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1. 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

2. QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

Author

Li, Hongwei Bran, Navarro, Fernando, Ezhov, Ivan, Bayat, Amirhossein, Das, Dhritiman, Kofler, Florian, Shit, Suprosanna, Waldmannstetter, Diana, Paetzold, Johannes C., Hu, Xiaobin, Wiestler, Benedikt, Zimmer, Lucas, Amiranashvili, Tamaz, Prabhakar, Chinmay, Berger, Christoph, Weidner, Jonas, Alonso-Basant, Michelle, Rashid, Arif, Baid, Ujjwal, Adel, Wesam, Ali, Deniz, Baheti, Bhakti, Bai, Yingbin, Bhatt, Ishaan, Cetindag, Sabri Can, Chen, Wenting, Cheng, Li, Dutand, Prasad, Dular, Lara, Elattar, Mustafa A., Feng, Ming, Gao, Shengbo, Huisman, Henkjan, Hu, Weifeng, Innani, Shubham, Jiat, Wei, Karimi, Davood, Kuijf, Hugo J., Kwak, Jin Tae, Le, Hoang Long, Lia, Xiang, Lin, Huiyan, Liu, Tongliang, Ma, Jun, Ma, Kai, Ma, Ting, Oksuz, Ilkay, Holland, Robbie, Oliveira, Arlindo L., Pal, Jimut Bahan, Pei, Xuan, Qiao, Maoying, Saha, Anindo, Selvan, Raghavendra, Shen, Linlin, Silva, Joao Lourenco, Spiclin, Ziga, Talbar, Sanjay, Wang, Dadong, Wang, Wei, Wang, Xiong, Wang, Yin, Xia, Ruiling, Xu, Kele, Yan, Yanwu, Yergin, Mert, Yu, Shuang, Zeng, Lingxi, Zhang, YingLin, Zhao, Jiachen, Zheng, Yefeng, Zukovec, Martin, Do, Richard, Becker, Anton, Simpson, Amber, Konukoglu, Ender, Jakab, Andras, Bakas, Spyridon, Joskowicz, Leo, and Menze, Bjoern

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

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT; various organs such as the brain, prostate, kidney, and pancreas; and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks., Comment: initial technical report

Published
2024

3. 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

5. The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting

Author

Kofler, Florian, Meissen, Felix, Steinbauer, Felix, Graf, Robert, Ehrlich, Stefan K, Reinke, Annika, Oswald, Eva, Waldmannstetter, Diana, Hoelzl, Florian, Horvath, Izabela, Turgut, Oezguen, Shit, Suprosanna, Bukas, Christina, Yang, Kaiyuan, Paetzold, Johannes C., de da Rosa, Ezequiel, Mekki, Isra, Vinayahalingam, Shankeeth, Kassem, Hasan, Zhang, Juexin, Chen, Ke, Weng, Ying, Durrer, Alicia, Cattin, Philippe C., Wolleb, Julia, Sadique, M. S., Rahman, M. M., Farzana, W., Temtam, A., Iftekharuddin, K. M., Adewole, Maruf, Anwar, Syed Muhammad, Baid, Ujjwal, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Li, Hongwei Bran, Moawad, Ahmed W, Conte, Gian-Marco, Farahani, Keyvan, Eddy, James, Sheller, Micah, Pati, Sarthak, Karagyris, Alexandros, Aristizabal, Alejandro, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Johanson, Elaine, Meier, Zeke, Familiar, Ariana, Davatzikos, Christos, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan M, Wiest, Roland, Kirschke, Jan, Colen, Rivka R, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-André, Mahajan, Abhishek, Mohan, Suyash, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva-Meyer, Javier, Colak, Errol, Crivellaro, Priscila, Jakab, Andras, Fatade, Abiodun, Omidiji, Olubukola, Lagos, Rachel Akinola, Olatunji, O O, Khanna, Goldey, Kirkpatrick, John, Alonso-Basanta, Michelle, Rashid, Arif, Bornhorst, Miriam, Nabavizadeh, Ali, Lepore, Natasha, Palmer, Joshua, Porras, Antonio, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Calabrese, Evan, Rudie, Jeffrey David, Linguraru, Marius George, Iglesias, Juan Eugenio, Van Leemput, Koen, Bakas, Spyridon, Wiestler, Benedikt, Ezhov, Ivan, Piraud, Marie, and Menze, Bjoern H

