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1. A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey.

Author

Vahdati, Sanaz, Khosravi, Bardia, Mahmoudi, Elham, Zhang, Kuan, Rouzrokh, Pouria, Faghani, Shahriar, Moassefi, Mana, Tahmasebi, Aylin, Andriole, Katherine, Chang, Peter, Farahani, Keyvan, Flores, Mona, Folio, Les, Houshmand, Sina, Giger, Maryellen, Gichoya, Judy, and Erickson, Bradley

Subjects
Artificial intelligence, Data curation, Open source, Toolkits, Artificial Intelligence, Diagnostic Imaging, Humans, Societies, Medical, Medical Informatics, Surveys and Questionnaires, Data Curation, Machine Learning
Abstract

In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases. Data curation transforms raw data into a valuable, organized, and dependable resource and is a fundamental process to the success of machine learning and analytical projects. Considering the plethora of available tools for data curation in different stages, it is crucial to stay informed about the most relevant tools within specific research areas. In the current work, we propose a descriptive outline for different steps of data curation while we furnish compilations of tools collected from a survey applied among members of the Society of Imaging Informatics (SIIM) for each of these stages. This collection has the potential to enhance the decision-making process for researchers as they select the most appropriate tool for their specific tasks.

Published
2024

2. Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation

Author

LaBella, Dominic, Schumacher, Katherine, Mix, Michael, Leu, Kevin, McBurney-Lin, Shan, Nedelec, Pierre, Villanueva-Meyer, Javier, Shapey, Jonathan, Vercauteren, Tom, Chia, Kazumi, Al-Salihi, Omar, Leu, Justin, Halasz, Lia, Velichko, Yury, Wang, Chunhao, Kirkpatrick, John, Floyd, Scott, Reitman, Zachary J., Mullikin, Trey, Bagci, Ulas, Sachdev, Sean, Hattangadi-Gluth, Jona A., Seibert, Tyler, Farid, Nikdokht, Puett, Connor, Pease, Matthew W., Shiue, Kevin, Anwar, Syed Muhammad, Faghani, Shahriar, Haider, Muhammad Ammar, Warman, Pranav, Albrecht, Jake, Jakab, András, Moassefi, Mana, Chung, Verena, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Huang, Christina, Coley, Aaron, Ghanta, Siddharth, Schneider, Alex, Sharp, Conrad, Saluja, Rachit, Kofler, Florian, Lohmann, Philipp, Vollmuth, Phillipp, Gagnon, Louis, Adewole, Maruf, Li, Hongwei Bran, Kazerooni, Anahita Fathi, Tahon, Nourel Hoda, Anazodo, Udunna, Moawad, Ahmed W., Menze, Bjoern, Linguraru, Marius George, Aboian, Mariam, Wiestler, Benedikt, Baid, Ujjwal, Conte, Gian-Marco, Rauschecker, Andreas M., Nada, Ayman, Abayazeed, Aly H., Huang, Raymond, de Verdier, Maria Correia, Rudie, Jeffrey D., Bakas, Spyridon, and Calabrese, Evan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or postoperative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery. Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space, accompanied by a single-label "target volume" representing the gross tumor volume (GTV) and any at-risk postoperative site. Target volume annotations adhere to established radiotherapy planning protocols, ensuring consistency across cases and institutions. For preoperative meningiomas, the target volume encompasses the entire GTV and associated nodular dural tail, while for postoperative cases, it includes at-risk resection cavity margins as determined by the treating institution. Case annotations were reviewed and approved by expert neuroradiologists and radiation oncologists. Participating teams will develop, containerize, and evaluate automated segmentation models using this comprehensive dataset. Model performance will be assessed using an adapted lesion-wise Dice Similarity Coefficient and the 95% Hausdorff distance. The top-performing teams will be recognized at the Medical Image Computing and Computer Assisted Intervention Conference in October 2024. BraTS-MEN-RT is expected to significantly advance automated radiotherapy planning by enabling precise tumor segmentation and facilitating tailored treatment, ultimately improving patient outcomes., Comment: 14 pages, 9 figures, 1 table

Published
2024

3. The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI

Author

de Verdier, Maria Correia, Saluja, Rachit, Gagnon, Louis, LaBella, Dominic, Baid, Ujjwall, Tahon, Nourel Hoda, Foltyn-Dumitru, Martha, Zhang, Jikai, Alafif, Maram, Baig, Saif, Chang, Ken, D'Anna, Gennaro, Deptula, Lisa, Gupta, Diviya, Haider, Muhammad Ammar, Hussain, Ali, Iv, Michael, Kontzialis, Marinos, Manning, Paul, Moodi, Farzan, Nunes, Teresa, Simon, Aaron, Sollmann, Nico, Vu, David, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Chai, Rongrong, Chung, Verena, Faghani, Shahriar, Farahani, Keyvan, Kazerooni, Anahita Fathi, Iglesias, Eugenio, Kofler, Florian, Li, Hongwei, Linguraru, Marius George, Menze, Bjoern, Moawad, Ahmed W., Velichko, Yury, Wiestler, Benedikt, Altes, Talissa, Basavasagar, Patil, Bendszus, Martin, Brugnara, Gianluca, Cho, Jaeyoung, Dhemesh, Yaseen, Fields, Brandon K. K., Garrett, Filip, Gass, Jaime, Hadjiiski, Lubomir, Hattangadi-Gluth, Jona, Hess, Christopher, Houk, Jessica L., Isufi, Edvin, Layfield, Lester J., Mastorakos, George, Mongan, John, Nedelec, Pierre, Nguyen, Uyen, Oliva, Sebastian, Pease, Matthew W., Rastogi, Aditya, Sinclair, Jason, Smith, Robert X., Sugrue, Leo P., Thacker, Jonathan, Vidic, Igor, Villanueva-Meyer, Javier, White, Nathan S., Aboian, Mariam, Conte, Gian Marco, Dale, Anders, Sabuncu, Mert R., Seibert, Tyler M., Weinberg, Brent, Abayazeed, Aly, Huang, Raymond, Turk, Sevcan, Rauschecker, Andreas M., Farid, Nikdokht, Vollmuth, Philipp, Nada, Ayman, Bakas, Spyridon, Calabrese, Evan, and Rudie, Jeffrey D.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care., Comment: 10 pages, 4 figures, 1 table

Published
2024

4. BraTS-Path Challenge: Assessing Heterogeneous Histopathologic Brain Tumor Sub-regions

Author

Bakas, Spyridon, Thakur, Siddhesh P., Faghani, Shahriar, Moassefi, Mana, Baid, Ujjwal, Chung, Verena, Pati, Sarthak, Innani, Shubham, Baheti, Bhakti, Albrecht, Jake, Karargyris, Alexandros, Kassem, Hasan, Nasrallah, MacLean P., Ahrendsen, Jared T., Barresi, Valeria, Gubbiotti, Maria A., López, Giselle Y., Lucas, Calixto-Hope G., Miller, Michael L., Cooper, Lee A. D., Huse, Jason T., and Bell, William R.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Glioblastoma is the most common primary adult brain tumor, with a grim prognosis - median survival of 12-18 months following treatment, and 4 months otherwise. Glioblastoma is widely infiltrative in the cerebral hemispheres and well-defined by heterogeneous molecular and micro-environmental histopathologic profiles, which pose a major obstacle in treatment. Correctly diagnosing these tumors and assessing their heterogeneity is crucial for choosing the precise treatment and potentially enhancing patient survival rates. In the gold-standard histopathology-based approach to tumor diagnosis, detecting various morpho-pathological features of distinct histology throughout digitized tissue sections is crucial. Such "features" include the presence of cellular tumor, geographic necrosis, pseudopalisading necrosis, areas abundant in microvascular proliferation, infiltration into the cortex, wide extension in subcortical white matter, leptomeningeal infiltration, regions dense with macrophages, and the presence of perivascular or scattered lymphocytes. With these features in mind and building upon the main aim of the BraTS Cluster of Challenges https://www.synapse.org/brats2024, the goal of the BraTS-Path challenge is to provide a systematically prepared comprehensive dataset and a benchmarking environment to develop and fairly compare deep-learning models capable of identifying tumor sub-regions of distinct histologic profile. These models aim to further our understanding of the disease and assist in the diagnosis and grading of conditions in a consistent manner.

Published
2024

5. Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

Author

LaBella, Dominic, Baid, Ujjwal, Khanna, Omaditya, McBurney-Lin, Shan, McLean, Ryan, Nedelec, Pierre, Rashid, Arif, Tahon, Nourel Hoda, Altes, Talissa, Bhalerao, Radhika, Dhemesh, Yaseen, Godfrey, Devon, Hilal, Fathi, Floyd, Scott, Janas, Anastasia, Kazerooni, Anahita Fathi, Kirkpatrick, John, Kent, Collin, Kofler, Florian, Leu, Kevin, Maleki, Nazanin, Menze, Bjoern, Pajot, Maxence, Reitman, Zachary J., Rudie, Jeffrey D., Saluja, Rachit, Velichko, Yury, Wang, Chunhao, Warman, Pranav, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Anwar, Syed Muhammad, Bergquist, Timothy, Chen, Sully Francis, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Khalili, Nastaran, Iglesias, Juan Eugenio, Jiang, Zhifan, Johanson, Elaine, Van Leemput, Koen, Li, Hongwei Bran, Linguraru, Marius George, Liu, Xinyang, Mahtabfar, Aria, Meier, Zeke, Moawad, Ahmed W., Mongan, John, Piraud, Marie, Shinohara, Russell Takeshi, Wiggins, Walter F., Abayazeed, Aly H., Akinola, Rachel, Jakab, András, Bilello, Michel, de Verdier, Maria Correia, Crivellaro, Priscila, Davatzikos, Christos, Farahani, Keyvan, Freymann, John, Hess, Christopher, Huang, Raymond, Lohmann, Philipp, Moassefi, Mana, Pease, Matthew W., Vollmuth, Phillipp, Sollmann, Nico, Diffley, David, Nandolia, Khanak K., Warren, Daniel I., Hussain, Ali, Fehringer, Pascal, Bronstein, Yulia, Deptula, Lisa, Stein, Evan G., Taherzadeh, Mahsa, de Oliveira, Eduardo Portela, Haughey, Aoife, Kontzialis, Marinos, Saba, Luca, Turner, Benjamin, Brüßeler, Melanie M. T., Ansari, Shehbaz, Gkampenis, Athanasios, Weiss, David Maximilian, Mansour, Aya, Shawali, Islam H., Yordanov, Nikolay, Stein, Joel M., Hourani, Roula, Moshebah, Mohammed Yahya, Abouelatta, Ahmed Magdy, Rizvi, Tanvir, Willms, Klara, Martin, Dann C., Okar, Abdullah, D'Anna, Gennaro, Taha, Ahmed, Sharifi, Yasaman, Faghani, Shahriar, Kite, Dominic, Pinho, Marco, Haider, Muhammad Ammar, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Alonso-Basanta, Michelle, Villanueva-Meyer, Javier, Rauschecker, Andreas M., Nada, Ayman, Aboian, Mariam, Flanders, Adam E., Wiestler, Benedikt, Bakas, Spyridon, and Calabrese, Evan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps., Comment: 16 pages, 11 tables, 10 figures, MICCAI

