35 results on '"Casella, Alessandro"'
Search Results
2. Why is the winner the best?
- Author
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Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Ali, Sharib, Andrearczyk, Vincent, Aubreville, Marc, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Cheplygina, Veronika, Daum, Marie, de Bruijne, Marleen, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Ellis, David G., Engelhardt, Sandy, Ganz, Melanie, Ghatwary, Noha, Girard, Gabriel, Godau, Patrick, Gupta, Anubha, Hansen, Lasse, Harada, Kanako, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Jannin, Pierre, Kavur, Ali Emre, Kodym, Oldřich, Kozubek, Michal, Li, Jianning, Li, Hongwei, Ma, Jun, Martín-Isla, Carlos, Menze, Bjoern, Noble, Alison, Oreiller, Valentin, Padoy, Nicolas, Pati, Sarthak, Payette, Kelly, Rädsch, Tim, Rafael-Patiño, Jonathan, Bawa, Vivek Singh, Speidel, Stefanie, Sudre, Carole H., van Wijnen, Kimberlin, Wagner, Martin, Wei, Donglai, Yamlahi, Amine, Yap, Moi Hoon, Yuan, Chun, Zenk, Maximilian, Zia, Aneeq, Zimmerer, David, Aydogan, Dogu Baran, Bhattarai, Binod, Bloch, Louise, Brüngel, Raphael, Cho, Jihoon, Choi, Chanyeol, Dou, Qi, Ezhov, Ivan, Friedrich, Christoph M., Fuller, Clifton, Gaire, Rebati Raman, Galdran, Adrian, Faura, Álvaro García, Grammatikopoulou, Maria, Hong, SeulGi, Jahanifar, Mostafa, Jang, Ikbeom, Kadkhodamohammadi, Abdolrahim, Kang, Inha, Kofler, Florian, Kondo, Satoshi, Kuijf, Hugo, Li, Mingxing, Luu, Minh Huan, Martinčič, Tomaž, Morais, Pedro, Naser, Mohamed A., Oliveira, Bruno, Owen, David, Pang, Subeen, Park, Jinah, Park, Sung-Hong, Płotka, Szymon, Puybareau, Elodie, Rajpoot, Nasir, Ryu, Kanghyun, Saeed, Numan, Shephard, Adam, Shi, Pengcheng, Štepec, Dejan, Subedi, Ronast, Tochon, Guillaume, Torres, Helena R., Urien, Helene, Vilaça, João L., Wahid, Kareem Abdul, Wang, Haojie, Wang, Jiacheng, Wang, Liansheng, Wang, Xiyue, Wiestler, Benedikt, Wodzinski, Marek, Xia, Fangfang, Xie, Juanying, Xiong, Zhiwei, Yang, Sen, Yang, Yanwu, Zhao, Zixuan, Maier-Hein, Klaus, Jäger, Paul F., Kopp-Schneider, Annette, and Maier-Hein, Lena
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work., Comment: accepted to CVPR 2023
- Published
- 2023
3. Biomedical image analysis competitions: The state of current participation practice
- Author
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Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Godau, Patrick, Cheplygina, Veronika, Kozubek, Michal, Ali, Sharib, Gupta, Anubha, Kybic, Jan, Noble, Alison, de Solórzano, Carlos Ortiz, Pachade, Samiksha, Petitjean, Caroline, Sage, Daniel, Wei, Donglai, Wilden, Elizabeth, Alapatt, Deepak, Andrearczyk, Vincent, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bawa, Vivek Singh, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Choi, Jinwook, Commowick, Olivier, Daum, Marie, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Eichhorn, Hannah, Engelhardt, Sandy, Ganz, Melanie, Girard, Gabriel, Hansen, Lasse, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Kim, Hyunjeong, Landman, Bennett, Li, Hongwei Bran, Li, Jianning, Ma, Jun, Martel, Anne, Martín-Isla, Carlos, Menze, Bjoern, Nwoye, Chinedu Innocent, Oreiller, Valentin, Padoy, Nicolas, Pati, Sarthak, Payette, Kelly, Sudre, Carole, van Wijnen, Kimberlin, Vardazaryan, Armine, Vercauteren, Tom, Wagner, Martin, Wang, Chuanbo, Yap, Moi Hoon, Yu, Zeyun, Yuan, Chun, Zenk, Maximilian, Zia, Aneeq, Zimmerer, David, Bao, Rina, Choi, Chanyeol, Cohen, Andrew, Dzyubachyk, Oleh, Galdran, Adrian, Gan, Tianyuan, Guo, Tianqi, Gupta, Pradyumna, Haithami, Mahmood, Ho, Edward, Jang, Ikbeom, Li, Zhili, Luo, Zhengbo, Lux, Filip, Makrogiannis, Sokratis, Müller, Dominik, Oh, Young-tack, Pang, Subeen, Pape, Constantin, Polat, Gorkem, Reed, Charlotte