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

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with an already pathological scan. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantee for images featuring lesions. Examples include, but are not limited to, algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS inpainting challenge. Here, the participants explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later, it will be updated to summarize the findings of the challenge. The challenge is organized as part of the ASNR-BraTS MICCAI challenge., Comment: 14 pages, 6 figures

Published
2023

6. The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma

Author

LaBella, Dominic, Adewole, Maruf, Alonso-Basanta, Michelle, Altes, Talissa, Anwar, Syed Muhammad, Baid, Ujjwal, Bergquist, Timothy, Bhalerao, Radhika, Chen, Sully, Chung, Verena, Conte, Gian-Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Godfrey, Devon, Hilal, Fathi, Familiar, Ariana, Farahani, Keyvan, Iglesias, Juan Eugenio, Jiang, Zhifan, Johanson, Elaine, Kazerooni, Anahita Fathi, Kent, Collin, Kirkpatrick, John, Kofler, Florian, Van Leemput, Koen, Li, Hongwei Bran, Liu, Xinyang, Mahtabfar, Aria, McBurney-Lin, Shan, McLean, Ryan, Meier, Zeke, Moawad, Ahmed W, Mongan, John, Nedelec, Pierre, Pajot, Maxence, Piraud, Marie, Rashid, Arif, Reitman, Zachary, Shinohara, Russell Takeshi, Velichko, Yury, Wang, Chunhao, Warman, Pranav, Wiggins, Walter, Aboian, Mariam, Albrecht, Jake, Anazodo, Udunna, Bakas, Spyridon, Flanders, Adam, Janas, Anastasia, Khanna, Goldey, Linguraru, Marius George, Menze, Bjoern, Nada, Ayman, Rauschecker, Andreas M, Rudie, Jeff, Tahon, Nourel Hoda, Villanueva-Meyer, Javier, Wiestler, Benedikt, and Calabrese, Evan

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

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.

Published
2023

8. Contributors

Author

Ahmed, Sara, primary, Au Yong, Tracy P.T., additional, Bakst, Richard, additional, Barnett, Gill, additional, Bond, Sarah, additional, Boon, Ian S., additional, Boon, Cheng S., additional, Buckstein, Michael, additional, Chen, Liyuan, additional, Chowdhry, Amit Kumar, additional, Cui, Sunan, additional, Dekker, Andre, additional, Dohopolski, Michael, additional, El-Charif, Omar, additional, El Naqa, Issam, additional, Fijten, Rianne, additional, Fuller, Clifton D., additional, Green, Andrew, additional, Jain, Anshu, additional, Kalendralis, Petros, additional, Kalet, Alan M., additional, Kang, John, additional, Kann, Benjamin H., additional, Kerns, Sarah, additional, Kim, Ellen, additional, Kiser, Kendall J., additional, Levitin, Ronald, additional, Liu, Jinyuan, additional, Lyon, Robert J., additional, McCoy, Lance A., additional, McDonald, Brigid A., additional, McWilliam, Alan, additional, Milano, Michael, additional, Mulder, Samuel, additional, Nayak, Stuti, additional, Nobel, Martijn, additional, Placide, John, additional, Porter, Evan, additional, Preston, Kathryn, additional, Puts, Sander, additional, Pybus, Matt, additional, Rashid, Arif S., additional, Rattay, Tim, additional, Rosenstein, Barry S., additional, Sanders, Keith L., additional, Sanjay, Aneja, additional, Schwartz, Russell, additional, Sharafi, Christina Setareh, additional, Sher, David, additional, Shumway, John, additional, Siddiqui, Zaid, additional, Speers, Corey, additional, Strawderman, Robert, additional, Tao, Yifeng, additional, Thomas, Charles R., additional, Thompson, Reid F., additional, Tu, Xin Ming, additional, Tu, Justin Xiang-Yuan, additional, Vallières, Martin, additional, van Dijk, Lisanne V., additional, Ventura, Juan, additional, Wahid, Kareem A., additional, Wald, Hedy S., additional, Wang, Jing, additional, Wasserman, Jason Adam, additional, West, Catharine, additional, Yang, Kun, additional, Yap, Moi Hoon, additional, Yuan, Yading, additional, Yuan, Ye, additional, Zegers, Catharina, additional, Zhu, Lin L., additional, and Zuhour, Raed, additional

