1 code implementation • 3 Apr 2024 • Yunjie Chen, Jelmer M. Wolterink, Olaf M. Neve, Stephan R. Romeijn, Berit M. Verbist, Erik F. Hensen, Qian Tao, Marius Staring
In the proposed method, each tumor is represented as a signed distance function (SDF) conditioned on a low-dimensional latent code.
1 code implementation • 12 Jan 2024 • Jingnan Jia, Marius Staring, Berend C. Stoel
In response to a concerning trend of selectively emphasizing metrics in medical image segmentation (MIS) studies, we introduce \texttt{seg-metrics}, an open-source Python package for standardized MIS model evaluation.
1 code implementation • 6 Sep 2023 • Yunjie Chen, Marius Staring, Olaf M. Neve, Stephan R. Romeijn, Erik F. Hensen, Berit M. Verbist, Jelmer M. Wolterink, Qian Tao
In this paper, we propose Conditional Neural fields with Shift modulation (CoNeS), a model that takes voxel coordinates as input and learns a representation of the target images for multi-sequence MRI translation.
1 code implementation • 28 Mar 2023 • Viktor van der Valk, Douwe Atsma, Roderick Scherptong, Marius Staring
Electrocardiography is the most common method to investigate the condition of the heart through the observation of cardiac rhythm and electrical activity, for both diagnosis and monitoring purposes.
no code implementations • 2 Feb 2023 • Yunjie Chen, Marius Staring, Jelmer M. Wolterink, Qian Tao
In this paper, we propose a novel MR image translation solution based on local implicit neural representations.
1 code implementation • 1 Nov 2021 • Prerak Mody, Nicolas Chaves-de-Plaza, Klaus Hildebrandt, Rene van Egmond, Huib de Ridder, Marius Staring
However, in a QA context, a model should also have high uncertainty in inaccurate regions and low uncertainty in accurate regions.
no code implementations • 15 Oct 2021 • Jingnan Jia, Marius Staring, Irene Hernández-Girón, Lucia J. M. Kroft, Anne A. Schouffoer, Berend C. Stoel
We used 227 3D CT scans to train and validate the first network, and the resulting 1135 axial slices were used in the second network.
no code implementations • 5 May 2021 • Mohamed S. Elmahdy, Laurens Beljaards, Sahar Yousefi, Hessam Sokooti, Fons Verbeek, U. A. van der Heide, Marius Staring
In this paper, we formulate registration and segmentation as a joint problem via a Multi-Task Learning (MTL) setting, allowing these tasks to leverage their strengths and mitigate their weaknesses through the sharing of beneficial information.
no code implementations • 22 Apr 2021 • Jingnan Jia, Zhiwei Zhai, M. Els Bakker, I. Hernandez Giron, Marius Staring, Berend C. Stoel
Deep multi-task learning is expected to utilize labels of multiple different structures.
1 code implementation • 8 Mar 2021 • Sahar Yousefi, Hessam Sokooti, Wouter M. Teeuwisse, Dennis F. R. Heijtel, Aart J. Nederveen, Marius Staring, Matthias J. P. van Osch
To tackle this problem, we present a new semi-supervised multitask CNN which is trained on both paired data, i. e. ASL and PET scans, and unpaired data, i. e. only ASL scans, which alleviates the problem of training a network on limited paired data.
3 code implementations • 6 Dec 2020 • Sahar Yousefi, Hessam Sokooti, Mohamed S. Elmahdy, Irene M. Lips, Mohammad T. Manzuri Shalmani, Roel T. Zinkstok, Frank J. W. M. Dankers, Marius Staring
The proposed network achieved a $\mathrm{DSC}$ value of $0. 79 \pm 0. 20$, a mean surface distance of $5. 4 \pm 20. 2mm$ and $95\%$ Hausdorff distance of $14. 7 \pm 25. 0mm$ for 287 test scans, demonstrating promising results with a simplified clinical workflow based on CT alone.
