1 code implementation • 20 Apr 2024 • Ali Nasiri-Sarvi, Vincent Quoc-Huy Trinh, Hassan Rivaz, Mahdi S. Hosseini
Multi-instance learning methods have addressed this challenge, leveraging image patches to classify slides utilizing pretrained models using Self-Supervised Learning (SSL) approaches.
no code implementations • 29 Mar 2024 • Taha Koleilat, Hojat Asgariandehkordi, Hassan Rivaz, Yiming Xiao
Medical image segmentation of anatomical structures and pathology is crucial in modern clinical diagnosis, disease study, and treatment planning.
no code implementations • 25 Mar 2024 • Zirui Qiu, Hassan Rivaz, Yiming Xiao
As deep learning has become the state-of-the-art for computer-assisted diagnosis, interpretability of the automatic decisions is crucial for clinical deployment.
no code implementations • 19 Jan 2024 • Ali K. Z. Tehrani, Guy Cloutier, An Tang, Ivan M. Rosado-Mendez, Hassan Rivaz
Statistics of the envelope of the backscattered radiofrequency (RF) data can be utilized to estimate several QUS parameters.
no code implementations • 29 Aug 2023 • Zirui Qiu, Hassan Rivaz, Yiming Xiao
While deep learning techniques have provided the state-of-the-art performance in various clinical tasks, explainability regarding their decision-making process can greatly enhance the credence of these methods for safer and quicker clinical adoption.
no code implementations • 28 Aug 2023 • Mumu Aktar, Hassan Rivaz, Marta Kersten-Oertel, Yiming Xiao
Angiography is widely used to detect, diagnose, and treat cerebrovascular diseases.
no code implementations • 22 Aug 2023 • Mostafa Sharifzadeh, Habib Benali, Hassan Rivaz
In this study, we illustrate the challenge of applying this technique to plane-wave imaging, where, at shallower depths, signals from more distant elements lose relevance, and a fewer number of elements contribute to image reconstruction.
no code implementations • 22 Aug 2023 • Mostafa Sharifzadeh, Habib Benali, Hassan Rivaz
Radio frequency (RF) data contain richer information compared to other data types, such as envelope or B-mode, and employing RF data for training deep neural networks has attracted growing interest in ultrasound image processing.
no code implementations • 22 Aug 2023 • Mostafa Sharifzadeh, Sobhan Goudarzi, An Tang, Habib Benali, Hassan Rivaz
This dataset serves to mitigate the data scarcity problem in the development of deep learning-based techniques for phase aberration correction.
no code implementations • 21 Aug 2023 • Soorena Salari, Amirhossein Rasoulian, Hassan Rivaz, Yiming Xiao
Early surgical treatment of brain tumors is crucial in reducing patient mortality rates.
no code implementations • 18 Aug 2023 • Sepideh K. Gharamaleki, Brandon Helfield, Hassan Rivaz
The results manifest an improvement both in precision and recall and the final super-resolution maps compared to DETR.
no code implementations • 14 Aug 2023 • Parinaz Roshanzamir, Hassan Rivaz, Joshua Ahn, Hamza Mirza, Neda Naghdi, Meagan Anstruther, Michele C. Battié, Maryse Fortin, Yiming Xiao
Recent developments in deep learning (DL) techniques have led to great performance improvement in medical image segmentation tasks, especially with the latest Transformer model and its variants.
no code implementations • 26 Jul 2023 • Soorena Salari, Amirhossein Rasoulian, Hassan Rivaz, Yiming Xiao
In brain tumor resection, accurate removal of cancerous tissues while preserving eloquent regions is crucial to the safety and outcomes of the treatment.
no code implementations • 26 Jul 2023 • Soorena Salari, Amirhossein Rasoulian, Hassan Rivaz, Yiming Xiao
Specifically, two convolutional neural networks were trained jointly to encode image features in MRI and US scans to help match the US image patch that contain the corresponding landmarks in the MRI.