Published
2024

6. RadRotator: 3D Rotation of Radiographs with Diffusion Models

Author

Rouzrokh, Pouria, Khosravi, Bardia, Faghani, Shahriar, Mulford, Kellen L., Taunton, Michael J., Erickson, Bradley J., and Wyles, Cody C.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Transforming two-dimensional (2D) images into three-dimensional (3D) volumes is a well-known yet challenging problem for the computer vision community. In the medical domain, a few previous studies attempted to convert two or more input radiographs into computed tomography (CT) volumes. Following their effort, we introduce a diffusion model-based technology that can rotate the anatomical content of any input radiograph in 3D space, potentially enabling the visualization of the entire anatomical content of the radiograph from any viewpoint in 3D. Similar to previous studies, we used CT volumes to create Digitally Reconstructed Radiographs (DRRs) as the training data for our model. However, we addressed two significant limitations encountered in previous studies: 1. We utilized conditional diffusion models with classifier-free guidance instead of Generative Adversarial Networks (GANs) to achieve higher mode coverage and improved output image quality, with the only trade-off being slower inference time, which is often less critical in medical applications; and 2. We demonstrated that the unreliable output of style transfer deep learning (DL) models, such as Cycle-GAN, to transfer the style of actual radiographs to DRRs could be replaced with a simple yet effective training transformation that randomly changes the pixel intensity histograms of the input and ground-truth imaging data during training. This transformation makes the diffusion model agnostic to any distribution variations of the input data pixel intensity, enabling the reliable training of a DL model on input DRRs and applying the exact same model to conventional radiographs (or DRRs) during inference., Comment: Website: https://pouriarouzrokh.github.io/RadRotator Online demo: https://huggingface.co/spaces/Pouriarouzrokh/RadRotator Article information: 16 pages, 11 figures

Published
2024

7. CONFLARE: CONFormal LArge language model REtrieval

Author

Rouzrokh, Pouria, Faghani, Shahriar, Gamble, Cooper U., Shariatnia, Moein, and Erickson, Bradley J.

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Retrieval-augmented generation (RAG) frameworks enable large language models (LLMs) to retrieve relevant information from a knowledge base and incorporate it into the context for generating responses. This mitigates hallucinations and allows for the updating of knowledge without retraining the LLM. However, RAG does not guarantee valid responses if retrieval fails to identify the necessary information as the context for response generation. Also, if there is contradictory content, the RAG response will likely reflect only one of the two possible responses. Therefore, quantifying uncertainty in the retrieval process is crucial for ensuring RAG trustworthiness. In this report, we introduce a four-step framework for applying conformal prediction to quantify retrieval uncertainty in RAG frameworks. First, a calibration set of questions answerable from the knowledge base is constructed. Each question's embedding is compared against document embeddings to identify the most relevant document chunks containing the answer and record their similarity scores. Given a user-specified error rate ({\alpha}), these similarity scores are then analyzed to determine a similarity score cutoff threshold. During inference, all chunks with similarity exceeding this threshold are retrieved to provide context to the LLM, ensuring the true answer is captured in the context with a (1-{\alpha}) confidence level. We provide a Python package that enables users to implement the entire workflow proposed in our work, only using LLMs and without human intervention., Comment: Github code: https://github.com/Mayo-Radiology-Informatics-Lab/conflare

Published
2024

9. A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation.

Author

LaBella, Dominic, Khanna, Omaditya, McBurney-Lin, Shan, Mclean, Ryan, Nedelec, Pierre, Rashid, Arif, Tahon, Nourel, Altes, Talissa, Baid, Ujjwal, Bhalerao, Radhika, Dhemesh, Yaseen, Floyd, Scott, Godfrey, Devon, Hilal, Fathi, Janas, Anastasia, Kazerooni, Anahita, Kent, Collin, Kirkpatrick, John, Kofler, Florian, Leu, Kevin, Maleki, Nazanin, Menze, Bjoern, Pajot, Maxence, Reitman, Zachary, Rudie, Jeffrey, Saluja, Rachit, Velichko, Yury, Wang, Chunhao, Warman, Pranav, Sollmann, Nico, Diffley, David, Nandolia, Khanak, Warren, Daniel, Hussain, Ali, Fehringer, John, Bronstein, Yulia, Deptula, Lisa, Stein, Evan, Taherzadeh, Mahsa, Portela de Oliveira, Eduardo, Haughey, Aoife, Kontzialis, Marinos, Saba, Luca, Turner, Benjamin, Brüßeler, Melanie, Ansari, Shehbaz, Gkampenis, Athanasios, Weiss, David, Mansour, Aya, Shawali, Islam, Yordanov, Nikolay, Stein, Joel, Hourani, Roula, Moshebah, Mohammed, Abouelatta, Ahmed, Rizvi, Tanvir, Willms, Klara, Martin, Dann, Okar, Abdullah, DAnna, Gennaro, Taha, Ahmed, Sharifi, Yasaman, Faghani, Shahriar, Kite, Dominic, Pinho, Marco, Haider, Muhammad, Alonso-Basanta, Michelle, Rauschecker, Andreas, Nada, Ayman, Aboian, Mariam, Flanders, Adam, Bakas, Spyridon, Calabrese, Evan, and Villanueva-Meyer, Javier

Subjects
Meningioma, Humans, Magnetic Resonance Imaging, Meningeal Neoplasms, Male, Female, Image Processing, Computer-Assisted, Middle Aged, Aged
Abstract

Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by expert manually refined segmentations of three distinct meningioma sub-compartments: enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Basic demographic data are provided including age at time of initial imaging, sex, and CNS WHO grade. The goal of releasing this dataset is to facilitate the development of automated computational methods for meningioma segmentation and expedite their incorporation into clinical practice, ultimately targeting improvement in the care of meningioma patients.

Published
2024

10. Checklist for Reproducibility of Deep Learning in Medical Imaging

Author

Moassefi, Mana, Singh, Yashbir, Conte, Gian Marco, Khosravi, Bardia, Rouzrokh, Pouria, Vahdati, Sanaz, Safdar, Nabile, Moy, Linda, Kitamura, Felipe, Gentili, Amilcare, Lakhani, Paras, Kottler, Nina, Halabi, Safwan S., Yacoub, Joseph H., Hou, Yuankai, Younis, Khaled, Erickson, Bradley J., Krupinski, Elizabeth, and Faghani, Shahriar

Published
2024
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12. The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

