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48 results on '"Milchenko, Mikhail"'

1. The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Author

Li, Hongwei Bran, Conte, Gian Marco, Anwar, Syed Muhammad, Kofler, Florian, Ezhov, Ivan, van Leemput, Koen, Piraud, Marie, Diaz, Maria, Cole, Byrone, Calabrese, Evan, Rudie, Jeff, Meissen, Felix, Adewole, Maruf, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Moawad, Ahmed W., Farahani, Keyvan, Eddy, James, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Familiar, Ariana, Johanson, Elaine, Meier, Zeke, 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, Javier, Colak, Meyer Errol, Crivellaro, Priscila, Jakab, Andras, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Yu, Thomas, Baid, Ujjwal, Bakas, Spyridon, Linguraru, Marius George, Menze, Bjoern, Iglesias, Juan Eugenio, and Wiestler, Benedikt

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

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions., Comment: Technical report of BraSyn

Published
2023

2. The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting

Author

Kofler, Florian, Meissen, Felix, Steinbauer, Felix, Graf, Robert, Oswald, Eva, de da Rosa, Ezequiel, Li, Hongwei Bran, Baid, Ujjwal, Hoelzl, Florian, Turgut, Oezguen, Horvath, Izabela, Waldmannstetter, Diana, Bukas, Christina, Adewole, Maruf, Anwar, Syed Muhammad, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Moawad, Ahmed W, Farahani, Keyvan, Eddy, James, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Familiar, Ariana, Conte, Gian-Marco, Johanson, Elaine, Meier, Zeke, 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, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Iglesias, Juan Eugenio, Van Leemput, Koen, Bakas, Spyridon, Rueckert, Daniel, Wiestler, Benedikt, Ezhov, Ivan, Piraud, Marie, and Menze, Bjoern

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 a scan that is already pathological. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantees 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 2023 inpainting challenge. Here, the participants' task is to 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 BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada., Comment: 5 pages, 1 figure

Published
2023

3. The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Author

Baid, Ujjwal, Ghodasara, Satyam, Mohan, Suyash, Bilello, Michel, Calabrese, Evan, Colak, Errol, Farahani, Keyvan, Kalpathy-Cramer, Jayashree, Kitamura, Felipe C., Pati, Sarthak, Prevedello, Luciano M., Rudie, Jeffrey D., Sako, Chiharu, Shinohara, Russell T., Bergquist, Timothy, Chai, Rong, Eddy, James, Elliott, Julia, Reade, Walter, Schaffter, Thomas, Yu, Thomas, Zheng, Jiaxin, Moawad, Ahmed W., Coelho, Luiz Otavio, McDonnell, Olivia, Miller, Elka, Moron, Fanny E., Oswood, Mark C., Shih, Robert Y., Siakallis, Loizos, Bronstein, Yulia, Mason, James R., Miller, Anthony F., Choudhary, Gagandeep, Agarwal, Aanchal, Besada, Cristina H., Derakhshan, Jamal J., Diogo, Mariana C., Do-Dai, Daniel D., Farage, Luciano, Go, John L., Hadi, Mohiuddin, Hill, Virginia B., Iv, Michael, Joyner, David, Lincoln, Christie, Lotan, Eyal, Miyakoshi, Asako, Sanchez-Montano, Mariana, Nath, Jaya, Nguyen, Xuan V., Nicolas-Jilwan, Manal, Jimenez, Johanna Ortiz, Ozturk, Kerem, Petrovic, Bojan D., Shah, Chintan, Shah, Lubdha M., Sharma, Manas, Simsek, Onur, Singh, Achint K., Soman, Salil, Statsevych, Volodymyr, Weinberg, Brent D., Young, Robert J., Ikuta, Ichiro, Agarwal, Amit K., Cambron, Sword C., Silbergleit, Richard, Dusoi, Alexandru, Postma, Alida A., Letourneau-Guillon, Laurent, Perez-Carrillo, Gloria J. Guzman, Saha, Atin, Soni, Neetu, Zaharchuk, Greg, Zohrabian, Vahe M., Chen, Yingming, Cekic, Milos M., Rahman, Akm, Small, Juan E., Sethi, Varun, Davatzikos, Christos, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva-Meyer, Javier, Freymann, John B., Kirby, Justin S., Wiestler, Benedikt, Crivellaro, Priscila, Colen, Rivka R., Kotrotsou, Aikaterini, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Fathallah-Shaykh, Hassan, Wiest, Roland, Jakab, Andras, Weber, Marc-Andre, Mahajan, Abhishek, Menze, Bjoern, Flanders, Adam E., and Bakas, Spyridon

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society. Since its inception, BraTS has been focusing on being a common benchmarking venue for brain glioma segmentation algorithms, with well-curated multi-institutional multi-parametric magnetic resonance imaging (mpMRI) data. Gliomas are the most common primary malignancies of the central nervous system, with varying degrees of aggressiveness and prognosis. The RSNA-ASNR-MICCAI BraTS 2021 challenge targets the evaluation of computational algorithms assessing the same tumor compartmentalization, as well as the underlying tumor's molecular characterization, in pre-operative baseline mpMRI data from 2,040 patients. Specifically, the two tasks that BraTS 2021 focuses on are: a) the segmentation of the histologically distinct brain tumor sub-regions, and b) the classification of the tumor's O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. The performance evaluation of all participating algorithms in BraTS 2021 will be conducted through the Sage Bionetworks Synapse platform (Task 1) and Kaggle (Task 2), concluding in distributing to the top ranked participants monetary awards of $60,000 collectively., Comment: 19 pages, 2 figures, 1 table