no code implementations • 12 Jun 2023 • Hojat Asgariandehkordi, Sobhan Goudarzi, Adrian Basarab, Hassan Rivaz
To improve the Peak Signal to Noise Ratio (PSNR) of the images, previous denoising methods often remove the speckles, which could be informative for radiologists and also for quantitative ultrasound.
no code implementations • 31 May 2023 • Md Ashikuzzaman, Ali K. Z. Tehrani, Hassan Rivaz
This paper proposes exploiting the effective Poisson's ratio (EPR)-based mechanical correspondence between the axial and lateral strains along with the RF data fidelity and displacement continuity to improve the lateral displacement and strain estimation accuracies.
no code implementations • 10 Mar 2023 • Mostafa Sharifzadeh, Habib Benali, Hassan Rivaz
Phase aberration is one of the primary sources of image quality degradation in ultrasound, which is induced by spatial variations in sound speed across the heterogeneous medium.
no code implementations • 24 Feb 2023 • Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, Hayley Whitson, Hassan Rivaz
Quantitative ultrasound (QUS) aims to find properties of scatterers which are related to the tissue microstructure.
no code implementations • 16 Dec 2022 • Ali K. Z. Tehrani, Md Ashikuzzaman, Hassan Rivaz
Many modifications have been proposed to improve the displacement estimation of CNNs for USE in the axial direction.
no code implementations • 31 Oct 2022 • Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, Hassan Rivaz
Speckle statistics are the QUS parameters that describe the first order statistics of ultrasound (US) envelope data.
no code implementations • 31 Oct 2022 • Ali K. Z. Tehrani, Hassan Rivaz
This method took into account the range of the feasible lateral strain defined by the rules of physics of motion and employed a regularization strategy to improve the lateral strains.
no code implementations • 23 Sep 2022 • Sepideh K. Gharamaleki, Brandon Helfield, Hassan Rivaz
Ultrasound Localization Microscopy (ULM) is an emerging technique that employs the localization of echogenic microbubbles (MBs) to finely sample and image the microcirculation beyond the diffraction limit of ultrasound imaging.
no code implementations • 21 Sep 2022 • Md Ashikuzzaman, Brandon Helfield, Hassan Rivaz
Ultrasound localization microscopy (ULM) refers to a promising medical imaging modality that systematically leverages the advantages of contrast-enhanced ultrasound (CEUS) to surpass the diffraction barrier and delineate the microvascular map.
1 code implementation • 12 Sep 2022 • Nima Masoumi, Hassan Rivaz, M. Omair Ahmad, Yiming Xiao
Results: The proposed algorithm, named DiffeoRaptor, was validated with three public databases for the tasks of brain and abdominal image registration while comparing the results against three state-of-the-art techniques, including FLASH, NiftyReg, and Symmetric image normalization (SyN).
no code implementations • 13 Aug 2022 • Noushin Jafarpisheh, Laura Castaneda Martinez, Hayley Whitson, Ivan M. Rosado-Mendez, Hassan Rivaz
Pulse-echo Quantitative ultrasound (PEQUS), which estimates the quantitative properties of tissue microstructure, entails estimating the average attenuation and the backscatter coefficient (BSC).
no code implementations • 13 Jul 2022 • Bahareh Behboodi, Francois-Xavier Carton, Matthieu Chabanas, Sandrine de Ribaupierre, Ole Solheim, Bodil K. R. Munkvold, Hassan Rivaz, Yiming Xiao, Ingerid Reinertsen
The proposed dataset contains tumor tissues and resection cavity annotations of the iUS images.