no code implementations • MIDL 2019 • Laurens Beljaards, Mohamed S. Elmahdy, Fons Verbeek, Marius Staring
The obtained performance as well as the inference speed make this a promising candidate for daily re-contouring in adaptive radiotherapy, potentially reducing treatment-related side effects and improving quality-of-life after treatment.
no code implementations • 15 Apr 2020 • Nicola Pezzotti, Sahar Yousefi, Mohamed S. Elmahdy, Jeroen van Gemert, Christophe Schülke, Mariya Doneva, Tim Nielsen, Sergey Kastryulin, Boudewijn P. F. Lelieveldt, Matthias J. P. van Osch, Elwin de Weerdt, Marius Staring
In this work, we present the application of adaptive intelligence to accelerate MR acquisition.
no code implementations • 17 Feb 2020 • Mohamed S. Elmahdy, Tanuj Ahuja, U. A. van der Heide, Marius Staring
We investigate a transfer learning approach, fine-tuning the baseline CNN model to a specific patient, based on imaging acquired in earlier treatment fractions.
1 code implementation • Preprint 2019 • Zhiwei Zhai, Marius Staring, Xuhui Zhou, Qiuxia Xie, Xiaojuan Xiao, M. Els Bakker, Lucia J. Kroft, Boudewijn P. F. Lelieveldt, Gudula J.A.M. Boon, Frederikus A. Klok, Berend C. Stoel
In conclusion, the proposed CNN-GCN method combines local image information with graph connectivity information, improving pulmonary A/V separation over a baseline CNN method, approaching the performance of human observers.
Ranked #1 on Pulmonary Artery–Vein Classification on SunYs
3D Medical Imaging Segmentation Pulmonary Artery–Vein Classification
1 code implementation • 27 Aug 2019 • Hessam Sokooti, Bob de Vos, Floris Berendsen, Mohsen Ghafoorian, Sahar Yousefi, Boudewijn P. F. Lelieveldt, Ivana Isgum, Marius Staring
We propose a supervised nonrigid image registration method, trained using artificial displacement vector fields (DVF), for which we propose and compare three network architectures.
no code implementations • 24 Aug 2019 • Sahar Yousefi, Lydiane Hirschler, Merlijn van der Plas, Mohamed S. Elmahdy, Hessam Sokooti, Matthias Van Osch, Marius Staring
Hadamard time-encoded pseudo-continuous arterial spin labeling (te-pCASL) is a signal-to-noise ratio (SNR)-efficient MRI technique for acquiring dynamic pCASL signals that encodes the temporal information into the labeling according to a Hadamard matrix.
no code implementations • 28 Jun 2019 • Mohamed S. Elmahdy, Jelmer M. Wolterink, Hessam Sokooti, Ivana Išgum, Marius Staring
Joint image registration and segmentation has long been an active area of research in medical imaging.
1 code implementation • 18 May 2019 • Hessam Sokooti, Gorkem Saygili, Ben Glocker, Boudewijn P. F. Lelieveldt, Marius Staring
This paper proposes a new automatic method to predict the registration error in a quantitative manner, and is applied to chest CT scans.
1 code implementation • MICCAI2018 2018 • Zhiwei Zhai, Marius Staring, Hideki Ota, Berend C. Stoel
Quantifying morphological changes may provide a non-invasive assessment of treatment effects in CTEPH patients, consistent with hemodynamic changes from invasive RHC.
no code implementations • 17 Sep 2018 • Bob D. de Vos, Floris F. Berendsen, Max A. Viergever, Hessam Sokooti, Marius Staring, Ivana Isgum
To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for \textit{unsupervised} affine and deformable image registration.
no code implementations • 20 Apr 2017 • Bob D. de Vos, Floris F. Berendsen, Max A. Viergever, Marius Staring, Ivana Išgum
In this work we propose a deep learning network for deformable image registration (DIRNet).
no code implementations • 11 Dec 2016 • Sahar Yousefi, M. T. Manzuri Shalmani, Jeremy Lin, Marius Staring
Recently, there has been a considerable attention given to the motion detection problem due to the explosive growth of its applications in video analysis and surveillance systems.