Rosalie, Ryu, Kanghyun, Scherr, Tim, Thambawita, Vajira, Wang, Haoyu, Wang, Xinliang, Xu, Kele, Yeh, Hung, Yeo, Doyeob, Yuan, Yixuan, Zeng, Yan, Zhao, Xin, Abbing, Julian, Adam, Jannes, Adluru, Nagesh, Agethen, Niklas, Ahmed, Salman, Khalil, Yasmina Al, Alenyà, Mireia, Alhoniemi, Esa, An, Chengyang, Anwar, Talha, Arega, Tewodros Weldebirhan, Avisdris, Netanell, Aydogan, Dogu Baran, Bai, Yingbin, Calisto, Maria Baldeon, Basaran, Berke Doga, Beetz, Marcel, Bian, Cheng, Bian, Hao, Blansit, Kevin, Bloch, Louise, Bohnsack, Robert, Bosticardo, Sara, Breen, Jack, Brudfors, Mikael, Brüngel, Raphael, Cabezas, Mariano, Cacciola, Alberto, Chen, Zhiwei, Chen, Yucong, Chen, Daniel Tianming, Cho, Minjeong, Choi, Min-Kook, Xie, Chuantao Xie Chuantao, Cobzas, Dana, Cohen-Adad, Julien, Acero, Jorge Corral, Das, Sujit Kumar, de Oliveira, Marcela, Deng, Hanqiu, Dong, Guiming, Doorenbos, Lars, Efird, Cory, Escalera, Sergio, Fan, Di, Serj, Mehdi Fatan, Fenneteau, Alexandre, Fidon, Lucas, Filipiak, Patryk, Finzel, René, Freitas, Nuno R., Friedrich, Christoph M., Fulton, Mitchell, Gaida, Finn, Galati, Francesco, Galazis, Christoforos, Gan, Chang Hee, Gao, Zheyao, Gao, Shengbo, Gazda, Matej, Gerats, Beerend, Getty, Neil, Gibicar, Adam, Gifford, Ryan, Gohil, Sajan, Grammatikopoulou, Maria, Grzech, Daniel, Güley, Orhun, Günnemann, Timo, Guo, Chunxu, Guy, Sylvain, Ha, Heonjin, Han, Luyi, Han, Il Song, Hatamizadeh, Ali, He, Tian, Heo, Jimin, Hitziger, Sebastian, Hong, SeulGi, Hong, SeungBum, Huang, Rian, Huang, Ziyan, Huellebrand, Markus, Huschauer, Stephan, Hussain, Mustaffa, Inubushi, Tomoo, Polat, Ece Isik, Jafaritadi, Mojtaba, Jeong, SeongHun, Jian, Bailiang, Jiang, Yuanhong, Jiang, Zhifan, Jin, Yueming, Joshi, Smriti, Kadkhodamohammadi, Abdolrahim, Kamraoui, Reda Abdellah, Kang, Inha, Kang, Junghwa, Karimi, Davood, Khademi, April, Khan, Muhammad Irfan, Khan, Suleiman A., Khantwal, Rishab, Kim, Kwang-Ju, Kline, Timothy, Kondo, Satoshi, Kontio, Elina, Krenzer, Adrian, Kroviakov, Artem, Kuijf, Hugo, Kumar, Satyadwyoom, La Rosa, Francesco, Lad, Abhi, Lee, Doohee, Lee, Minho, Lena, Chiara, Li, Hao, Li, Ling, Li, Xingyu, Liao, Fuyuan, Liao, KuanLun, Oliveira, Arlindo Limede, Lin, Chaonan, Lin, Shan, Linardos, Akis, Linguraru, Marius George, Liu, Han, Liu, Tao, Liu, Di, Liu, Yanling, Lourenço-Silva, João, Lu, Jingpei, Lu, Jiangshan, Luengo, Imanol, Lund, Christina B., Luu, Huan Minh, Lv, Yi, Macar, Uzay, Maechler, Leon, L., Sina Mansour, Marshall, Kenji, Mazher, Moona, McKinley, Richard, Medela, Alfonso, Meissen, Felix, Meng, Mingyuan, Miller, Dylan, Mirjahanmardi, Seyed Hossein, Mishra, Arnab, Mitha, Samir, Mohy-ud-Din, Hassan, Mok, Tony Chi Wing, Murugesan, Gowtham Krishnan, Karthik, Enamundram Naga, Nalawade, Sahil, Nalepa, Jakub, Naser, Mohamed, Nateghi, Ramin, Naveed, Hammad, Nguyen, Quang-Minh, Quoc, Cuong Nguyen, Nichyporuk, Brennan, Oliveira, Bruno, Owen, David, Pal, Jimut Bahan, Pan, Junwen, Pan, Wentao, Pang, Winnie, Park, Bogyu, Pawar, Vivek, Pawar, Kamlesh, Peven, Michael, Philipp, Lena, Pieciak, Tomasz, Plotka, Szymon, Plutat, Marcel, Pourakpour, Fattaneh, Preložnik, Domen, Punithakumar, Kumaradevan, Qayyum, Abdul, Queirós, Sandro, Rahmim, Arman, Razavi, Salar, Ren, Jintao, Rezaei, Mina, Rico, Jonathan Adam, Rieu, ZunHyan, Rink, Markus, Roth, Johannes, Ruiz-Gonzalez, Yusely, Saeed, Numan, Saha, Anindo, Salem, Mostafa, Sanchez-Matilla, Ricardo, Schilling, Kurt, Shao, Wei, Shen, Zhiqiang, Shi, Ruize, Shi, Pengcheng, Sobotka, Daniel, Soulier, Théodore, Fadida, Bella Specktor, Stoyanov, Danail, Mun, Timothy Sum Hon, Sun, Xiaowu, Tao, Rong, Thaler, Franz, Théberge, Antoine, Thielke, Felix, Torres, Helena, Wahid, Kareem A., Wang, Jiacheng, Wang, YiFei, Wang, Wei, Wang, Xiong, Wen, Jianhui, Wen, Ning, Wodzinski, Marek, Wu, Ye, Xia, Fangfang, Xiang, Tianqi, Xiaofei, Chen, Xu, Lizhan, Xue, Tingting, Yang, Yuxuan, Yang, Lin, Yao, Kai, Yao, Huifeng, Yazdani, Amirsaeed, Yip, Michael, Yoo, Hwanseung, Yousefirizi, Fereshteh, Yu, Shunkai, Yu, Lei, Zamora, Jonathan, Zeineldin, Ramy Ashraf, Zeng, Dewen, Zhang, Jianpeng, Zhang, Bokai, Zhang, Jiapeng, Zhang, Fan, Zhang, Huahong, Zhao, Zhongchen, Zhao, Zixuan, Zhao, Jiachen, Zhao, Can, Zheng, Qingshuo, Zhi, Yuheng, Zhou, Ziqi, Zou, Baosheng, Maier-Hein, Klaus, Jäger, Paul F., Kopp-Schneider, Annette, and Maier-Hein, Lena