Author

Moawad, Ahmed W., Janas, Anastasia, Baid, Ujjwal, Ramakrishnan, Divya, Saluja, Rachit, Ashraf, Nader, Jekel, Leon, Amiruddin, Raisa, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Aneja, Sanjay, Anwar, Syed Muhammad, Bergquist, Timothy, Calabrese, Evan, Chiang, Veronica, Chung, Verena, Conte, Gian Marco Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Familiar, Ariana, Farahani, Keyvan, Iglesias, Juan Eugenio, Jiang, Zhifan, Johanson, Elaine, Kazerooni, Anahita Fathi, Kofler, Florian, Krantchev, Kiril, LaBella, Dominic, Van Leemput, Koen, Li, Hongwei Bran, Linguraru, Marius George, Link, Katherine E., Liu, Xinyang, Maleki, Nazanin, Meier, Zeke, Menze, Bjoern H, Moy, Harrison, Osenberg, Klara, Piraud, Marie, Reitman, Zachary, Shinohara, Russel Takeshi, Tahon, Nourel hoda, Nada, Ayman, Velichko, Yuri S., Wang, Chunhao, Wiestler, Benedikt, Wiggins, Walter, Shafique, Umber, Willms, Klara, Avesta, Arman, Bousabarah, Khaled, Chakrabarty, Satrajit, Gennaro, Nicolo, Holler, Wolfgang, Kaur, Manpreet, LaMontagne, Pamela, Lin, MingDe, Lost, Jan, Marcus, Daniel S., Maresca, Ryan, Merkaj, Sarah, Nada, Ayaman, Pedersen, Gabriel Cassinelli, von Reppert, Marc, Sotiras, Aristeidis, Teytelboym, Oleg, Tillmans, Niklas, Westerhoff, Malte, Youssef, Ayda, Godfrey, Devon, Floyd, Scott, Rauschecker, Andreas, Villanueva-Meyer, Javier, Pflüger, Irada, Cho, Jaeyoung, Bendszus, Martin, Brugnara, Gianluca, Cramer, Justin, Perez-Carillo, Gloria J. Guzman, Johnson, Derek R., Kam, Anthony, Kwan, Benjamin Yin Ming, Lai, Lillian, Lall, Neil U., Memon, Fatima, Patro, Satya Narayana, Petrovic, Bojan, So, Tiffany Y., Thompson, Gerard, Wu, Lei, Schrickel, E. Brooke, Bansal, Anu, Barkhof, Frederik, Besada, Cristina, Chu, Sammy, Druzgal, Jason, Dusoi, Alexandru, Farage, Luciano, Feltrin, Fabricio, Fong, Amy, Fung, Steve H., Gray, R. Ian, Ikuta, Ichiro, Iv, Michael, Postma, Alida A., Mahajan, Amit, Joyner, David, Krumpelman, Chase, Letourneau-Guillon, Laurent, Lincoln, Christie M., Maros, Mate E., Miller, Elka, Morón, Fanny, Nimchinsky, Esther A., Ozsarlak, Ozkan, Patel, Uresh, Rohatgi, Saurabh, Saha, Atin, Sayah, Anousheh, Schwartz, Eric D., Shih, Robert, Shiroishi, Mark S., Small, Juan E., Tanwar, Manoj, Valerie, Jewels, Weinberg, Brent D., White, Matthew L., Young, Robert, Zohrabian, Vahe M., Azizova, Aynur, Brüßeler, Melanie Maria Theresa, Fehringer, Pascal, Ghonim, Mohanad, Ghonim, Mohamed, Gkampenis, Athanasios, Okar, Abdullah, Pasquini, Luca, Sharifi, Yasaman, Singh, Gagandeep, Sollmann, Nico, Soumala, Theodora, Taherzadeh, Mahsa, Yordanov, Nikolay, Vollmuth, Philipp, Foltyn-Dumitru, Martha, Malhotra, Ajay, Abayazeed, Aly H., Dellepiane, Francesco, Lohmann, Philipp, Pérez-García, Víctor M., Elhalawani, Hesham, Al-Rubaiey, Sanaria, Armindo, Rui Duarte, Ashraf, Kholod, Asla, Moamen M., Badawy, Mohamed, Bisschop, Jeroen, Lomer, Nima Broomand, Bukatz, Jan, Chen, Jim, Cimflova, Petra, Corr, Felix, Crawley, Alexis, Deptula, Lisa, Elakhdar, Tasneem, Shawali, Islam H., Faghani, Shahriar, Frick, Alexandra, Gulati, Vaibhav, Haider, Muhammad Ammar, Hierro, Fátima, Dahl, Rasmus Holmboe, Jacobs, Sarah Maria, Hsieh, Kuang-chun Jim, Kandemirli, Sedat G., Kersting, Katharina, Kida, Laura, Kollia, Sofia, Koukoulithras, Ioannis, Li, Xiao, Abouelatta, Ahmed, Mansour, Aya, Maria-Zamfirescu, Ruxandra-Catrinel, Marsiglia, Marcela, Mateo-Camacho, Yohana Sarahi, McArthur, Mark, McDonnell, Olivia, McHugh, Maire, Moassefi, Mana, Morsi, Samah Mostafa, Muntenu, Alexander, Nandolia, Khanak K., Naqvi, Syed Raza, Nikanpour, Yalda, Alnoury, Mostafa, Nouh, Abdullah Mohamed Aly, Pappafava, Francesca, Patel, Markand D., Petrucci, Samantha, Rawie, Eric, Raymond, Scott, Roohani, Borna, Sabouhi, Sadeq, Sanchez-Garcia, Laura M., Shaked, Zoe, Suthar, Pokhraj P., Altes, Talissa, Isufi, Edvin, Dhermesh, Yaseen, Gass, Jaime, Thacker, Jonathan, Tarabishy, Abdul Rahman, Turner, Benjamin, Vacca, Sebastiano, Vilanilam, George K., Warren, Daniel, Weiss, David, Worede, Fikadu, Yousry, Sara, Lerebo, Wondwossen, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Bakas, Spyridon, Rudie, Jeffrey D., and Aboian, Mariam

Subjects
Quantitative Biology - Other Quantitative Biology, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space.The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.

Published
2023

14. Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report

Author

Rouzrokh, Pouria, Khosravi, Bardia, Faghani, Shahriar, Moassefi, Mana, Vahdati, Sanaz, and Erickson, Bradley J.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Despite the ever-increasing interest in applying deep learning (DL) models to medical imaging, the typical scarcity and imbalance of medical datasets can severely impact the performance of DL models. The generation of synthetic data that might be freely shared without compromising patient privacy is a well-known technique for addressing these difficulties. Inpainting algorithms are a subset of DL generative models that can alter one or more regions of an input image while matching its surrounding context and, in certain cases, non-imaging input conditions. Although the majority of inpainting techniques for medical imaging data use generative adversarial networks (GANs), the performance of these algorithms is frequently suboptimal due to their limited output variety, a problem that is already well-known for GANs. Denoising diffusion probabilistic models (DDPMs) are a recently introduced family of generative networks that can generate results of comparable quality to GANs, but with diverse outputs. In this paper, we describe a DDPM to execute multiple inpainting tasks on 2D axial slices of brain MRI with various sequences, and present proof-of-concept examples of its performance in a variety of evaluation scenarios. Our model and a public online interface to try our tool are available at: https://github.com/Mayo-Radiology-Informatics-Lab/MBTI, Comment: 17 pages, 7 figures

Published
2022

19. The burden of neurological conditions in north Africa and the Middle East, 1990–2019: a systematic analysis of the Global Burden of Disease Study 2019

Author

Avan, Abolfazl, Feigin, Valery L, Bennett, Derrick A., Steinmetz, Jaimie D, Hachinski, Vladimir, Stranges, Saverio, Owolabi, Mayowa O, Aali, Amirali, Abbasi-Kangevari, Mohsen, Abbasi-Kangevari, Zeinab, Abd-Allah, Foad, Abdollahzade, Sina, Abidi, Hassan, Abolhassani, Hassan, Abualhasan, Ahmed, Abu-Gharbieh, Eman, Abu-Rmeileh, Niveen ME, Abu-Zaid, Ahmed, Ahmad, Aqeel, Ahmadi, Sepideh, Ahmed, Luai A, Ajami, Marjan, Al Hamad, Hanadi, Alanezi, Fahad Mashhour, Alanzi, Turki M, Alimohamadi, Yousef, Aljunid, Syed Mohamed, Al-Raddadi, Rajaa M, Amiri, Sohrab, Arabloo, Jalal, Arulappan, Judie, Arumugam, Ashokan, Asadi-Pooya, Ali A, Athar, Mohammad, Athari, Seyyed Shamsadin, Atout, Maha Moh'd Wahbi, Azadnajafabad, Sina, Azangou-Khyavy, Mohammadreza, Azari Jafari, Amirhossein, Azzam, Ahmed Y, Baghcheghi, Nayereh, Bagherieh, Sara, Baltatu, Ovidiu Constantin, Bazmandegan, Gholamreza, Bhojaraja, Vijayalakshmi S, Bijani, Ali, Bitaraf, Saeid, Calina, Daniela, Darwish, Amira Hamed, Djalalinia, Shirin, Doheim, Mohamed Fahmy, Dorostkar, Fariba, Eini, Ebrahim, El Nahas, Nevine, El Sayed, Iman, Elhadi, Muhammed, Elmonem, Mohamed A, Eskandarieh, Sharareh, Faghani, Shahriar, Fallahzadeh, Aida, Farahmand, Mohammad, Ghafourifard, Mansour, Ghamari, Seyyed-Hadi, Gholami, Ali, Ghozy, Sherief, Goleij, Pouya, Hadei, Mostafa, Hafezi-Nejad, Nima, Haj-Mirzaian, Arvin, Halwani, Rabih, Hamidi, Samer, Hasaballah, Ahmed I, Hassan, Amr, Hedna, Khedidja, Hegazy, Mohamed I, Heidari-Soureshjani, Reza, Hosseini, Mohammad-Salar, Hoveidamanesh, Soodabeh, Jahrami, Haitham, Jamshidi, Elham, Javaheri, Tahereh, Jayapal, Sathish Kumar, Kalankesh, Laleh R, Kalhor, Rohollah, Kamiab, Zahra, Keykhaei, Mohammad, Khader, Yousef Saleh, Khan, Maseer, Khan, Moien AB, Khatatbeh, Khayat Kashani, Hamid Reza, Khosravi, Ahmad, Kompani, Farzad, Koohestani, Hamid Reza, Larijani, Bagher, Lasrado, Savita, Magdy Abd El Razek, Mohammed, Malekpour, Mohammad-Reza, Malik, Ahmad Azam, Mansournia, Mohammad Ali, Mardi, Parham, Maroufi, Seyed Farzad, Masoudi, Sahar, Mayeli, Mahsa, Mehrabi Nasab, Entezar, Menezes, Ritesh G, Mirmoeeni, Seyyedmohammadsadeq, Mirza-Aghazadeh-Attari, Mohammad, Mobarakabadi, Maryam, Mohammadi, Esmaeil, Mohammadi, Soheil, Mohan, Syam, Mokdad, Ali H, Momtazmanesh, Sara, Montazeri, Fateme, Moradi Sarabi, Mostafa, Moraga, Paula, Morovatdar, Negar, Motaghinejad, Majid, Naghavi, Mohsen, Natto, Zuhair S, Nejadghaderi, Seyed Aria, Noroozi, Nafise, Okati-Aliabad, Hassan, Pazoki Toroudi, Hamidreza, Perna, Simone, Piradov, Michael A, Pourahmadi, Mohammadreza, Rafiei, Alireza, Rahimi-Movaghar, Vafa, Rahmani, Amir Masoud, Rahmani, Shayan, Rahmanian, Vahid, Rajabpour-Sanati, Ali, Rao, Chythra R, Rashidi, Mohammad-Mahdi, Rawassizadeh, Reza, Razeghian-Jahromi, Iman, Redwan, Elrashdy Moustafa Mohamed, Rezaee, Malihe, Rezaei, Nazila, Rezaei, Negar, Rezaei, Nima, Rezaeian, Mohsen, Rikhtegar, Reza, Saad, Aly M A, Saddik, Basema, Sadeghi, Masoumeh, Sadeghian, Saeid, Saeedi Moghaddam, Sahar, Sahebkar, Amirhossein, Salahi, Saina, Salahi, Sarvenaz, Samy, Abdallah M, Sanadgol, Nima, Sarveazad, Arash, Sathian, Brijesh, Saylan, Mete, Shahbandi, Ataollah, Shahrokhi, Shayan, Shams-Beyranvand, Mehran, Shanawaz, Mohd, Sharifi-Rad, Javad, Sheikhi, Rahim Ali, Shetty, Jeevan K, Shobeiri, Parnian, Shorofi, Seyed Afshin, Siabani, Soraya, Tabatabaei, Seyyed Mohammad, Taheri Abkenar, Yasaman, Taheri Soodejani, Moslem, Temsah, Mohamad-Hani, Vakilian, Alireza, Valadan Tahbaz, Sahel, Valizadeh, Rohollah, Vaziri, Siavash, Vo, Bay, Yahyazadeh Jabbari, Seyed Hossein, Yesiltepe, Metin, Zaki, Nazar, Zare, Iman, Zare Dehnavi, Ali, and Zoladl, Mohammad

