Search Results for author: Hessam Sokooti

Found 8 papers, 4 papers with code

Joint Registration and Segmentation via Multi-Task Learning for Adaptive Radiotherapy of Prostate Cancer

no code implementations5 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.

Image Registration Medical Image Registration +1

ASL to PET Translation by a Semi-supervised Residual-based Attention-guided Convolutional Neural Network

1 code implementation8 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.

SSIM Translation

Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)

3 code implementations6 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.

Tumor Segmentation

3D Convolutional Neural Networks Image Registration Based on Efficient Supervised Learning from Artificial Deformations

1 code implementation27 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.

Image Registration

Fast Dynamic Perfusion and Angiography Reconstruction using an end-to-end 3D Convolutional Neural Network

no code implementations24 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.

SSIM

A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration

no code implementations17 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.

Affine Image Registration Image Registration

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