1 code implementation • 16 Jun 2022 • Sobhan Goudarzi, Adrian Basarab, Hassan Rivaz
During the past few years, inverse problem formulations of ultrasound beamforming have attracted a growing interest.
no code implementations • 8 Jun 2022 • Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, Hassan Rivaz
Quantitative Ultrasound (QUS) provides important information about the tissue properties.
no code implementations • 5 Jun 2022 • Ali K. Z. Tehrani, Hassan Rivaz
Recently, the architecture of the optical flow networks has been modified to be able to use RF data.
no code implementations • 30 Mar 2022 • Md Ashikuzzaman, Timothy J. Hall, Hassan Rivaz
The data term associated with the existing techniques takes only the amplitude similarity into account and hence is not sufficiently robust to the outlier samples present in the RF frames under consideration.
no code implementations • 31 Jan 2022 • Ali K. Z. Tehrani, Mostafa Sharifzadeh, Emad Boctor, Hassan Rivaz
We also show that the network fine-tuned by our proposed method using experimental phantom data performs well on in vivo data similar to the network fine-tuned on in vivo data.
no code implementations • 16 Jan 2022 • Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, Hassan Rivaz
In conventional methods, the envelope data is divided into small overlapping windows (a strategy here we refer to as patching), and statistical parameters such as SNR and skewness are employed to classify each patch of envelope data.
no code implementations • 12 Jan 2022 • Md Ashikuzzaman, Hassan Rivaz
ADMM empowers the proposed algorithm to use different techniques for optimizing different parts of the cost function and obtain high-contrast strain images with smooth background and sharp boundaries.
no code implementations • 7 Jan 2022 • Sobhan Goudarzi, Hassan Rivaz
On one hand, the transmitted ultrasound beam gets attenuated as propagates through the tissue.
no code implementations • 6 Jan 2022 • Md Ashikuzzaman, Hassan Rivaz
To resolve these issues, herein, we propose a novel TDE algorithm where instead of L2-, L1-norms of both first- and second-order displacement derivatives are taken into account to devise the continuity functional.
no code implementations • 28 Dec 2021 • Sobhan Goudarzi, Adrian Basarab, Hassan Rivaz
In parallel to beamforming approaches, deconvolution methods have also been explored in ultrasound imaging to mitigate the adverse effects of PSF.
no code implementations • 27 Oct 2021 • Behnaz Gheflati, Hassan Rivaz
In the past decade, convolutional neural networks (CNNs) have emerged as the method of choice in vision applications and have shown excellent potential in automatic classification of US images.
no code implementations • 16 Oct 2021 • Abdelrahman Zayed, Hassan Rivaz
In the inference stage, we use dynamic programming (DP) to compute an initial displacement estimate of around 1% of the samples, and then decompose this sparse displacement into a linear combination of the 12 displacement modes.
no code implementations • 27 Sep 2021 • Sobhan Goudarzi, Hassan Rivaz
Simulation test results confirm that the proposed method reconstructs images with a high quality in terms of resolution and contrast, which are also visually similar to the proposed ground-truth image.
no code implementations • 21 Sep 2021 • Mostafa Sharifzadeh, Habib Benali, Hassan Rivaz
Convolutional neural networks (CNNs) have attracted a rapidly growing interest in a variety of different processing tasks in the medical ultrasound community.
no code implementations • 21 Sep 2021 • Noushin Jafarpisheh, Ivan M. Rosado-Mendez, Timothy J. Hall, Hassan Rivaz
In the first technique, we cast scatterer size distribution as an optimization problem, and efficiently solve it using a linear system of equations.
no code implementations • 21 Sep 2021 • Mostafa Sharifzadeh, Ali K. Z. Tehrani, Habib Benali, Hassan Rivaz
A common issue in exploiting simulated ultrasound data for training neural networks is the domain shift problem, where the trained models on synthetic data are not generalizable to clinical data.
no code implementations • 20 Sep 2021 • Bahareh Behboodi, Hassan Rivaz, Susan Lalondrelle, Emma Harris
In the second scenario, our proposed network was trained using all the planes of each 3D volume.
no code implementations • 9 Aug 2021 • Hamza Rasaee, Hassan Rivaz
Ultrasound is a non-invasive imaging modality that can be conveniently used to classify suspicious breast nodules and potentially detect the onset of breast cancer.
1 code implementation • 22 Jul 2021 • Mostafa Sharifzadeh, Habib Benali, Hassan Rivaz
To the best of our knowledge, this problem has not been studied in ultrasound image segmentation or even more broadly in ultrasound images.