Published
2021

4. The Federated Tumor Segmentation (FeTS) Challenge

Author

Pati, Sarthak, Baid, Ujjwal, Zenk, Maximilian, Edwards, Brandon, Sheller, Micah, Reina, G. Anthony, Foley, Patrick, Gruzdev, Alexey, Martin, Jason, Albarqouni, Shadi, Chen, Yong, Shinohara, Russell Taki, Reinke, Annika, Zimmerer, David, Freymann, John B., Kirby, Justin S., Davatzikos, Christos, Colen, Rivka R., Kotrotsou, Aikaterini, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Fathallah-Shaykh, Hassan, Wiest, Roland, Jakab, Andras, Weber, Marc-Andre, Mahajan, Abhishek, Maier-Hein, Lena, Kleesiek, Jens, Menze, Bjoern, Maier-Hein, Klaus, and Bakas, Spyridon

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

This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standard for validation of biomedical image analysis methods. However, the actual performance of participating (even the winning) algorithms on "real-world" clinical data often remains unclear, as the data included in challenges are usually acquired in very controlled settings at few institutions. The seemingly obvious solution of just collecting increasingly more data from more institutions in such challenges does not scale well due to privacy and ownership hurdles. Towards alleviating these concerns, we are proposing the FeTS challenge 2021 to cater towards both the development and the evaluation of models for the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Specifically, the FeTS 2021 challenge uses clinically acquired, multi-institutional magnetic resonance imaging (MRI) scans from the BraTS 2020 challenge, as well as from various remote independent institutions included in the collaborative network of a real-world federation (https://www.fets.ai/). The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i.e. on data from institutional distributions that were not part of the training datasets.

Published
2021

6. Mapping language function with task-based vs. resting-state functional MRI

Author

Park, Ki Yun, Lee, John J, Dierker, Donna, Marple, Laura M, Hacker, Carl D, Roland, Jarod L, Marcus, Daniel S, Milchenko, Mikhail, Miller-Thomas, Michelle M, Benzinger, Tammie L, Shimony, Joshua S, Snyder, Abraham Z, and Leuthardt, Eric C

Subjects
Biological Psychology, Psychology, Brain Disorders, Behavioral and Social Science, Bioengineering, Basic Behavioral and Social Science, Biomedical Imaging, Neurosciences, Clinical Research, Neurological, Adult, Aged, Attention, Brain, Brain Mapping, Broca Area, Female, Frontal Lobe, Functional Laterality, Glioblastoma, Humans, Language, Magnetic Resonance Imaging, Male, Middle Aged, Rest, Temporal Lobe, Young Adult, General Science & Technology
Abstract

BackgroundUse of functional MRI (fMRI) in pre-surgical planning is a non-invasive method for pre-operative functional mapping for patients with brain tumors, especially tumors located near eloquent cortex. Currently, this practice predominantly involves task-based fMRI (T-fMRI). Resting state fMRI (RS-fMRI) offers an alternative with several methodological advantages. Here, we compare group-level analyses of RS-fMRI vs. T-fMRI as methods for language localization.PurposeTo contrast RS-fMRI vs. T-fMRI as techniques for localization of language function.MethodsWe analyzed data obtained in 35 patients who had both T-fMRI and RS-fMRI scans during the course of pre-surgical evaluation. The RS-fMRI data were analyzed using a previously trained resting-state network classifier. The T-fMRI data were analyzed using conventional techniques. Group-level results obtained by both methods were evaluated in terms of two outcome measures: (1) inter-subject variability of response magnitude and (2) sensitivity/specificity analysis of response topography, taking as ground truth previously reported maps of the language system based on intraoperative cortical mapping as well as meta-analytic maps of language task fMRI responses.ResultsBoth fMRI methods localized major components of the language system (areas of Broca and Wernicke) although not with equal inter-subject consistency. Word-stem completion T-fMRI strongly activated Broca's area but also several task-general areas not specific to language. RS-fMRI provided a more specific representation of the language system.ConclusionWe demonstrate several advantages of classifier-based mapping of language representation in the brain. Language T-fMRI activated task-general (i.e., not language-specific) functional systems in addition to areas of Broca and Wernicke. In contrast, classifier-based analysis of RS-fMRI data generated maps confined to language-specific regions of the brain.