Published
2024
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27. Global, regional, and national burden of rheumatoid arthritis, 1990–2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021

Author

Black, Rachel J, Cross, Marita, Haile, Lydia M, Culbreth, Garland T, Steinmetz, Jaimie D, Hagins, Hailey, Kopec, Jacek A, Brooks, Peter M, Woolf, Anthony D, Ong, Kanyin Liane, Kopansky-Giles, Deborah R, Dreinhoefer, Karsten E, Betteridge, Neil, Aali, Amirali, Abbasifard, Mitra, Abbasi-Kangevari, Mohsen, Abdurehman, Ame Mehadi, Abedi, Aidin, Abidi, Hassan, Aboagye, Richard Gyan, Abolhassani, Hassan, Abu-Gharbieh, Eman, Abu-Zaid, Ahmed, Adamu, Kidist, Addo, Isaac Yeboah, Adesina, Miracle Ayomikun, Adnani, Qorinah Estiningtyas Sakilah, Afzal, Muhammad Sohail, Ahmed, Ayman, Aithala, Janardhana P, Akhlaghdoust, Meisam, Alemayehu, Astawus, Alvand, Saba, Alvis-Zakzuk, Nelson J, Amu, Hubert, Antony, Benny, Arabloo, Jalal, Aravkin, Aleksandr Y, Arulappan, Judie, Ashraf, Tahira, Athari, Seyyed Shamsadin, Azadnajafabad, Sina, Badawi, Alaa, Baghcheghi, Nayereh, Baig, Atif Amin, Balta, Asaminew Birhanu, Banach, Maciej, Banik, Palash Chandra, Barrow, Amadou, Bashiri, Azadeh, Bearne, Lindsay M, Bekele, Alehegn, Bensenor, Isabela M, Berhie, Alemshet Yirga, Bhagavathula, Akshaya Srikanth, Bhardwaj, Pankaj, Bhat, Ajay Nagesh, Bhojaraja, Vijayalakshmi S, Bitaraf, Saeid, Bodicha, Belay Boda Abule, Botelho, João Silva, Briggs, Andrew M, Buchbinder, Rachelle, Castañeda-Orjuela, Carlos A, Charalampous, Periklis, Chattu, Vijay Kumar, Coberly, Kaleb, Cruz-Martins, Natália, Dadras, Omid, Dai, Xiaochen, de Luca, Katie, Dessalegn, Fikadu Nugusu, Dessie, Gashaw, Dhimal, Meghnath, Digesa, Lankamo Ena, Diress, Mengistie, Doku, Paul Narh, Edinur, Hisham Atan, Ekholuenetale, Michael, Elhadi, Muhammed, El-Sherbiny, Yasser Mohamed, Etaee, Farshid, Ezzeddini, Rana, Faghani, Shahriar, Filip, Irina, Fischer, Florian, Fukumoto, Takeshi, Ganesan, Balasankar, Gebremichael, Mathewos Alemu, Gerema, Urge, Getachew, Motuma Erena, Ghashghaee, Ahmad, Gill, Tiffany K, Gupta, Bhawna, Gupta, Sapna, Gupta, Veer Bala, Gupta, Vivek Kumar, Halwani, Rabih, Hannan, Md Abdul, Haque, Shafiul, Harlianto, Netanja I, Harorani, Mehdi, Hasaballah, Ahmed I, Hassen, Mohammed Bheser, Hay, Simon I, Hayat, Khezar, Heidari, Golnaz, Hezam, Kamal, Hill, Catherine L, Hiraike, Yuta, Horita, Nobuyuki, Hoveidaei, Amir Human, Hsiao, Alexander Kevin, Hsieh, Evelyn, Hussain, Salman, Iavicoli, Ivo, Ilic, Irena M, Islam, Sheikh Mohammed Shariful, Ismail, Nahlah Elkudssiah, Iwagami, Masao, Jakovljevic, Mihajlo, Jani, Chinmay T, Jeganathan, Jayakumar, Joseph, Nitin, Kadashetti, Vidya, Kandel, Himal, Kanko, Tesfaye K, Karaye, Ibraheem M, Khajuria, Himanshu, Khan, Md Jobair, Khan, Moien AB, Khanali, Javad, Khatatbeh, Moawiah Mohammad, Khubchandani, Jagdish, Kim, Yun Jin, Kisa, Adnan, Kolahi, Ali-Asghar, Kompani, Farzad, Koohestani, Hamid Reza, Koyanagi, Ai, Krishan, Kewal, Kuddus, Mohammed, Kumar, Narinder, Kuttikkattu, Ambily, Larijani, Bagher, Lim, Stephen S, Lo, Justin, Machado, Vanessa Sintra, Mahajan, Preetam Bhalchandra, Majeed, Azeem, Malakan Rad, Elaheh, Malik, Ahmad Azam, Mansournia, Mohammad Ali, Mathews, Elezebeth, Mendes, José João, Mentis, Alexios-Fotios A, Mesregah, Mohamed Kamal, Mestrovic, Tomislav, Mirghaderi, Seyed Peyman, Mirrakhimov, Erkin M, Misganaw, Awoke, Mohamadkhani, Ashraf, Mohammed, Shafiu, Mokdad, Ali H, Moniruzzaman, Md, Montasir, Ahmed Al, Mulu, Getaneh Baye, Murillo-Zamora, Efrén, Murray, Christopher J L, Mustafa, Ghulam, Naghavi, Mohsen, Nair, Tapas Sadasivan, Naqvi, Atta Abbas, Natto, Zuhair S, Nayak, Biswa Prakash, Neupane, Subas, Nguyen, Cuong Tat, Niazi, Robina Khan, Nzoputam, Ogochukwu Janet, Oh, In-Hwan, Okati-Aliabad, Hassan, Okonji, Osaretin Christabel, Olufadewa, Isaac Iyinoluwa, Owolabi, Mayowa O, Pacheco-Barrios, Kevin, Padubidri, Jagadish Rao, Patel, Jay, Pathan, Aslam Ramjan, Pawar, Shrikant, Pedersini, Paolo, Perianayagam, Arokiasamy, Petcu, Ionela-Roxana, Qattea, Ibrahim, Radfar, Amir, Rafiei, Alireza, Rahman, Mohammad Hifz Ur, Rahmanian, Vahid, Rashedi, Vahid, Rashidi, Mohammad-Mahdi, Ratan, Zubair Ahmed, Rawaf, Salman, Razeghinia, Mohammad Sadegh, Redwan, Elrashdy Moustafa Mohamed, Renzaho, Andre M N, Rezaei, Nazila, Rezaei, Nima, Riad, Abanoub, Saad, Aly M A, Saddik, Basema, Saeed, Umar, Safary, Azam, Sahebazzamani, Maryam, Sahebkar, Amirhossein, Sahoo, Harihar, Salek Farrokhi, Amir, Saqib, Muhammad Arif Nadeem, Seylani, Allen, Shahabi, Saeed, Shaikh, Masood Ali, Shashamo, Bereket Beyene, Shetty, Adithi, Shetty, Jeevan K, Shigematsu, Mika, Shivarov, Velizar, Shobeiri, Parnian, Sibhat, Migbar Mekonnen, Sinaei, Ehsan, Singh, Ambrish, Singh, Jasvinder A, Singh, Paramdeep, Singh, Surjit, Siraj, Md Shahjahan, Skryabina, Anna Aleksandrovna, Slater, Helen, Smith, Amanda E, Solomon, Yonatan, Soltani-Zangbar, Mohammad Sadegh, Tabish, Mohammad, Tan, Ker-Kan, Tat, Nathan Y, Tehrani-Banihashemi, Arash, Tharwat, Samar, Tovani-Palone, Marcos Roberto, Tusa, Biruk Shalmeno, Valadan Tahbaz, Sahel, Valdez, Pascual R, Valizadeh, Rohollah, Vaziri, Siavash, Vollset, Stein Emil, Wu, Ai-Min, Yada, Dereje Y, Yehualashet, Sisay Shewasinad, Yonemoto, Naohiro, You, Yuyi, Yunusa, Ismaeel, Zangiabadian, Moein, Zare, Iman, Zarrintan, Armin, Zhang, Zhi-Jiang, Zhong, Chenwen, Zoladl, Mohammad, Vos, Theo, and March, Lyn M

Published
2023
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28. Global, regional, and national incidence of six major immune-mediated inflammatory diseases: findings from the global burden of disease study 2019