Published
2024
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12. Exploring the interface dynamics of emulsion gel via a novel non-destructive technique provides insights into the underlying mechanism of gel formation, water mobility, functionality and structural variations

Author

Qayum, Abdul, Rashid, Arif, Khan, Asif Ali, Zhong, Mingming, Liu, Yuxuan, Hussain, Muhammad, Virk, Muhammad Safiullah, Ahmed, Zahoor, Rehman, Abdur, Hussain, Abid, Liang, Qiufang, Ren, Xiaofeng, Ma, Haile, El-Salam, Ekram Abd, and Miao, Song

Published
2024
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23. Layer-by-layer concurrent encapsulation of probiotics and bioactive compounds with supplementation in intermediary layers: An establishing instrument for microbiome recharge, core safety, and targeted delivery

Author

Virk, Muhammad Safiullah, Virk, Muhammad Abdulrehman, Gul, Mehak, Awais, Muhammad, Liang, Qiufang, Tufail, Tabussam, Zhong, Mingming, Sun, Yufan, Qayum, Abdul, El-Salam, Ekram Abd, Ekumah, John-Nelson, Rehman, Abdur, Rashid, Arif, and Ren, Xiaofeng

Published
2025
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31. QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

Author

Bran, Hongwei, Navarro, Fernando, Ezhov, Ivan, Bayat, Amirhossein, Das, Dhritiman, Kofler, Florian, Shit, Suprosanna, Waldmannstetter, Diana, Paetzold, Johannes C., Hu, Xiaobin, Wiestler, Benedikt, Zimmer, Lucas, Amiranashvili, Tamaz, Prabhakar, Chinmay, Berger, Christoph, Weidner, Jonas, Alonso-Basant, Michelle, Rashid, Arif, Baid, Ujjwal, Adel, Wesam, Ali, Deniz, Baheti, Bhakti, Bai, Yingbin, Bhatt, Ishaan, Cetindag, Sabri Can, Chen, Wenting, Cheng, Li, Dutand, Prasad, Dular, Lara, Elattar, Mustafa A., Feng, Ming, Gao, Shengbo, Huisman, Henkjan, Hu, Weifeng, Innani, Shubham, Jiat, Wei, Karimi, Davood, Kuijf, Hugo J., Kwak, Jin Tae, Le, Hoang Long, Lia, Xiang, Lin, Huiyan, Liu, Tongliang, Ma, Jun, Ma, Kai, Ma, Ting, Oksuz, Ilkay, Holland, Robbie, Oliveira, Arlindo L., Pal, Jimut Bahan, Pei, Xuan, Qiao, Maoying, Saha, Anindo, Selvan, Raghavendra, Shen, Linlin, Silva, Joao