Published
2020

7. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Author

Bakas, Spyridon, Reyes, Mauricio, Jakab, Andras, Bauer, Stefan, Rempfler, Markus, Crimi, Alessandro, Shinohara, Russell Takeshi, Berger, Christoph, Ha, Sung Min, Rozycki, Martin, Prastawa, Marcel, Alberts, Esther, Lipkova, Jana, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan, Wiest, Roland, Kirschke, Jan, Wiestler, Benedikt, Colen, Rivka, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-Andre, Mahajan, Abhishek, Baid, Ujjwal, Gerstner, Elizabeth, Kwon, Dongjin, Acharya, Gagan, Agarwal, Manu, Alam, Mahbubul, Albiol, Alberto, Albiol, Antonio, Albiol, Francisco J., Alex, Varghese, Allinson, Nigel, Amorim, Pedro H. A., Amrutkar, Abhijit, Anand, Ganesh, Andermatt, Simon, Arbel, Tal, Arbelaez, Pablo, Avery, Aaron, Azmat, Muneeza, B., Pranjal, Bai, W, Banerjee, Subhashis, Barth, Bill, Batchelder, Thomas, Batmanghelich, Kayhan, Battistella, Enzo, Beers, Andrew, Belyaev, Mikhail, Bendszus, Martin, Benson, Eze, Bernal, Jose, Bharath, Halandur Nagaraja, Biros, George, Bisdas, Sotirios, Brown, James, Cabezas, Mariano, Cao, Shilei, Cardoso, Jorge M., Carver, Eric N, Casamitjana, Adrià, Castillo, Laura Silvana, Catà, Marcel, Cattin, Philippe, Cerigues, Albert, Chagas, Vinicius S., Chandra, Siddhartha, Chang, Yi-Ju, Chang, Shiyu, Chang, Ken, Chazalon, Joseph, Chen, Shengcong, Chen, Wei, Chen, Jefferson W, Chen, Zhaolin, Cheng, Kun, Choudhury, Ahana Roy, Chylla, Roger, Clérigues, Albert, Colleman, Steven, Colmeiro, Ramiro German Rodriguez, Combalia, Marc, Costa, Anthony, Cui, Xiaomeng, Dai, Zhenzhen, Dai, Lutao, Daza, Laura Alexandra, Deutsch, Eric, Ding, Changxing, Dong, Chao, Dong, Shidu, Dudzik, Wojciech, Eaton-Rosen, Zach, Egan, Gary, Escudero, Guilherme, Estienne, Théo, Everson, Richard, Fabrizio, Jonathan, Fan, Yong, Fang, Longwei, Feng, Xue, Ferrante, Enzo, Fidon, Lucas, Fischer, Martin, French, Andrew P., Fridman, Naomi, Fu, Huan, Fuentes, David, Gao, Yaozong, Gates, Evan, Gering, David, Gholami, Amir, Gierke, Willi, Glocker, Ben, Gong, Mingming, González-Villá, Sandra, Grosges, T., Guan, Yuanfang, Guo, Sheng, Gupta, Sudeep, Han, Woo-Sup, Han, Il Song, Harmuth, Konstantin, He, Huiguang, Hernández-Sabaté, Aura, Herrmann, Evelyn, Himthani, Naveen, Hsu, Winston, Hsu, Cheyu, Hu, Xiaojun, Hu, Xiaobin, Hu, Yan, Hu, Yifan, Hua, Rui, Huang, Teng-Yi, Huang, Weilin, Van Huffel, Sabine, Huo, Quan, HV, Vivek, Iftekharuddin, Khan M., Isensee, Fabian, Islam, Mobarakol, Jackson, Aaron S., Jambawalikar, Sachin R., Jesson, Andrew, Jian, Weijian, Jin, Peter, Jose, V Jeya Maria, Jungo, Alain, Kainz, B, Kamnitsas, Konstantinos, Kao, Po-Yu, Karnawat, Ayush, Kellermeier, Thomas, Kermi, Adel, Keutzer, Kurt, Khadir, Mohamed Tarek, Khened, Mahendra, Kickingereder, Philipp, Kim, Geena, King, Nik, Knapp, Haley, Knecht, Urspeter, Kohli, Lisa, Kong, Deren, Kong, Xiangmao, Koppers, Simon, Kori, Avinash, Krishnamurthi, Ganapathy, Krivov, Egor, Kumar, Piyush, Kushibar, Kaisar, Lachinov, Dmitrii, Lambrou, Tryphon, Lee, Joon, Lee, Chengen, Lee, Yuehchou, Lee, M, Lefkovits, Szidonia, Lefkovits, Laszlo, Levitt, James, Li, Tengfei, Li, Hongwei, Li, Wenqi, Li, Hongyang, Li, Xiaochuan, Li, Yuexiang, Li, Heng, Li, Zhenye, Li, Xiaoyu, Li, Zeju, Li, XiaoGang, Lin, Zheng-Shen, Lin, Fengming, Lio, Pietro, Liu, Chang, Liu, Boqiang, Liu, Xiang, Liu, Mingyuan, Liu, Ju, Liu, Luyan, Llado, Xavier, Lopez, Marc Moreno, Lorenzo, Pablo Ribalta, Lu, Zhentai, Luo, Lin, Luo, Zhigang, Ma, Jun, Ma, Kai, Mackie, Thomas, Madabushi, Anant, Mahmoudi, Issam, Maier-Hein, Klaus H., Maji, Pradipta, Mammen, CP, Mang, Andreas, Manjunath, B. S., Marcinkiewicz, Michal, McDonagh, S, McKenna, Stephen, McKinley, Richard, Mehl, Miriam, Mehta, Sachin, Mehta, Raghav, Meier, Raphael, Meinel, Christoph, Merhof, Dorit, Meyer, Craig, Miller, Robert, Mitra, Sushmita, Moiyadi, Aliasgar, Molina-Garcia, David, Monteiro, Miguel A. B., Mrukwa, Grzegorz, Myronenko, Andriy, Nalepa, Jakub, Ngo, Thuyen, Nie, Dong, Ning, Holly, Niu, Chen, Nuechterlein, Nicholas K, Oermann, Eric, Oliveira, Arlindo, Oliveira, Diego D. C., Oliver, Arnau, Osman, Alexander F. I., Ou, Yu-Nian, Ourselin, Sebastien, Paragios, Nikos, Park, Moo Sung, Paschke, Brad, Pauloski, J. Gregory, Pawar, Kamlesh, Pawlowski, Nick, Pei, Linmin, Peng, Suting, Pereira, Silvio M., Perez-Beteta, Julian, Perez-Garcia, Victor M., Pezold, Simon, Pham, Bao, Phophalia, Ashish, Piella, Gemma, Pillai, G. N., Piraud, Marie, Pisov, Maxim, Popli, Anmol, Pound, Michael P., Pourreza, Reza, Prasanna, Prateek, Prkovska, Vesna, Pridmore, Tony P., Puch, Santi, Puybareau, Élodie, Qian, Buyue, Qiao, Xu, Rajchl, Martin, Rane, Swapnil, Rebsamen, Michael, Ren, Hongliang, Ren, Xuhua, Revanuru, Karthik, Rezaei, Mina, Rippel, Oliver, Rivera, Luis Carlos, Robert, Charlotte, Rosen, Bruce, Rueckert, Daniel, Safwan, Mohammed, Salem, Mostafa, Salvi, Joaquim, Sanchez, Irina, Sánchez, Irina, Santos, Heitor M., Sartor, Emmett, Schellingerhout, Dawid, Scheufele, Klaudius, Scott, Matthew R., Scussel, Artur A., Sedlar, Sara, Serrano-Rubio, Juan Pablo, Shah, N. Jon, Shah, Nameetha, Shaikh, Mazhar, Shankar, B. Uma, Shboul, Zeina, Shen, Haipeng, Shen, Dinggang, Shen, Linlin, Shen, Haocheng, Shenoy, Varun, Shi, Feng, Shin, Hyung Eun, Shu, Hai, Sima, Diana, Sinclair, M, Smedby, Orjan, Snyder, James M., Soltaninejad, Mohammadreza, Song, Guidong, Soni, Mehul, Stawiaski, Jean, Subramanian, Shashank, Sun, Li, Sun, Roger, Sun, Jiawei, Sun, Kay, Sun, Yu, Sun, Guoxia, Sun, Shuang, Suter, Yannick R, Szilagyi, Laszlo, Talbar, Sanjay, Tao, Dacheng, Teng, Zhongzhao, Thakur, Siddhesh, Thakur, Meenakshi H, Tharakan, Sameer, Tiwari, Pallavi, Tochon, Guillaume, Tran, Tuan, Tsai, Yuhsiang M., Tseng, Kuan-Lun, Tuan, Tran Anh, Turlapov, Vadim, Tustison, Nicholas, Vakalopoulou, Maria, Valverde, Sergi, Vanguri, Rami, Vasiliev, Evgeny, Ventura, Jonathan, Vera, Luis, Vercauteren, Tom, Verrastro, C. A., Vidyaratne, Lasitha, Vilaplana, Veronica, Vivekanandan, Ajeet, Wang, Guotai, Wang, Qian, Wang, Chiatse J., Wang, Weichung, Wang, Duo, Wang, Ruixuan, Wang, Yuanyuan, Wang, Chunliang, Wen, Ning, Wen, Xin, Weninger, Leon, Wick, Wolfgang, Wu, Shaocheng, Wu, Qiang, Wu, Yihong, Xia, Yong, Xu, Yanwu, Xu, Xiaowen, Xu, Peiyuan, Yang, Tsai-Ling, Yang, Xiaoping, Yang, Hao-Yu, Yang, Junlin, Yang, Haojin, Yang, Guang, Yao, Hongdou, Ye, Xujiong, Yin, Changchang, Young-Moxon, Brett, Yu, Jinhua, Yue, Xiangyu, Zhang, Songtao, Zhang, Angela, Zhang, Kun, Zhang, Xuejie, Zhang, Lichi, Zhang, Xiaoyue, Zhang, Yazhuo, Zhang, Lei, Zhang, Jianguo, Zhang, Xiang, Zhang, Tianhao, Zhao, Sicheng, Zhao, Yu, Zhao, Xiaomei, Zhao, Liang, Zheng, Yefeng, Zhong, Liming, Zhou, Chenhong, Zhou, Xiaobing, Zhou, Fan, Zhu, Hongtu, Zhu, Jin, Zhuge, Ying, Zong, Weiwei, Kalpathy-Cramer, Jayashree, Farahani, Keyvan, Davatzikos, Christos, van Leemput, Koen, and Menze, Bjoern