Author

Wu, Dongze, Jin, Yingzhao, Xing, Yuhan, Abate, Melsew Dagne, Abbasian, Mohammadreza, Abbasi-Kangevari, Mohsen, Abbasi-Kangevari, Zeinab, Abd-Allah, Foad, Abdelmasseh, Michael, Abdollahifar, Mohammad-Amin, Abdulah, Deldar Morad, Abedi, Aidin, Abedi, Vida, Abidi, Hassan, Aboagye, Richard Gyan, Abolhassani, Hassan, Abuabara, Katrina, Abyadeh, Morteza, Addo, Isaac Yeboah, Adeniji, Kayode Nelson, Adepoju, Abiola Victor, Adesina, Miracle Ayomikun, Sakilah Adnani, Qorinah Estiningtyas, Afarideh, Mohsen, Aghamiri, Shahin, Agodi, Antonella, Agrawal, Anurag, Aguilera Arriagada, Constanza Elizabeth, Ahmad, Aqeel, Ahmad, Danish, Ahmad, Sajjad, Ahmad, Sohail, Ahmadi, Ali, Ahmed, Ali, Ahmed, Ayman, Aithala, Janardhana P., Ajadi, Abdullateef Abiodun, Ajami, Marjan, Akbarzadeh-Khiavi, Mostafa, Alahdab, Fares, AlBataineh, Mohammad T., Alemi, Sharifullah, Saeed Al-Gheethi, Adel Ali, Ali, Liaqat, Alif, Sheikh Mohammad, Almazan, Joseph Uy, Almustanyir, Sami, Alqahtani, Jaber S., Alqasmi, Ibrahim, Khan Altaf, Ihsan Ullah, Alvis-Guzman, Nelson, Alvis-Zakzuk, Nelson J., Al-Worafi, Yaser Mohammed, Aly, Hany, Amani, Reza, Amu, Hubert, Amusa, Ganiyu Adeniyi, Andrei, Catalina Liliana, Ansar, Adnan, Ansariniya, Hossein, Anyasodor, Anayochukwu Edward, Arabloo, Jalal, Arefnezhad, Reza, Arulappan, Judie, Asghari-Jafarabadi, Mohammad, Ashraf, Tahira, Atata, Jamila Abdulhamid, Athari, Seyyed Shamsadin, Atlaw, Daniel, Wahbi Atout, Maha Moh'd, Aujayeb, Avinash, Awan, Asma Tahir, Ayatollahi, Haleh, Azadnajafabad, Sina, Azzam, Ahmed Y., Badawi, Alaa, Badiye, Ashish D., Bagherieh, Sara, Baig, Atif Amin, Bantie, Berihun Bantie, Barchitta, Martina, Bardhan, Mainak, Barker-Collo, Suzanne Lyn, Barone-Adesi, Francesco, Batra, Kavita, Bayileyegn, Nebiyou Simegnew, Behnoush, Amir Hossein, Belgaumi, Uzma Iqbal, Bemanalizadeh, Maryam, Bensenor, Isabela M., Beyene, Kebede A., Bhagavathula, Akshaya Srikanth, Bhardwaj, Pankaj, Bhaskar, Sonu, Bhat, Ajay Nagesh, Bitaraf, Saeid, Bitra, Veera R., Boloor, Archith, Bora, Kaustubh, Botelho, João Silva, Buchbinder, Rachelle, Calina, Daniela, Cámera, Luis Alberto, Carvalho, Andre F., Kai Chan, Jeffrey Shi, Chattu, Vijay Kumar, Abebe, Endeshaw Chekol, Chichagi, Fatemeh, Choi, Sungchul, Chou, Tzu-Chieh, Chu, Dinh-Toi, Coberly, Kaleb, Costa, Vera Marisa, Couto, Rosa A.S., Cruz-Martins, Natália, Dadras, Omid, Dai, Xiaochen, Damiani, Giovanni, Dascalu, Ana Maria, Dashti, Mohsen, Debela, Sisay Abebe, Dellavalle, Robert Paul, Demetriades, Andreas K., Demlash, Alemayehu Anley, Deng, Xinlei, Desai, Hardik Dineshbhai, Desai, Rupak, Rahman Dewan, Syed Masudur, Dey, Sourav, Dharmaratne, Samath Dhamminda, Diaz, Daniel, Dibas, Mahmoud, Dinis-Oliveira, Ricardo Jorge, Diress, Mengistie, Do, Thanh Chi, Doan, Duy Khanh, Dodangeh, Masoud, Dodangeh, Milad, Dongarwar, Deepa, Dube, John, Dziedzic, Arkadiusz Marian, Ed-Dra, Abdelaziz, Edinur, Hisham Atan, Eissazade, Negin, Ekholuenetale, Michael, Ekundayo, Temitope Cyrus, Elemam, Noha Mousaad, Elhadi, Muhammed, Elmehrath, Ahmed O., Abdou Elmeligy, Omar Abdelsadek, Emamverdi, Mehdi, Emeto, Theophilus I., Esayas, Hawi Leul, Eshetu, Habitu Birhan, Etaee, Farshid, Fagbamigbe, Adeniyi Francis, Faghani, Shahriar, Fakhradiyev, Ildar Ravisovich, Fatehizadeh, Ali, Fathi, Mobina, Feizkhah, Alireza, Fekadu, Ginenus, Fereidouni, Mohammad, Fereshtehnejad, Seyed-Mohammad, Fernandes, João C., Ferrara, Pietro, Fetensa, Getahun, Filip, Irina, Fischer, Florian, Foroutan, Behzad, Foroutan, Masoud, Fukumoto, Takeshi, Ganesan, Balasankar, Belete Gemeda, Belete Negese, Ghamari, Seyyed-Hadi, Ghasemi, MohammadReza, Gholamalizadeh, Maryam, Gill, Tiffany K., Gillum, Richard F., Goldust, Mohamad, Golechha, Mahaveer, Goleij, Pouya, Golinelli, Davide, Goudarzi, Houman, Guan, Shi-Yang, Guo, Yang, Gupta, Bhawna, Gupta, Veer Bala, Gupta, Vivek Kumar, Haddadi, Rasool, Hadi, Najah R., Halwani, Rabih, Haque, Shafiul, Hasan, Ikramul, Hashempour, Reza, Hassan, Amr, Hassan, Treska S., Hassanzadeh, Sara, Hassen, Mohammed Bheser, Haubold, Johannes, Hayat, Khezar, Heidari, Golnaz, Heidari, Mohammad, Heidari-Soureshjani, Reza, Herteliu, Claudiu, Hessami, Kamran, Hezam, Kamal, Hiraike, Yuta, Holla, Ramesh, Hosseini, Mohammad-Salar, Huynh, Hong-Han, Hwang, Bing-Fang, Ibitoye, Segun Emmanuel, Ilic, Irena M., Ilic, Milena D., Iranmehr, Arad, Iravanpour, Farideh, Ismail, Nahlah Elkudssiah, Iwagami, Masao, Iwu, Chidozie C.D., Jacob, Louis, Jafarinia, Morteza, Jafarzadeh, Abdollah, Jahankhani, Kasra, Jahrami, Haitham, Jakovljevic, Mihajlo, Jamshidi, Elham, Jani, Chinmay T., Janodia, Manthan Dilipkumar, Jayapal, Sathish Kumar, Jayaram, Shubha, Jeganathan, Jayakumar, Jonas, Jost B., Joseph, Abel, Joseph, Nitin, Joshua, Charity Ehimwenma, Vaishali, K., Kaambwa, Billingsley, Kabir, Ali, Kabir, Zubair, Kadashetti, Vidya, Kaliyadan, Feroze, Kalroozi, Fatemeh, Kamal, Vineet Kumar, Kandel, Amit, Kandel, Himal, Kanungo, Srikanta, Karami, Jafar, Karaye, Ibraheem M., Karimi, Hanie, Kasraei, Hengameh, Kazemian, Sina, Kebede, Sewnet Adem, Keikavoosi-Arani, Leila, Keykhaei, Mohammad, Khader, Yousef Saleh, Khajuria, Himanshu, Khamesipour, Faham, Khan, Ejaz Ahmad, Khan, Imteyaz A., Khan, Maseer, Khan, Md Jobair, Khan, Moien A.B., Khan, Muhammad Arslan, Khatatbeh, Haitham, Khatatbeh, Moawiah Mohammad, Khateri, Sorour, Khayat Kashani, Hamid Reza, Kim, Min Seo, Kisa, Adnan, Kisa, Sezer, Koh, Hyun Yong, Kolkhir, Pavel, Korzh, Oleksii, Kotnis, Ashwin Laxmikant, Koul, Parvaiz A., Koyanagi, Ai, Krishan, Kewal, Kuddus, Mohammed, Kulkarni, Vishnutheertha Vishnutheertha, Kumar, Narinder, Kundu, Satyajit, Kurmi, Om P., La Vecchia, Carlo, Lahariya, Chandrakant, Laksono, Tri, Lám, Judit, Latief, Kamaluddin, Lauriola, Paolo, Lawal, Basira Kankia, Thu Le, Thao Thi, Bich Le, Trang Thi, Lee, Munjae, Lee, Seung Won, Lee, Wei-Chen, Lee, Yo Han, Lenzi, Jacopo, Levi, Miriam, Li, Wei, Ligade, Virendra S., Lim, Stephen S., Liu, Gang, Liu, Xuefeng, Llanaj, Erand, Lo, Chun-Han, Machado, Vanessa