1 code implementation • 12 Apr 2021 • Annika Reinke, Minu D. Tizabi, Carole H. Sudre, Matthias Eisenmann, Tim Rädsch, Michael Baumgartner, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, Matthew Blaschko, Florian Buettner, M. Jorge Cardoso, Jianxu Chen, Veronika Cheplygina, Evangelia Christodoulou, Beth Cimini, Gary S. Collins, Sandy Engelhardt, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Fred Hamprecht, Daniel A. Hashimoto, Doreen Heckmann-Nötzel, Peter Hirsch, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, A. Emre Kavur, Hannes Kenngott, Jens Kleesiek, Andreas Kleppe, Sven Kohler, Florian Kofler, Annette Kopp-Schneider, Thijs Kooi, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, David Moher, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, M. Alican Noyan, Jens Petersen, Gorkem Polat, Susanne M. Rafelski, Nasir Rajpoot, Mauricio Reyes, Nicola Rieke, Michael Riegler, Hassan Rivaz, Julio Saez-Rodriguez, Clara I. Sánchez, Julien Schroeter, Anindo Saha, M. Alper Selver, Lalith Sharan, Shravya Shetty, Maarten van Smeden, Bram Stieltjes, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul Jäger, Lena Maier-Hein
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
no code implementations • 22 Dec 2020 • Md Ashikuzzaman, Noushin Jafarpisheh, Sunil Rottoo, Pierre Brisson, Hassan Rivaz
This paper introduces a fast and computationally efficient implementation of linear KF to improve the measurement accuracy of an optical tracking system with high temporal resolution.
Robotics
no code implementations • 19 Dec 2020 • Morteza Mirzaei, Amir Asif, Hassan Rivaz
Despite capabilities of elastography techniques in estimating displacement in both axial and lateral directions, estimation of axial displacement is more accurate than lateral direction due to higher sampling frequency, higher resolution and having a carrier signal propagating in the axial direction.
no code implementations • 4 Dec 2020 • Ali K. Z. Tehrani, Mina Amiri, Ivan M. Rosado-Mendez, Timothy J. Hall, Hassan Rivaz
The results also show that the proposed network is able to work with different imaging parameters with no need for a reference phantom.
no code implementations • 2 Jul 2020 • Ali K. Z. Tehrani, Morteza Mirzaei, Hassan Rivaz
Convolutional Neural Networks (CNN) have been found to have great potential in optical flow problems thanks to an abundance of data available for training a deep network.
Image and Video Processing
no code implementations • 19 Feb 2020 • Mina Amiri, Rupert Brooks, Hassan Rivaz
In this study, we investigated the effect of fine-tuning different layers of a U-Net which was trained on segmentation of natural images in breast ultrasound image segmentation.
no code implementations • 17 Feb 2020 • Abdelrahman Zayed, Guy Cloutier, Hassan Rivaz
Ultrasound elastography is used to estimate the mechanical properties of the tissue by monitoring its response to an internal or external force.
no code implementations • 21 Jan 2020 • Bahareh Behboodi, Mina Amiri, Rupert Brooks, Hassan Rivaz
Ultrasound (US) is one of the most commonly used imaging modalities in both diagnosis and surgical interventions due to its low-cost, safety, and non-invasive characteristic.
no code implementations • 13 Nov 2019 • Abdelrahman Zayed, Hassan Rivaz
This work focuses on strain imaging in quasi-static elastography, where the tissue undergoes slow deformations and strain images are estimated as a surrogate for elasticity modulus.
no code implementations • 13 Nov 2019 • Abdelrahman Zayed, Hassan Rivaz
Time delay estimation (TDE) is a critical and challenging step in all ultrasound elastography methods.
no code implementations • 24 Apr 2019 • Bahareh Behboodi, Hassan Rivaz
Therefore, in this study, we propose the use of simulated ultrasound (US) images for training the U-Net deep learning segmentation architecture and test on tissue-mimicking phantom data collected by an ultrasound machine.