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

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset., Comment: The International Multimodal Brain Tumor Segmentation (BraTS) Challenge

Published
2018

8. Integration of resting state functional MRI into clinical practice - A large single institution experience.

Author

Leuthardt, Eric C, Guzman, Gloria, Bandt, S Kathleen, Hacker, Carl, Vellimana, Ananth K, Limbrick, David, Milchenko, Mikhail, Lamontagne, Pamela, Speidel, Benjamin, Roland, Jarod, Miller-Thomas, Michelle, Snyder, Abraham Z, Marcus, Daniel, Shimony, Joshua, and Benzinger, Tammie LS

Subjects
Cerebral Cortex, Humans, Brain Neoplasms, Nervous System Diseases, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Algorithms, Rest, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, Child, Child, Preschool, Female, Male, Vascular Malformations, Young Adult, Preoperative Period, Drug Resistant Epilepsy, and over, Preschool, Image Interpretation, Computer-Assisted, General Science & Technology
Abstract

Functional magnetic resonance imaging (fMRI) is an important tool for pre-surgical evaluation of eloquent cortex. Classic task-based paradigms require patient participation and individual imaging sequence acquisitions for each functional domain that is being assessed. Resting state fMRI (rs-fMRI), however, enables functional localization without patient participation and can evaluate numerous functional domains with a single imaging session. To date, post-processing of this resting state data has been resource intensive, which limits its widespread application for routine clinical use. Through a novel automated algorithm and advanced imaging IT structure, we report the clinical application and the large-scale integration of rs-fMRI into routine neurosurgical practice. One hundred and ninety one consecutive patients underwent a 3T rs-fMRI, 83 of whom also underwent both motor and language task-based fMRI. Data were processed using a novel, automated, multi-layer perceptron algorithm and integrated into stereotactic navigation using a streamlined IT imaging pipeline. One hundred eighty-five studies were performed for intracranial neoplasm, 14 for refractory epilepsy and 33 for vascular malformations or other neurological disorders. Failure rate of rs-fMRI of 13% was significantly better than that for task-based fMRI (38.5%,) (p

Published
2018

9. Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

Author

Thakur, Siddhesh, Doshi, Jimit, Pati, Sarthak, Rathore, Saima, Sako, Chiharu, Bilello, Michel, Ha, Sung Min, Shukla, Gaurav, Flanders, Adam, Kotrotsou, Aikaterini, Milchenko, Mikhail, Liem, Spencer, Alexander, Gregory S., Lombardo, Joseph, Palmer, Joshua D., LaMontagne, Pamela, Nazeri, Arash, Talbar, Sanjay, Kulkarni, Uday, Marcus, Daniel, Colen, Rivka, Davatzikos, Christos, Erus, Guray, and Bakas, Spyridon

Published
2020
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12. Optimal Atlas Registration and Resting State Functional Architecture in Patients with Glioblastoma