Sintra, Maghazachi, Azzam A., Mahmoud, Mansour Adam, Mai, Tuan A., Majeed, Azeem, Sanaye, Pantea Majma, Makram, Omar Mohamed, Rad, Elaheh Malakan, Malhotra, Kashish, Malik, Ahmad Azam, Malik, Iram, Mallhi, Tauqeer Hussain, Malta, Deborah Carvalho, Mansournia, Mohammad Ali, Mantovani, Lorenzo Giovanni, Martorell, Miquel, Masoudi, Sahar, Masoumi, Seyedeh Zahra, Mathangasinghe, Yasith, Mathews, Elezebeth, Mathioudakis, Alexander G., Maugeri, Andrea, Mayeli, Mahsa, Carabeo Medina, John Robert, Meles, Gebrekiros Gebremichael, Mendes, José João, Menezes, Ritesh G., Mestrovic, Tomislav, Michalek, Irmina Maria, Micheletti Gomide Nogueira de Sá, Ana Carolina, Mihretie, Ephrem Tesfaye, Nhat Minh, Le Huu, Mirfakhraie, Reza, Mirrakhimov, Erkin M., Misganaw, Awoke, Mohamadkhani, Ashraf, Mohamed, Nouh Saad, Mohammadi, Faezeh, Mohammadi, Soheil, Mohammed, Salahuddin, Mohammed, Shafiu, Mohan, Syam, Mohseni, Anita, Mokdad, Ali H., Momtazmanesh, Sara, Monasta, Lorenzo, Moni, Mohammad Ali, Moniruzzaman, Md, Moradi, Yousef, Morovatdar, Negar, Mostafavi, Ebrahim, Mousavi, Parsa, Mukoro, George Duke, Mulita, Admir, Mulu, Getaneh Baye, Murillo-Zamora, Efrén, Musaigwa, Fungai, Mustafa, Ghulam, Muthu, Sathish, Nainu, Firzan, Nangia, Vinay, Swamy, Sreenivas Narasimha, Natto, Zuhair S., Navaraj, Perumalsamy, Nayak, Biswa Prakash, Nazri-Panjaki, Athare, Negash, Hadush, Nematollahi, Mohammad Hadi, Nguyen, Dang H., Hien Nguyen, Hau Thi, Nguyen, Hien Quang, Nguyen, Phat Tuan, Nguyen, Van Thanh, Niazi, Robina Khan, Nikolouzakis, Taxiarchis Konstantinos, Nnyanzi, Lawrence Achilles, Noreen, Mamoona, Nzoputam, Chimezie Igwegbe, Nzoputam, Ogochukwu Janet, Oancea, Bogdan, Oh, In-Hwan, Okati-Aliabad, Hassan, Okonji, Osaretin Christabel, Okwute, Patrick Godwin, Olagunju, Andrew T., Olatubi, Matthew Idowu, Olufadewa, Isaac Iyinoluwa, Ordak, Michal, Otstavnov, Nikita, Owolabi, Mayowa O., Mahesh, P.A., Padubidri, Jagadish Rao, Pak, Anton, Pakzad, Reza, Palladino, Raffaele, Pana, Adrian, Pantazopoulos, Ioannis, Papadopoulou, Paraskevi, Pardhan, Shahina, Parthasarathi, Ashwaghosha, Pashaei, Ava, Patel, Jay, Pathan, Aslam Ramjan, Patil, Shankargouda, Paudel, Uttam, Pawar, Shrikant, Pedersini, Paolo, Pensato, Umberto, Pereira, David M., Pereira, Jeevan, Pereira, Maria Odete, Pereira, Renato B., Peres, Mario F.P., Perianayagam, Arokiasamy, Perna, Simone, Petcu, Ionela-Roxana, Pezeshki, Parmida Sadat, Pham, Hoang Tran, Philip, Anil K., Piradov, Michael A., Podder, Indrashis, Podder, Vivek, Poddighe, Dimitri, Sady Prates, Elton Junio, Qattea, Ibrahim, Radfar, Amir, Raee, Pourya, Rafiei, Alireza, Raggi, Alberto, Rahim, Fakher, Rahimi, Mehran, Rahimifard, Mahban, Rahimi-Movaghar, Vafa, Rahman, Md Obaidur, Ur Rahman, Mohammad Hifz, Rahman, Mosiur, Rahman, Muhammad Aziz, Rahmani, Amir Masoud, Rahmani, Mohamed, Rahmani, Shayan, Rahmanian, Vahid, Ramasubramani, Premkumar, Rancic, Nemanja, Rao, Indu Ramachandra, Rashedi, Sina, Rashid, Ahmed Mustafa, Ravikumar, Nakul, Rawaf, Salman, Mohamed Redwan, Elrashdy Moustafa, Rezaei, Nazila, Rezaei, Negar, Rezaei, Nima, Rezaeian, Mohsen, Ribeiro, Daniela, Rodrigues, Mónica, Buendia Rodriguez, Jefferson Antonio, Roever, Leonardo, Romero-Rodríguez, Esperanza, Saad, Aly M.A., Saddik, Basema, Sadeghian, Saeid, Saeed, Umar, Safary, Azam, Safdarian, Mahdi, Safi, Sher Zaman, Saghazadeh, Amene, Sagoe, Dominic, Sharif-Askari, Fatemeh Saheb, Sharif-Askari, Narjes Saheb, Sahebkar, Amirhossein, Sahoo, Harihar, Sahraian, Mohammad Ali, Sajid, Mirza Rizwan, Sakhamuri, Sateesh, Sakshaug, Joseph W., Saleh, Mohamed A., Salehi, Leili, Salehi, Sana, Farrokhi, Amir Salek, Samadzadeh, Sara, Samargandy, Saad, Samieefar, Noosha, Samy, Abdallah M., Sanadgol, Nima, Sanjeev, Rama Krishna, Sawhney, Monika, Saya, Ganesh Kumar, Schuermans, Art, Senthilkumaran, Subramanian, Sepanlou, Sadaf G., Sethi, Yashendra, Shafie, Mahan, Shah, Humaira, Shahid, Izza, Shahid, Samiah, Shaikh, Masood Ali, Sharfaei, Sadaf, Sharma, Manoj, Shayan, Maryam, Shehata, Hatem Samir, Sheikh, Aziz, Shetty, Jeevan K., Shin, Jae Il, Shirkoohi, Reza, Shitaye, Nebiyu Aniley, Shivakumar, K.M., Shivarov, Velizar, Shobeiri, Parnian, Siabani, Soraya, Sibhat, Migbar Mekonnen, Siddig, Emmanuel Edwar, Simpson, Colin R., Sinaei, Ehsan, Singh, Harpreet, Singh, Inderbir, Singh, Jasvinder A., Singh, Paramdeep, Singh, Surjit, Siraj, Md Shahjahan, Al Mamun Sohag, Abdullah, Solanki, Ranjan, Solikhah, Solikhah, Solomon, Yonatan, Soltani-Zangbar, Mohammad Sadegh, Sun, Jing, Szeto, Mindy D., Tabarés-Seisdedos, Rafael, Tabatabaei, Seyyed Mohammad, Tabish, Mohammad, Taheri, Ensiyeh, Tahvildari, Azin, Talaat, Iman M., Lukenze Tamuzi, Jacques J.L., Tan, Ker-Kan, Tat, Nathan Y., Oliaee, Razieh Tavakoli, Tavasol, Arian, Temsah, Mohamad-Hani, Thangaraju, Pugazhenthan, Tharwat, Samar, Tibebu, Nigusie Selomon, Vera Ticoalu, Jansje Henny, Tillawi, Tala, Tiruye, Tenaw Yimer, Tiyuri, Amir, Tovani-Palone, Marcos Roberto, Tripathi, Manjari, Tsegay, Guesh Mebrahtom, Tualeka, Abdul Rohim, Ty, Sree Sudha, Ubah, Chukwudi S., Ullah, Saif, Ullah, Sana, Umair, Muhammad, Umakanthan, Srikanth, Upadhyay, Era, Vahabi, Seyed Mohammad, Vaithinathan, Asokan Govindaraj, Tahbaz, Sahel Valadan, Valizadeh, Rohollah, Varthya, Shoban Babu, Vasankari, Tommi Juhani, Venketasubramanian, Narayanaswamy, Verras, Georgios-Ioannis, Villafañe, Jorge Hugo, Vlassov, Vasily, Vo, Danh Cao, Waheed, Yasir, Waris, Abdul, Welegebrial, Brhane Gebrehiwot, Westerman, Ronny, Wickramasinghe, Dakshitha Praneeth, Wickramasinghe, Nuwan Darshana, Willekens, Barbara, Woldegeorgis, Beshada Zerfu, Woldemariam, Melat, Xiao, Hong, Yada, Dereje Y., Yahya, Galal, Yang, Lin, Yazdanpanah, Fereshteh, Yon, Dong Keon, Yonemoto, Naohiro, You, Yuyi, Zahir, Mazyar, Zaidi, Syed Saoud, Zangiabadian, Moein, Zare, Iman, Zeineddine, Mohammad A., Zemedikun, Dawit T., Zeru, Naod Gebrekrstos, Zhang, Chen, Zhao, Hanqing, Zhong, Chenwen, Zielińska, Magdalena, Zoladl, Mohammad, Zumla, Alimuddin, Guo, Cui, and Tam, Lai-shan

