dubbed Focused Decoder.

Object Detection

Relationformer: A Unified Framework for Image-to-Graph Generation

1 code implementation19 Mar 2022 Suprosanna Shit, Rajat Koner, Bastian Wittmann, Johannes Paetzold, Ivan Ezhov, Hongwei Li, Jiazhen Pan, Sahand Sharifzadeh, Georgios Kaissis, Volker Tresp, Bjoern Menze

We leverage direct set-based object prediction and incorporate the interaction among the objects to learn an object-relation representation jointly.

Graph Generation Object +4

A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning

no code implementations1 Mar 2022 Fernando Navarro, Guido Sasahara, Suprosanna Shit, Ivan Ezhov, Jan C. Peeken, Stephanie E. Combs, Bjoern H. Menze

Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning.

Organ Segmentation Segmentation

FedCostWAvg: A new averaging for better Federated Learning

no code implementations16 Nov 2021 Leon Mächler, Ivan Ezhov, Florian Kofler, Suprosanna Shit, Johannes C. Paetzold, Timo Loehr, Benedikt Wiestler, Bjoern Menze

We propose a simple new aggregation strategy for federated learning that won the MICCAI Federated Tumor Segmentation Challenge 2021 (FETS), the first ever challenge on Federated Learning in the Machine Learning community.

Federated Learning Segmentation +1

Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling

1 code implementation7 Nov 2021 Ivan Ezhov, Kevin Scibilia, Katharina Franitza, Felix Steinbauer, Suprosanna Shit, Lucas Zimmer, Jana Lipkova, Florian Kofler, Johannes Paetzold, Luca Canalini, Diana Waldmannstetter, Martin Menten, Marie Metz, Benedikt Wiestler, Bjoern Menze

Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration.


Partial supervision for the FeTA challenge 2021

2 code implementations3 Nov 2021 Lucas Fidon, Michael Aertsen, Suprosanna Shit, Philippe Demaerel, Sébastien Ourselin, Jan Deprest, Tom Vercauteren

Label-set loss functions allow to train deep neural networks with partially segmented images, i. e. segmentations in which some classes may be grouped into super-classes.

Brain Segmentation Image Segmentation +2

Semi-Implicit Neural Solver for Time-dependent Partial Differential Equations

no code implementations3 Sep 2021 Suprosanna Shit, Ivan Ezhov, Leon Mächler, Abinav R., Jana Lipkova, Johannes C. Paetzold, Florian Kofler, Marie Piraud, Bjoern H. Menze

In this paper, we propose a neural solver to learn an optimal iterative scheme in a data-driven fashion for any class of PDEs.

Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)

1 code implementation30 Aug 2021 Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit, Ivan Ezhov, Paul Büschl, Chinmay Prabhakar, Mihail I. Todorov, Anjany Sekuboyina, Georgios Kaissis, Ali Ertürk, Stephan Günnemann, Bjoern H. Menze

Moreover, we benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification using the introduced vessel graph dataset.

Graph Learning

Automated Claustrum Segmentation in Human Brain MRI Using Deep Learning

1 code implementation8 Aug 2020 Hongwei Li, Aurore Menegaux, Benita Schmitz-Koep, Antonia Neubauer, Felix JB Bäuerlein, Suprosanna Shit, Christian Sorg, Bjoern Menze, Dennis Hedderich

In the last two decades, neuroscience has produced intriguing evidence for a central role of the claustrum in mammalian forebrain structure and function.


A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images

no code implementations10 Jul 2020 Stefan Gerl, Johannes C. Paetzold, Hailong He, Ivan Ezhov, Suprosanna Shit, Florian Kofler, Amirhossein Bayat, Giles Tetteh, Vasilis Ntziachristos, Bjoern Menze

Raster-scan optoacoustic mesoscopy (RSOM) is a powerful, non-invasive optical imaging technique for functional, anatomical, and molecular skin and tissue analysis.


Relation Transformer Network

2 code implementations13 Apr 2020 Rajat Koner, Suprosanna Shit, Volker Tresp

In this work, we propose a novel transformer formulation for scene graph generation and relation prediction.

Graph Generation Relation +2

Implicit Neural Solver for Time-dependent Linear PDEs with Convergence Guarantee

no code implementations8 Oct 2019 Suprosanna Shit, Abinav Ravi Venkatakrishnan, Ivan Ezhov, Jana Lipkova, Marie Piraud, Bjoern Menze

The existing implicit schemes are usually iterative and employ a general-purpose solver which may be sub-optimal for a specific class of PDEs.

Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation

1 code implementation14 Aug 2019 Fernando Navarro, Suprosanna Shit, Ivan Ezhov, Johannes Paetzold, Andrei Gafita, Jan Peeken, Stephanie Combs, Bjoern Menze

Multi-organ segmentation in whole-body computed tomography (CT) is a constant pre-processing step which finds its application in organ-specific image retrieval, radiotherapy planning, and interventional image analysis.

Computed Tomography (CT) Image Retrieval +3

Neural parameters estimation for brain tumor growth modeling

no code implementations1 Jul 2019 Ivan Ezhov, Jana Lipkova, Suprosanna Shit, Florian Kofler, Nore Collomb, Benjamin Lemasson, Emmanuel Barbier, Bjoern Menze

In this work, we propose a learning-based technique for the estimation of tumor growth model parameters from medical scans.


The Liver Tumor Segmentation Benchmark (LiTS)

6 code implementations13 Jan 2019 Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.

Benchmarking Computed Tomography (CT) +3

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