Lourenco, Spiclin, Ziga, Talbar, Sanjay, Wang, Dadong, Wang, Wei, Wang, Xiong, Wang, Yin, Xia, Ruiling, Xu, Kele, Yan, Yanwu, Yergin, Mert, Yu, Shuang, Zeng, Lingxi, Zhang, YingLin, Zhao, Jiachen, Zheng, Yefeng, Zukovec, Martin, Do, Richard, Becker, Anton, Simpson, Amber, Konukoglu, Ender, Jakab, Andras, Bakas, Spyridon, Joskowicz, Leo, Menze, Bjoern, Bran, Hongwei, Navarro, Fernando, Ezhov, Ivan, Bayat, Amirhossein, Das, Dhritiman, Kofler, Florian, Shit, Suprosanna, Waldmannstetter, Diana, Paetzold, Johannes C., Hu, Xiaobin, Wiestler, Benedikt, Zimmer, Lucas, Amiranashvili, Tamaz, Prabhakar, Chinmay, Berger, Christoph, Weidner, Jonas, Alonso-Basant, Michelle, Rashid, Arif, Baid, Ujjwal, Adel, Wesam, Ali, Deniz, Baheti, Bhakti, Bai, Yingbin, Bhatt, Ishaan, Cetindag, Sabri Can, Chen, Wenting, Cheng, Li, Dutand, Prasad, Dular, Lara, Elattar, Mustafa A., Feng, Ming, Gao, Shengbo, Huisman, Henkjan, Hu, Weifeng, Innani, Shubham, Jiat, Wei, Karimi, Davood, Kuijf, Hugo J., Kwak, Jin Tae, Le, Hoang Long, Lia, Xiang, Lin, Huiyan, Liu, Tongliang, Ma, Jun, Ma, Kai, Ma, Ting, Oksuz, Ilkay, Holland, Robbie, Oliveira, Arlindo L., Pal, Jimut Bahan, Pei, Xuan, Qiao, Maoying, Saha, Anindo, Selvan, Raghavendra, Shen, Linlin, Silva, Joao Lourenco, Spiclin, Ziga, Talbar, Sanjay, Wang, Dadong, Wang, Wei, Wang, Xiong, Wang, Yin, Xia, Ruiling, Xu, Kele, Yan, Yanwu, Yergin, Mert, Yu, Shuang, Zeng, Lingxi, Zhang, YingLin, Zhao, Jiachen, Zheng, Yefeng, Zukovec, Martin, Do, Richard, Becker, Anton, Simpson, Amber, Konukoglu, Ender, Jakab, Andras, Bakas, Spyridon, Joskowicz, Leo, and Menze, Bjoern

Abstract

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT; various organs such as the brain, prostate, kidney, and pancreas; and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks., Comment: initial technical report

Published
2024

32. A flavonoid metabolon: cytochrome b5 enhances B‐ring trihydroxylated flavan‐3‐ols synthesis in tea plants.

Author

Ruan, Haixiang, Gao, Liping, Fang, Zhou, Lei, Ting, Xing, Dawei, Ding, Yan, Rashid, Arif, Zhuang, Juhua, Zhang, Qiang, Gu, Chunyang, Qian, Wei, Zhang, Niuniu, Qian, Tao, Li, Kongqing, Xia, Tao, and Wang, Yunsheng

Subjects
FLAVONOIDS, EPIGALLOCATECHIN gallate, TEA, PROTEIN-protein interactions, ENDOPLASMIC reticulum, PHENOLS
Abstract