Published
2023
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32. Global, regional, and national burden of low back pain, 1990–2020, its attributable risk factors, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021

Author

Ferreira, Manuela L, de Luca, Katie, Haile, Lydia M, Steinmetz, Jaimie D, Culbreth, Garland T, Cross, Marita, Kopec, Jacek A, Ferreira, Paulo H, Blyth, Fiona M, Buchbinder, Rachelle, Hartvigsen, Jan, Wu, Ai-Min, Safiri, Saeid, Woolf, Anthony D, Collins, Gary S, Ong, Kanyin Liane, Vollset, Stein Emil, Smith, Amanda E, Cruz, Jessica A, Fukutaki, Kai Glenn, Abate, Semagn Mekonnen, Abbasifard, Mitra, Abbasi-Kangevari, Mohsen, Abbasi-Kangevari, Zeinab, Abdelalim, Ahmed, Abedi, Aidin, Abidi, Hassan, Adnani, Qorinah Estiningtyas Sakilah, Ahmadi, Ali, Akinyemi, Rufus Olusola, Alamer, Abayneh Tadesse, Alem, Adugnaw Zeleke, Alimohamadi, Yousef, Alshehri, Mansour Abdullah, Alshehri, Mohammed Mansour, Alzahrani, Hosam, Amini, Saeed, Amiri, Sohrab, Amu, Hubert, Andrei, Catalina Liliana, Andrei, Tudorel, Antony, Benny, Arabloo, Jalal, Arulappan, Judie, Arumugam, Ashokan, Ashraf, Tahira, Athari, Seyyed Shamsadin, Awoke, Nefsu, Azadnajafabad, Sina, Bärnighausen, Till Winfried, Barrero, Lope H, Barrow, Amadou, Barzegar, Akbar, Bearne, Lindsay M, Bensenor, Isabela M, Berhie, Alemshet Yirga, Bhandari, Bharti Bhandari, Bhojaraja, Vijayalakshmi S, Bijani, Ali, Bodicha, Belay Boda Abule, Bolla, Srinivasa Rao, Brazo-Sayavera, Javier, Briggs, Andrew M, Cao, Chao, Charalampous, Periklis, Chattu, Vijay Kumar, Cicuttini, Flavia M, Clarsen, Benjamin, Cuschieri, Sarah, Dadras, Omid, Dai, Xiaochen, Dandona, Lalit, Dandona, Rakhi, Dehghan, Azizallah, Demie, Takele Gezahegn G, Denova-Gutiérrez, Edgar, Dewan, Syed Masudur Rahman, Dharmaratne, Samath Dhamminda, Dhimal, Mandira Lamichhane, Dhimal, Meghnath, Diaz, Daniel, Didehdar, Mojtaba, Digesa, Lankamo Ena, Diress, Mengistie, Do, Hoa Thi, Doan, Linh Phuong, Ekholuenetale, Michael, Elhadi, Muhammed, Eskandarieh, Sharareh, Faghani, Shahriar, Fares, Jawad, Fatehizadeh, Ali, Fetensa, Getahun, Filip, Irina, Fischer, Florian, Franklin, Richard Charles, Ganesan, Balasankar, Gemeda, Belete Negese Belete, Getachew, Motuma Erena, Ghashghaee, Ahmad, Gill, Tiffany K, Golechha, Mahaveer, Goleij, Pouya, Gupta, Bhawna, Hafezi-Nejad, Nima, Haj-Mirzaian, Arvin, Hamal, Pawan Kumar, Hanif, Asif, Harlianto, Netanja I, Hasani, Hamidreza, Hay, Simon I, Hebert, Jeffrey J, Heidari, Golnaz, Heidari, Mohammad, Heidari-Soureshjani, Reza, Hlongwa, Mbuzeleni Mbuzeleni, Hosseini, Mohammad-Salar, Hsiao, Alexander Kevin, Iavicoli, Ivo, Ibitoye, Segun Emmanuel, Ilic, Irena M, Ilic, Milena D, Islam, Sheikh Mohammed Shariful, Janodia, Manthan Dilipkumar, Jha, Ravi Prakash, Jindal, Har Ashish, Jonas, Jost B, Kabito, Gebisa Guyasa, Kandel, Himal, Kaur, Rimple Jeet, Keshri, Vikash Ranjan, Khader, Yousef Saleh, Khan, Ejaz Ahmad, Khan, Md Jobair, Khan, Moien AB, Khayat Kashani, Hamid Reza, Khubchandani, Jagdish, Kim, Yun Jin, Kisa, Adnan, Klugarová, Jitka, Kolahi, Ali-Asghar, Koohestani, Hamid Reza, Koyanagi, Ai, Kumar, G Anil, Kumar, Narinder, Lallukka, Tea, Lasrado, Savita, Lee, Wei-Chen, Lee, Yo Han, Mahmoodpoor, Ata, Malagón-Rojas, Jeadran N, Malekpour, Mohammad-Reza, Malekzadeh, Reza, Malih, Narges, Mehndiratta, Man Mohan, Mehrabi Nasab, Entezar, Menezes, Ritesh G, Mentis, Alexios-Fotios A, Mesregah, Mohamed Kamal, Miller, Ted R, Mirza-Aghazadeh-Attari, Mohammad, Mobarakabadi, Maryam, Mohammad, Yousef, Mohammadi, Esmaeil, Mohammed, Shafiu, Mokdad, Ali H, Momtazmanesh, Sara, Monasta, Lorenzo, Moni, Mohammad Ali, Mostafavi, Ebrahim, Murray, Christopher J L, Nair, Tapas Sadasivan, Nazari, Javad, Nejadghaderi, Seyed Aria, Neupane, Subas, Neupane Kandel, Sandhya, Nguyen, Cuong Tat, Nowroozi, Ali, Okati-Aliabad, Hassan, Omer, Emad, Oulhaj, Abderrahim, Owolabi, Mayowa O, Panda-Jonas, Songhomitra, Pandey, Anamika, Park, Eun-Kee, Pawar, Shrikant, Pedersini, Paolo, Pereira, Jeevan, Peres, Mario F P, Petcu, Ionela-Roxana, Pourahmadi, Mohammadreza, Radfar, Amir, Rahimi-Dehgolan, Shahram, Rahimi-Movaghar, Vafa, Rahman, Mosiur, Rahmani, Amir Masoud, Rajai, Nazanin, Rao, Chythra R, Rashedi, Vahid, Rashidi, Mohammad-Mahdi, Ratan, Zubair Ahmed, Rawaf, David Laith, Rawaf, Salman, Renzaho, Andre M N, Rezaei, Negar, Rezaei, Zahed, Roever, Leonardo, Ruela, Guilherme de Andrade, Saddik, Basema, Sahebkar, Amirhossein, Salehi, Sana, Sanmarchi, Francesco, Sepanlou, Sadaf G, Shahabi, Saeed, Shahrokhi, Shayan, Shaker, Elaheh, Shamsi, MohammadBagher, Shannawaz, Mohammed, Sharma, Saurab, Shaygan, Maryam, Sheikhi, Rahim Ali, Shetty, Jeevan K, Shiri, Rahman, Shivalli, Siddharudha, Shobeiri, Parnian, Sibhat, Migbar Mekonnen, Singh, Ambrish, Singh, Jasvinder A, Slater, Helen, Solmi, Marco, Somayaji, Ranjani, Tan, Ker-Kan, Thapar, Rekha, Tohidast, Seyed Abolfazl, Valadan Tahbaz, Sahel, Valizadeh, Rohollah, Vasankari, Tommi Juhani, Venketasubramanian, Narayanaswamy, Vlassov, Vasily, Vo, Bay, Wang, Yuan-Pang, Wiangkham, Taweewat, Yadav, Lalit, Yadollahpour, Ali, Yahyazadeh Jabbari, Seyed Hossein, Yang, Lin, Yazdanpanah, Fereshteh, Yonemoto, Naohiro, Younis, Mustafa Z, Zare, Iman, Zarrintan, Armin, Zoladl, Mohammad, Vos, Theo, and March, Lyn M

Published
2023
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34. MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks

Author

Yan, Benjamin B., Wei, Yujia, Jagtap, Jaidip Manikrao M., Moassefi, Mana, Garcia, Diana V. Vera, Singh, Yashbir, Vahdati, Sanaz, Faghani, Shahriar, Erickson, Bradley J., Conte, Gian Marco, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2022
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37. Health system performance in Iran: a systematic analysis for the Global Burden of Disease Study 2019