Author

Park, Ki Yun, primary, Chakrabarty, Satrajit, additional, Tanenbaum, Aaron B., additional, Hacker, Carl D., additional, Donovan, Kara M., additional, Luckett, Patrick H., additional, Milchenko, Mikhail, additional, Sotiras, Aristeidis, additional, Marcus, Daniel S., additional, Shimony, Joshua S., additional, Leuthardt, Eric C., additional, and Snyder, Abraham Z., additional

Published
2023
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13. Optimal Atlas Registration and Resting State Functional Architecture in Patients with Glioblastoma

Author

Park, Ki Yun, primary, Shimony, Joshua S., additional, Chakrabarty, Satrajit, additional, Tanenbaum, Aaron B., additional, Hacker, Carl D., additional, Donovan, Kara M., additional, Luckett, Patrick H., additional, Milchenko, Mikhail, additional, Sotiras, Aristeidis, additional, Marcus, Daniel S., additional, Leuthardt, Eric C., additional, and Snyder, Abraham Z., additional

Published
2022
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15. QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation – Analysis of Ranking Scores and Benchmarking Results

Author

Mehta, Raghav, primary, Filos, Angelos, additional, Baid, Ujjwal, additional, Sako, Chiharu, additional, McKinley, Richard, additional, Rebsamen, Michael, additional, Dätwyler, Katrin, additional, Meier, Raphael, additional, Radojewski, Piotr, additional, Murugesan, Gowtham Krishnan, additional, Nalawade, Sahil, additional, Ganesh, Chandan, additional, Wagner, Ben, additional, Yu, Fang F., additional, Fei, Baowei, additional, Madhuranthakam, Ananth J., additional, Maldjian, Joseph A., additional, Daza, Laura, additional, Gómez, Catalina, additional, Arbeláez, Pablo, additional, Dai, Chengliang, additional, Wang, Shuo, additional, Reynaud, Hadrien, additional, Mo, Yuanhan, additional, Angelini, Elsa, additional, Guo, Yike, additional, Bai, Wenjia, additional, Banerjee, Subhashis, additional, Pei, Linmin, additional, AK, Murat, additional, Rosas-González, Sarahi, additional, Zemmoura, Ilyess, additional, Tauber, Clovis, additional, Vu, Minh H., additional, Nyholm, Tufve, additional, Löfstedt, Tommy, additional, Ballestar, Laura Mora, additional, Vilaplana, Veronica, additional, McHugh, Hugh, additional, Maso Talou, Gonzalo, additional, Wang, Alan, additional, Patel, Jay, additional, Chang, Ken, additional, Hoebel, Katharina, additional, Gidwani, Mishka, additional, Arun, Nishanth, additional, Gupta, Sharut, additional, Aggarwal, Mehak, additional, Singh, Praveer, additional, Gerstner, Elizabeth R., additional, Kalpathy-Cramer, Jayashree, additional, Boutry, Nicolas, additional, Huard, Alexis, additional, Vidyaratne, Lasitha, additional, Rahman, Md Monibor, additional, Iftekharuddin, Khan M., additional, Chazalon, Joseph, additional, Puybareau, Elodie, additional, Tochon, Guillaume, additional, Ma, Jun, additional, Cabezas, Mariano, additional, Llado, Xavier, additional, Oliver, Arnau, additional, Valencia, Liliana, additional, Valverde, Sergi, additional, Amian, Mehdi, additional, Soltaninejad, Mohammadreza, additional, Myronenko, Andriy, additional, Hatamizadeh, Ali, additional, Feng, Xue, additional, Dou, Quan, additional, Tustison, Nicholas, additional, Meyer, Craig, additional, Shah, Nisarg A., additional, Talbar, Sanjay, additional, Weber, Marc-André, additional, Mahajan, Abhishek, additional, Jakab, Andras, additional, Wiest, Roland, additional, Fathallah-Shaykh, Hassan M., additional, Nazeri, Arash, additional, Milchenko, Mikhail, additional, Marcus, Daniel, additional, Kotrotsou, Aikaterini, additional, Colen, Rivka, additional, Freymann, John, additional, Kirby, Justin, additional, Davatzikos, Christos, additional, Menze, Bjoern, additional, Bakas, Spyridon, additional, Gal, Yarin, additional, and Arbel, Tal, additional

Published
2022
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17. QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

Author

Mehta, Raghav, Filos, Angelos, Baid, Ujjwal, Sako, Chiharu, McKinley, Richard, Rebsamen, Michael, Dätwyler, Katrin, Meier, Raphael, Radojewski, Piotr, Murugesan, Gowtham Krishnan, Nalawade, Sahil, Ganesh, Chandan, Wagner, Ben, Yu, Fang F., Fei, Baowei, Madhuranthakam, Ananth J., Maldjian, Joseph A., Daza, Laura, Gómez, Catalina, Arbeláez, Pablo, Dai, Chengliang, Wang, Shuo, Reynaud, Hadrien, Mo, Yuanhan, Angelini, Elsa, Guo, Yike, Bai, Wenjia, Banerjee, Subhashis, Pei, Linmin, AK, Murat, Rosas-González, Sarahi, Zemmoura, Ilyess, Tauber, Clovis, Vu, Minh Hoang, Nyholm, Tufve, Löfstedt, Tommy, Ballestar, Laura Mora, Vilaplana, Veronica, McHugh, Hugh, Talou, Gonzalo Maso, Wang, Alan, Patel, Jay, Chang, Ken, Hoebel, Katharina, Gidwani, Mishka, Arun, Nishanth, Gupta, Sharut, Aggarwal, Mehak, Singh, Praveer, Gerstner, Elizabeth R., Kalpathy-Cramer, Jayashree, Boutry, Nicolas, Huard, Alexis, Vidyaratne, Lasitha, Rahman, Md Monibor, Iftekharuddin, Khan M., Chazalon, Joseph, Puybareau, Elodie, Tochon, Guillaume, Ma, Jun, Cabezas, Mariano, Llado, Xavier, Oliver, Arnau, Valencia, Liliana, Valverde, Sergi, Amian, Mehdi, Soltaninejad, Mohammadreza, Myronenko, Andriy, Hatamizadeh, Ali, Feng, Xue, Dou, Quan, Tustison, Nicholas, Meyer, Craig, Shah, Nisarg A., Talbar, Sanjay, Weber, Marc-André, Mahajan, Abhishek, Jakab, Andras, Wiest, Roland, Fathallah-Shaykh, Hassan M., Nazeri, Arash, Milchenko, Mikhail, Marcus, Daniel, Kotrotsou, Aikaterini, Colen, Rivka, Freymann, John, Kirby, Justin, Davatzikos, Christos, Menze, Bjoern, Bakas, Spyridon, Gal, Yarin, and Arbel, Tal