SUMMARY: Flavan‐3‐ols are prominent phenolic compounds found abundantly in the young leaves of tea plants. The enzymes involved in flavan‐3‐ol biosynthesis in tea plants have been extensively investigated. However, the localization and associations of these numerous functional enzymes within cells have been largely neglected. In this study, we aimed to investigate the synthesis of flavan‐3‐ols in tea plants, particularly focusing on epigallocatechin gallate. Our analysis involving the DESI‐MSI method to reveal a distinct distribution pattern of B‐ring trihydroxylated flavonoids, primarily concentrated in the outer layer of buds. Subcellular localization showed that CsC4H, CsF3′H, and CsF3′5′H localizes endoplasmic reticulum. Protein–protein interaction studies demonstrated direct associations between CsC4H, CsF3′H, and cytoplasmic enzymes (CHS, CHI, F3H, DFR, FLS, and ANR), highlighting their interactions within the biosynthetic pathway. Notably, CsF3′5′H, the enzyme for B‐ring trihydroxylation, did not directly interact with other enzymes. We identified cytochrome b5 isoform C serving as an essential redox partner, ensuring the proper functioning of CsF3′5′H. Our findings suggest the existence of distinct modules governing the synthesis of different B‐ring hydroxylation compounds. This study provides valuable insights into the mechanisms underlying flavonoid diversity and efficient synthesis and enhances our understanding of the substantial accumulation of B‐ring trihydroxylated flavan‐3‐ols in tea plants. Significance Statement: Our work reveals the mechanism of flavonoid diversity and efficient synthesis in tea plants from the perspective of metabolon. Different metabolons modules provide a new perspective on the scientific problem of why tea plants accumulate high levels of EGCG. The discovery of obligate electron shuttle proteins provides new directions for improving metabolic engineering. [ABSTRACT FROM AUTHOR]

Published
2024
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34. The Anti-Inflammatory and Curative Exponent of Probiotics: A Comprehensive and Authentic Ingredient for the Sustained Functioning of Major Human Organs

Author

Virk, Muhammad Safiullah, primary, Virk, Muhammad Abdulrehman, additional, He, Yufeng, additional, Tufail, Tabussam, additional, Gul, Mehak, additional, Qayum, Abdul, additional, Rehman, Abdur, additional, Rashid, Arif, additional, Ekumah, John-Nelson, additional, Han, Xu, additional, Wang, Junxia, additional, and Ren, Xiaofeng, additional

Published
2024
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39. Production of Kudzu Starch Gels with Superior Mechanical and Rheological Properties through Submerged Ethanol Exposure and Implications for In Vitro Digestion

Author

Ekumah, John-Nelson, primary, Han, Xu, additional, Liang, Qiufang, additional, Kang, Lixin, additional, Wei, Benxi, additional, Rashid, Arif, additional, Virk, Muhammad Safiullah, additional, Qayum, Abdul, additional, Adade, Selorm Yao-Say Solomon, additional, Johnson, Nana Adwoa Nkuma, additional, and Ren, Xiaofeng, additional

Published
2023
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49. Characterization of Nano-SiO 2 /Zein Film Prepared Using Ultrasonic Treatment and the Ability of the Prepared Film to Resist Different Storage Environments.

Author

Ren, Xiaofeng, Wang, Junxia, Rashid, Arif, Hou, Ting, Ma, Haile, and Liang, Qiufang

Subjects
ATTENUATED total reflectance, ULTRASONICS, DIFFERENTIAL scanning calorimetry, FOOD packaging, PACKAGING film, HYDROGEN bonding
Abstract

This study has developed, ultrasound-assisted, a novel food packaging film (U-zein/SiO2) for food packaging applications. Incorporating an optimal concentration of 18 mg/mL of nano-SiO2 and subjecting the film to 10 min of ultrasonic treatment resulted in a remarkable increase of 32.89% in elongation at break and 55.86% in tensile strength. In addition, the incorporation of nano-SiO2 effectively reduces the water content and solubility of the composite film, resulting in improved water/oxygen barrier properties. These physiochemical properties were further improved with the application of ultrasound. The analysis of attenuated total reflectance-Fourier transform infrared, X-ray diffraction, differential scanning calorimetry, and scanning electronic microscope demonstrated that the ultrasound treatment improved the hydrogen bonds, improved thermal stability, molecular arrangement, structure stability, and intermolecular compatibility of the composite film, resulting in enhanced physio-mechanical properties of the film. In addition, the ultrasound treatment led to a smoother film surface and reduced the pores on the film's cross-section. Moreover, the U-zein/SiO2 film exhibited excellent mechanical and water/oxygen barrier properties in different storage environments over a period of 30 days. These results offer sound theoretical support for the practical application of the prepared preservative film. [ABSTRACT FROM AUTHOR]

Published
2023
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