Author

Farzadfar, Farshad, Naghavi, Mohsen, Sepanlou, Sadaf G, Saeedi Moghaddam, Sahar, Dangel, William James, Davis Weaver, Nicole, Aminorroaya, Arya, Azadnajafabad, Sina, Koolaji, Sogol, Mohammadi, Esmaeil, Rezaei, Negar, Abbas, Jaffar, Abbasi, Behzad, Abbasifard, Mitra, Abbasi-Kangevari, Mohsen, Abbasi-Kangevari, Zeinab, Abbastabar, Hedayat, Abdoli, Amir, Abdollahi, Mohammad, Abdollahzade, Sina, Abolhassani, Hassan, Abrehdari-Tafreshi, Zahra, Aghababaei, Soodabeh, Ahadinezhad, Bahman, Ahmadi, Ali, Ahmadi, Sepideh, Ahmadieh, Hamid, Akbari, Mohammad Esmaeil, Alimohamadi, Yousef, Alipour, Vahid, Alizade, Hesam, Alvand, Saba, Amini, Saeed, Amiri, Sohrab, Anoushirvani, Ali Arash, Ansari, Fereshteh, Arabloo, Jalal, Arab-Zozani, Morteza, Aryan, Zahra, Aryannejad, Armin, Asadi-Aliabadi, Mehran, Asadi-Pooya, Ali A, Asemi, Zatollah, Asgari, Samaneh, Asgary, Saeed, Asghari, Babak, Asghari Jafarabadi, Mohammad, Ashrafi, Elham, Atafar, Zahra, Athari, Seyyed Shamsadin, Avan, Abolfazl, Azadmehr, Abbas, Azami, Hiva, Azangou-Khyavy, Mohammadreza, Azari, Samad, Azari Jafari, Amirhossein, Azarian, Ghasem, Badirzadeh, Alireza, Bahrami, Elham, Bahrami, Mohammad Amin, Barati, Nastaran, Bayati, Mohsen, Bazmandegan, Gholamreza, Behzadifar, Masoud, Bijani, Ali, Bohlouli, Somayeh, Borzouei, Shiva, Daneshpajouhnejad, Parnaz, Dargahi, Abdollah, Daryani, Ahmad, Davoodi Lahijan, Jalal, Didehdar, Mojtaba, Djalalinia, Shirin, Doaei, Saeid, Dorostkar, Fariba, Doshmangir, Leila, Edraki, Mohammadreza, Emami, Amir, Eshrati, Babak, Eskandarieh, Sharareh, Esmaeilzadeh, Firooz, Faghani, Shahriar, Fakhar, Mahdi, Farpour, Hamid Reza, Farrokhpour, Hossein, Fasihi Harandi, Majid, Fereidouni, Mohammad, Foroutan, Masoud, Ghafourifard, Mansour, Ghamari, Azin, Ghamari, Seyyed-Hadi, Ghashghaee, Ahmad, Ghassemi, Fariba, Gholami, Ali, Gholamian, Asadollah, Gholizadeh, Abdolmajid, Goharinezhad, Salime, Goleij, Pouya, Hadei, Mostafa, Hafezi-Nejad, Nima, Hariri, Sanam, Hasanpoor, Edris, Hassanian-Moghaddam, Hossein, Hassanipour, Soheil, Hassankhani, Hadi, Heidari, Mohammad, Heidari-Soureshjani, Reza, Hoseini, Mohammad, Hosseini, Mohammad-Salar, Hosseini, Mostafa, Hosseini, Seyed Kianoosh, Hosseinzadeh, Ali, Hosseinzadeh, Mehdi, Hoveidamanesh, Soodabeh, Iranpour, Pooya, Irvani, Seyed Sina Naghibi, Jaafari, Jalil, Jabbarinejad, Roxana, Jafarinia, Morteza, Jafari-Vayghan, Hamed, Jahani, Mohammad Ali, Jahanmehr, Nader, Jalili, Mahsa, Janghorban, Roksana, Javanmardi, Fatemeh, Joukar, Farahnaz, Kabir, Ali, Kalankesh, Leila R, Kalhor, Rohollah, Kamiab, Zahra, Kamyari, Naser, Karami Matin, Behzad, Karimi, Amirali, Karimi, Salah Eddin, Kazemi Karyani, Ali, Keikavoosi-Arani, Leila, Keramati, Maryam, Keshavarz, Pedram, Keykhaei, Mohammad, Khaleghi, Ali, Khammarnia, Mohammad, Khanali, Javad, Khayamzadeh, Maryam, Khosravi, Sajad, Khosravifar, Mina, Khosravizadeh, Omid, Kianipour, Neda, Kolahi, Ali-Asghar, Maali, Amirhosein, Mahdavi, Mokhtar Mahdavi, Maleki, Afshin, Malekpour, Mohammad-Reza, Mansori, Kamyar, Mansouri, Borhan, Mansournia, Mohammad Ali, Maracy, Mohammad Reza, Marjani, Abdoljalal, Masoudi, Sahar, Masoumi, Seyedeh Zahra, Masoumi-Asl, Hossein, Mayeli, Mahsa, Mehrabi Nasab, Entezar, Mehri, Fereshteh, Miri, Mohammad, Mirmoeeni, Seyyedmohammadsadeq, Mirzaei, Hamed, Mirzaei, Maryam, Mirzaei, Roya, Mohamadkhani, Ashraf, Mohammadi, Heidar, Mohammadi, Seyyede Momeneh, Mohammadi, Shadieh, Mohammadian-Hafshejani, Abdollah, Mohammadifard, Noushin, Mohammadpourhodki, Reza, Mohseni, Mohammad, Mokari, Amin, Momtazmanesh, Sara, Moradi, Abdolvahab, Moradi, Masoud, Moradi, Yousef, Moradi-Joo, Mohammad, Moradpour, Farhad, Moradzadeh, Maliheh, Moradzadeh, Rahmatollah, Mosapour, Abbas, Moslehi, Shandiz, Mouodi, Simin, Naderi, Mehdi, Naderifar, Homa, Najafpour, Zhila, Nazari, Javad, Nejadghaderi, Seyed Aria, Nemati-Anaraki, Leila, Nikpoor, Amin Reza, Nojomi, Marzieh, Noori, Maryam, Nouraei, Hasti, Nowroozi, Ali, Oladnabi, Morteza, Pashazadeh Kan, Fatemeh, Pirestani, Majid, Pirsaheb, Meghdad, Pourahmadi, Mohammadreza, Pourchamani, Hadis, Pourjafar, Hadi, Pourshams, Akram, Rabiee, Mohammad, Rabiee, Navid, Rafiei, Alireza, Rafiei, Sima, Rahim, Fakher, Rahmani, Amir Masoud, Rashedi, Sina, Rashedi, Vahid, Rashidi, Amirfarzan, Rashidi, Mahsa, Rashidi, Mohammad-Mahdi, Ravangard, Ramin, Rawassizadeh, Reza, Razeghian-Jahromi, Iman, Razeghinia, Mohammad Sadegh, Redford, Sofia B, Rezaei, Maryam, Rezaei, Nazila, Rezaei, Nima, Rezaei, Saeid, Rezaei Aliabadi, Hossein, Rezaeian, Mohsen, Rezai, Mohammad Sadegh, Rezapour, Aziz, Rezazadeh, Hossein, Rezazadeh-Khadem, Sahba, Rostamian, Morteza, Sadeghi, Ehsan, Sadeghi, Erfan, Sadeghi, Masoumeh, Sadeghian, Reihaneh, Sadeghian, Saeid, Safarpour, Hamid, Safdarian, Mahdi, Safi, Sare, Sahebazzamani, Maryam, Sahebkar, Amirhossein, Sahraian, Mohammad Ali, Salahi, Sarvenaz, Salamati, Payman, Samadi Kafil, Hossein, Sarikhani, Yaser, Sarkhosh, Maryam, Sarveazad, Arash, Seyed-Nezhad, Maryam, Shafaat, Omid, Shaghaghi, Zahra, Shahabi, Saeed, Shahin, Sarvenaz, Shaker, Elaheh, Shakiba, Saeed, Shamsi, MohammadBagher, Shamsoddin, Erfan, Sharafi, Kiomars, Sharifian, Sakineh, Shaygan, Maryam, Sheikhtaheri, Abbas, Shiani, Amir, Shirbandi, Kiarash, Shirkoohi, Reza, Shobeiri, Parnian, Shokri, Azad, Siabani, Soraya, Sima, Ali Reza, Sofi-Mahmudi, Ahmad, Soheili, Amin, Soltani, Shahin, Soltani-Zangbar, Mohammad Sadegh, Soofi, Moslem, Tabaeian, Seidamir Pasha, Tabary, Mohammadreza, Tahamtan, Alireza, Taheri, Majid, Taherkhani, Amir, Tajdini, Masih, Tavolinejad, Hamed, Tehrani-Banihashemi, Arash, Tiyuri, Amir, Tohidast, Seyed Abolfazl, Vakilian, Alireza, Valadan Tahbaz, Sahel, Vo, Bay, Yahyazadeh Jabbari, Seyed Hossein, Yazdi-Feyzabadi, Vahid, Yousefi, Zabihollah, Yousefinezhadi, Taraneh, Zahir, Mazyar, Zahirian Moghadam, Telma, Zamanian, Maryam, Zandian, Hamed, Zangeneh, Alireza, Zarafshan, Hadi, Zare, Fariba, Zare Dehnavi, Ali, Zarea, Kourosh, Zarei, Ahmad, Zareshahrabadi, Zahra, Ziapour, Arash, Zoghi, Sina, Sarrafzadegan, Nizal, Rahimi-Movaghar, Vafa, Jamshidi, Hamid Reza, Mokdad, Ali H, Hay, Simon I, Murray, Christopher J L, Khosravi, Ardeshir, Moradi-Lakeh, Maziar, Asadi-Lari, Mohsen, Malekzadeh, Reza, and Larijani, Bagher

Published
2022
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41. Toward Clinically Trustworthy Deep Learning: Applying Conformal Prediction to Intracranial Hemorrhage Detection

Author

Gamble, Cooper, Faghani, Shahriar, Erickson, Bradley J., Gamble, Cooper, Faghani, Shahriar, and Erickson, Bradley J.

Abstract

As deep learning (DL) continues to demonstrate its ability in radiological tasks, it is critical that we optimize clinical DL solutions to include safety. One of the principal concerns in the clinical adoption of DL tools is trust. This study aims to apply conformal prediction as a step toward trustworthiness for DL in radiology. This is a retrospective study of 491 non-contrast head CTs from the CQ500 dataset, in which three senior radiologists annotated slices containing intracranial hemorrhage (ICH). The dataset was split into definite and challenging subsets, where challenging images were defined to those in which there was disagreement among readers. A DL model was trained on 146 patients (10,815 slices) from the definite data (training dataset) to perform ICH localization and classification for five classes of ICH. To develop an uncertainty-aware DL model, 1,546 cases of the definite data (calibration dataset) was used for Mondrian conformal prediction (MCP). The uncertainty-aware DL model was tested on 8,401 definite and challenging cases to assess its ability to identify challenging cases. After the MCP procedure, the model achieved an F1 score of 0.920 for ICH classification on the test dataset. Additionally, it correctly identified 6,837 of the 6,856 total challenging cases as challenging (99.7% accuracy). It did not incorrectly label any definite cases as challenging. The uncertainty-aware ICH detector performs on par with state-of-the-art models. MCP's performance in detecting challenging cases demonstrates that it is useful in automated ICH detection and promising for trustworthiness in radiological DL., Comment: 14 pages, 6 figures, 4 tables

Published
2024

48. NIMG-57. PERFORMANCE OF DEEP LEARNING IN MGMT PROMOTER METHYLATION STATUS PREDICTION USING BRAIN MRI: RESULTS FROM A LARGE COHORT OF IDH-WILDTYPE GLIOMAS TESTED BY A SINGLE METHYLATION ASSAY

Author

Conte, Gian Marco, primary, Moassefi, Mana, additional, Faghani, Shahriar, additional, Sanvito, Francesco, additional, Decker, Paul, additional, Kizilbash, Sani H, additional, Eckel-Passow, Jeanette, additional, Ida, Cristine, additional, and Erickson, Bradley, additional

Published
2023
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