Subjects
Segmentation, Deep Learning, Datorseende och robotik (autonoma system), Övrig annan medicin och hälsovetenskap, Brain Tumors, Uncertainty Quantification, Glioma, Neuro-Oncology, Glioblastoma, Computer Vision and Robotics (Autonomous Systems), Other Medical Sciences not elsewhere specified, Trustworthiness
Abstract

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS

Published
2022

18. NIMG-39. RADIOMIC ANALYSIS FOR NON-INVASIVE IN VIVO PROGNOSTIC STRATIFICATION OF DE NOVO GLIOBLASTOMA PATIENTS: A MULTI-INSTITUTIONAL EVALUATION FOR GENERALIZABILITY IN THE RESPOND CONSORTIUM

Author

Akbari*, Hamed, primary, Bakas*, Spyridon, additional, Sako*, Chiharu, additional, Kazerooni, Anahita Fathi, additional, Garcia, Jose A, additional, Bagley, Stephen J, additional, Mohan, Suyash, additional, Ahn, Sung Soo, additional, Ak, Murat, additional, Alexander, Gregory S, additional, Ali, Ayesha S, additional, Baid, Ujjwal, additional, Bavde, Chaitra, additional, Bilello, Michel, additional, Brem, Steven, additional, Capellades, Jaume, additional, Chang, Jong Hee, additional, Choi, Yoon Seong, additional, Dicker, Adam P, additional, Fathallah-Shaykh, Hassan, additional, Flanders, Adam E, additional, Griffith, Brent D, additional, LaMontagne, Pamela, additional, Lee, Matthew, additional, Lee, Seung-Koo, additional, Liem, Spencer, additional, Lombardo, Joseph, additional, Lustig, Robert A, additional, Mahajan, Abhishek, additional, Milchenko, Mikhail, additional, Nasrallah, MacLean, additional, Nazeri, Arash, additional, Puig, Josep, additional, Shukla, Gaurav, additional, Sloan, Andrew, additional, Taylor, William, additional, Vadmal, Vachan, additional, Waite, Kristin, additional, Balana, Carmen, additional, Booth, Thomas C, additional, Cepeda, Santiago, additional, Poisson, Laila, additional, Colen, Rivka R, additional, Marcus, Daniel S, additional, Palmer, Joshua, additional, Jain, Rajan, additional, Shi, Wenyin, additional, O’Rourke, Donald M, additional, Barnholtz-Sloan, Jill, additional, and Davatzikos, Christos, additional

Published
2021
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19. NIMG-22. PREDICTION OF GLIOBLASTOMA CELLULAR INFILTRATION AND RECURRENCE USING MACHINE LEARNING AND MULTI-PARAMETRIC MRI ANALYSIS: RESULTS FROM THE MULTI-INSTITUTIONAL RESPOND CONSORTIUM

Author

Akbari, Hamed, primary, Mohan, Suyash, additional, Garcia, Jose A, additional, Kazerooni, Anahita Fathi, additional, Sako, Chiharu, additional, Bakas, Spyridon, additional, Shukla, Gaurav, additional, Bagley, Stephen J, additional, Ahn, Sung Soo, additional, Ak, Murat, additional, Alexander, Gregory S, additional, Ali, Ayesha S, additional, Baid, Ujjwal, additional, Bavde, Chaitra, additional, Brem, Steven, additional, Capellades, Jaume, additional, Chang, Jong Hee, additional, Choi, Yoon Seong, additional, Dicker, Adam P, additional, Fathallah-Shaykh, Hassan, additional, Flanders, Adam E, additional, Griffith, Brent D, additional, LaMontagne, Pamela, additional, Lee, Matthew, additional, Lee, Seung-Koo, additional, Liem, Spencer, additional, Lombardo, Joseph, additional, Mahajan, Abhishek, additional, Milchenko, Mikhail, additional, Nazeri, Arash, additional, Puig, Josep, additional, Sloan, Andrew, additional, Taylor, William, additional, Vadmal, Vachan, additional, Waite, Kristin, additional, Nasrallah, MacLean, additional, Bilello, Michel, additional, Lustig, Robert A, additional, Balana, Carmen, additional, Booth, Thomas C, additional, Cepeda, Santiago, additional, Poisson, Laila, additional, Colen, Rivka R, additional, Marcus, Daniel S, additional, Palmer, Joshua, additional, Jain, Rajan, additional, Shi, Wenyin, additional, O’Rourke, Donald M, additional, Barnholtz-Sloan, Jill, additional, and Davatzikos, Christos, additional

Published
2021
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23. Resting‐State Functional Connectivity Predicts STN DBS Clinical Response

Author

Younce, John R., primary, Campbell, Meghan C., additional, Hershey, Tamara, additional, Tanenbaum, Aaron B., additional, Milchenko, Mikhail, additional, Ushe, Mwiza, additional, Karimi, Morvarid, additional, Tabbal, Samer D., additional, Kim, Albert E., additional, Snyder, Abraham Z., additional, Perlmutter, Joel S., additional, and Norris, Scott A., additional

Published
2020
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24. A feasibility study to evaluate early treatment response of brain metastases one week after stereotactic radiosurgery using perfusion weighted imaging

Author

Huang, Jiayi, primary, Milchenko, Mikhail, additional, Rao, Yuan J., additional, LaMontagne, Pamela, additional, Abraham, Christopher, additional, Robinson, Clifford G., additional, Huang, Yi, additional, Shimony, Joshua S., additional, Rich, Keith M., additional, and Benzinger, Tammie, additional

Published
2020
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27. Heterogeneity Diffusion Imaging of gliomas: Initial experience and validation

Author

Wang, Qing, primary, Pérez-Carrillo, Gloria J. Guzmán, additional, Ponisio, Maria Rosana, additional, LaMontagne, Pamela, additional, Dahiya, Sonika, additional, Marcus, Daniel S., additional, Milchenko, Mikhail, additional, Shimony, Joshua, additional, Liu, Jingxia, additional, Chen, Gengsheng, additional, Salter, Amber, additional, Massoumzadeh, Parinaz, additional, Miller-Thomas, Michelle M., additional, Rich, Keith M., additional, McConathy, Jonathan, additional, Benzinger, Tammie L. S., additional, and Wang, Yong, additional

Published
2019
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29. De‐identification procedures for magnetic resonance images and the impact on structural brain measures at different ages.

Author

Buimer, Elizabeth E. L., Schnack, Hugo G., Caspi, Yaron, Haren, Neeltje E. M., Milchenko, Mikhail, Pas, Pascal, Hulshoff Pol, Hilleke E., and Brouwer, Rachel M.

Subjects
MAGNETIC resonance imaging, BRAIN imaging, YOUNG adults, INTRACLASS correlation
Abstract

Surface rendering of MRI brain scans may lead to identification of the participant through facial characteristics. In this study, we evaluate three methods that overwrite voxels containing privacy‐sensitive information: Face Masking, FreeSurfer defacing, and FSL defacing. We included structural T1‐weighted MRI scans of children, young adults and older adults. For the young adults, test–retest data were included with a 1‐week interval. The effects of the de‐identification methods were quantified using different statistics to capture random variation and systematic noise in measures obtained through the FreeSurfer processing pipeline. Face Masking and FSL defacing impacted brain voxels in some scans especially in younger participants. FreeSurfer defacing left brain tissue intact in all cases. FSL defacing and FreeSurfer defacing preserved identifiable characteristics around the eyes or mouth in some scans. For all de‐identification methods regional brain measures of subcortical volume, cortical volume, cortical surface area, and cortical thickness were on average highly replicable when derived from original versus de‐identified scans with average regional correlations >.90 for children, young adults, and older adults. Small systematic biases were found that incidentally resulted in significantly different brain measures after de‐identification, depending on the studied subsample, de‐identification method, and brain metric. In young adults, test–retest intraclass correlation coefficients (ICCs) were comparable for original scans and de‐identified scans with average regional ICCs >.90 for (sub)cortical volume and cortical surface area and ICCs >.80 for cortical thickness. We conclude that apparent visual differences between de‐identification methods minimally impact reliability of brain measures, although small systematic biases can occur. [ABSTRACT FROM AUTHOR]

Published
2021
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30. TRLS-02. A PILOT STUDY OF EVALUATING EARLY TREATMENT RESPONSE OF BRAIN METASTASES AFTER STEREOTACTIC RADIOSURGERY USING DYNAMIC SUSCEPTIBILITY-WEIGHTED PERFUSION MAGNETIC RESONANCE IMAGING

Author

Huang, Jiayi, primary, Rao, Yuan, additional, Milchenko, Mikhail, additional, LaMontagne, Pamela, additional, Abraham, Christopher, additional, Robinson, Clifford, additional, Wan, Leping, additional, Shimony, Joshua, additional, Rich, Keith, additional, and Benzinger, Tammie, additional

Published
2019
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34. Resting‐State Functional Connectivity Predicts STN DBS Clinical Response.

Author

Younce, John R., Campbell, Meghan C., Hershey, Tamara, Tanenbaum, Aaron B., Milchenko, Mikhail, Ushe, Mwiza, Karimi, Morvarid, Tabbal, Samer D., Kim, Albert E., Snyder, Abraham Z., Perlmutter, Joel S., and Norris, Scott A.

Abstract

Background: Deep brain stimulation of the subthalamic nucleus is a widely used adjunctive therapy for motor symptoms of Parkinson's disease, but with variable motor response. Predicting motor response remains difficult, and novel approaches may improve surgical outcomes as well as the understanding of pathophysiological mechanisms. The objective of this study was to determine whether preoperative resting‐state functional connectivity MRI predicts motor response from deep brain stimulation of the subthalamic nucleus. Methods: We collected preoperative resting‐state functional MRI from 70 participants undergoing subthalamic nucleus deep brain stimulation. For this cohort, we analyzed the strength of STN functional connectivity with seeds determined by stimulation‐induced (ON/OFF) 15O H2O PET regional cerebral blood flow differences in a partially overlapping group (n = 42). We correlated STN‐seed functional connectivity strength with postoperative motor outcomes and applied linear regression to predict motor outcomes. Results: Preoperative functional connectivity between the left subthalamic nucleus and the ipsilateral internal globus pallidus correlated with postsurgical motor outcomes (r = −0.39, P = 0.0007), with stronger preoperative functional connectivity relating to greater improvement. Left pallidal‐subthalamic nucleus connectivity also predicted motor response to DBS after controlling for covariates. Discussion: Preoperative pallidal‐subthalamic nucleus resting‐state functional connectivity predicts motor benefit from deep brain stimulation, although this should be validated prospectively before clinical application. These observations suggest that integrity of pallidal‐subthalamic nucleus circuits may be critical to motor benefits from deep brain stimulation. © 2020 International Parkinson and Movement Disorder Society [ABSTRACT FROM AUTHOR]

Published
2021
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39. The Comprehensive Neuro-Oncology Data Repository (CONDR)

Author

Fouke, Sarah Jost, primary, Benzinger, Tammie L., additional, Milchenko, Mikhail, additional, LaMontagne, Pamela, additional, Shimony, Joshua S., additional, Chicoine, Michael R., additional, Rich, Keith M., additional, Kim, Albert H., additional, Leuthardt, Eric C., additional, Keogh, Bart, additional, and Marcus, Daniel S., additional

Published
2014
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40. Predicting a multi-parametric probability map of active tumor extent using random forests

Author

Prior, Fred W., primary, Fouke, Sarah J., additional, Benzinger, Tammie, additional, Boyd, Alicia, additional, Chicoine, Michael, additional, Cholleti, Sharath, additional, Kelsey, Matthew, additional, Keogh, Bart, additional, Kim, Lauren, additional, Milchenko, Mikhail, additional, Politte, David G., additional, Tyree, Stephen, additional, Weinberger, Kilian, additional, and Marcus, Daniel, additional

Published
2013
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42. Cover Image.

Author

Buimer, Elizabeth E. L., Schnack, Hugo G., Caspi, Yaron, Haren, Neeltje E. M., Milchenko, Mikhail, Pas, Pascal, Hulshoff Pol, Hilleke E., and Brouwer, Rachel M.

Subjects
BRAIN imaging
Published
2021
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45. Aerobic glycolysis in brain tumors

Author

Vlassenko, Andrei, Couture, Lars, Su, Yi, Massoumzadeh, Parinaz, Milchenko, Mikhail, Mcconathy, Jonathan, Marcus, Daniel, Raichle, Marcus, Fouke, Sarah, and Tammie Benzinger

46. The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn) .

Author

Li HB, Conte GM, Anwar SM, Kofler F, Ezhov I, van Leemput K, Piraud M, Diaz M, Cole B, Calabrese E, Rudie J, Meissen F, Adewole M, Janas A, Kazerooni AF, LaBella D, Moawad AW, Farahani K, Eddy J, Bergquist T, Chung V, Shinohara RT, Dako F, Wiggins W, Reitman Z, Wang C, Liu X, Jiang Z, Familiar A, Johanson E, Meier Z, Davatzikos C, Freymann J, Kirby J, Bilello M, Fathallah-Shaykh HM, Wiest R, Kirschke J, Colen RR, Kotrotsou A, Lamontagne P, Marcus D, Milchenko M, Nazeri A, Weber MA, Mahajan A, Mohan S, Mongan J, Hess C, Cha S, Villanueva-Meyer J, Colak E, Crivellaro P, Jakab A, Albrecht J, Anazodo U, Aboian M, Yu T, Chung V, Bergquist T, Eddy J, Albrecht J, Baid U, Bakas S, Linguraru MG, Menze B, Iglesias JE, and Wiestler B

Abstract

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

Published
2023

47. QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.

Author

Mehta R, Filos A, Baid U, Sako C, McKinley R, Rebsamen M, Dätwyler K, Meier R, Radojewski P, Murugesan GK, Nalawade S, Ganesh C, Wagner B, Yu FF, Fei B, Madhuranthakam AJ, Maldjian JA, Daza L, Gómez C, Arbeláez P, Dai C, Wang S, Reynaud H, Mo Y, Angelini E, Guo Y, Bai W, Banerjee S, Pei L, Ak M, Rosas-González S, Zemmoura I, Tauber C, Vu MH, Nyholm T, Löfstedt T, Ballestar LM, Vilaplana V, McHugh H, Maso Talou G, Wang A, Patel J, Chang K, Hoebel K, Gidwani M, Arun N, Gupta S, Aggarwal M, Singh P, Gerstner ER, Kalpathy-Cramer J, Boutry N, Huard A, Vidyaratne L, Rahman MM, Iftekharuddin KM, Chazalon J, Puybareau E, Tochon G, Ma J, Cabezas M, Llado X, Oliver A, Valencia L, Valverde S, Amian M, Soltaninejad M, Myronenko A, Hatamizadeh A, Feng X, Dou Q, Tustison N, Meyer C, Shah NA, Talbar S, Weber MA, Mahajan A, Jakab A, Wiest R, Fathallah-Shaykh HM, Nazeri A, Milchenko M, Marcus D, Kotrotsou A, Colen R, Freymann J, Kirby J, Davatzikos C, Menze B, Bakas S, Gal Y, and Arbel T

Abstract

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS., Competing Interests: Conflicts of Interest The conflicts of interest have not been entered yet.

Published
2022

48. Predicting a multi-parametric probability map of active tumor extent using random forests.

Author

Prior FW, Fouke SJ, Benzinger T, Boyd A, Chicoine M, Cholleti S, Kelsey M, Keogh B, Kim L, Milchenko M, Politte DG, Tyree S, Weinberger K, and Marcus D

Subjects
Algorithms, Artificial Intelligence, Contrast Media, Diagnostic Imaging, Humans, Image Processing, Computer-Assisted, Pattern Recognition, Automated, Predictive Value of Tests, Probability, ROC Curve, Brain Neoplasms diagnosis, Brain Neoplasms pathology, Glioblastoma diagnosis, Glioblastoma pathology, Magnetic Resonance Imaging
Abstract

Glioblastoma Mulitforme is highly infiltrative, making precise delineation of tumor margin difficult. Multimodality or multi-parametric MR imaging sequences promise an advantage over anatomic sequences such as post contrast enhancement as methods for determining the spatial extent of tumor involvement. In considering multi-parametric imaging sequences however, manual image segmentation and classification is time-consuming and prone to error. As a preliminary step toward integration of multi-parametric imaging into clinical assessments of primary brain tumors, we propose a machine-learning based multi-parametric approach that uses radiologist generated labels to train a classifier that is able to classify tissue on a voxel-wise basis and automatically generate a tumor segmentation. A random forests classifier was trained using a leave-one-out experimental paradigm. A simple linear classifier was also trained for comparison. The random forests classifier accurately predicted radiologist generated segmentations and tumor extent.

Published
2013
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