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.
- Published
- 2022
4. Learning-Based Keypoint Registration for Fetoscopic Mosaicking
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Casella, Alessandro, Bano, Sophia, Vasconcelos, Francisco, David, Anna L., Paladini, Dario, Deprest, Jan, De Momi, Elena, Mattos, Leonardo S., Moccia, Sara, and Stoyanov, Danail
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
In Twin-to-Twin Transfusion Syndrome (TTTS), abnormal vascular anastomoses in the monochorionic placenta can produce uneven blood flow between the two fetuses. In the current practice, TTTS is treated surgically by closing abnormal anastomoses using laser ablation. This surgery is minimally invasive and relies on fetoscopy. Limited field of view makes anastomosis identification a challenging task for the surgeon. To tackle this challenge, we propose a learning-based framework for in-vivo fetoscopy frame registration for field-of-view expansion. The novelties of this framework relies on a learning-based keypoint proposal network and an encoding strategy to filter (i) irrelevant keypoints based on fetoscopic image segmentation and (ii) inconsistent homographies. We validate of our framework on a dataset of 6 intraoperative sequences from 6 TTTS surgeries from 6 different women against the most recent state of the art algorithm, which relies on the segmentation of placenta vessels. The proposed framework achieves higher performance compared to the state of the art, paving the way for robust mosaicking to provide surgeons with context awareness during TTTS surgery.
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- 2022
5. Toward a navigation framework for fetoscopy
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Casella, Alessandro, Lena, Chiara, Moccia, Sara, Paladini, Dario, De Momi, Elena, and Mattos, Leonardo S.
- Published
- 2023
- Full Text
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6. Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings
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Bano, Sophia, Casella, Alessandro, Vasconcelos, Francisco, Qayyum, Abdul, Benzinou, Abdesslam, Mazher, Moona, Meriaudeau, Fabrice, Lena, Chiara, Cintorrino, Ilaria Anita, De Paolis, Gaia Romana, Biagioli, Jessica, Grechishnikova, Daria, Jiao, Jing, Bai, Bizhe, Qiao, Yanyan, Bhattarai, Binod, Gaire, Rebati Raman, Subedi, Ronast, Vazquez, Eduard, Płotka, Szymon, Lisowska, Aneta, Sitek, Arkadiusz, Attilakos, George, Wimalasundera, Ruwan, David, Anna L, Paladini, Dario, Deprest, Jan, De Momi, Elena, Mattos, Leonardo S, Moccia, Sara, and Stoyanov, Danail
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to regulate blood exchange among twins. The procedure is particularly challenging due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation. Computer-assisted intervention (CAI) can provide surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision challenge, we released the first largescale multicentre TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper, we present the findings of the FetReg2021 challenge alongside reporting a detailed literature review for CAI in TTTS fetoscopy. Through this challenge, its analysis and the release of multi-centre fetoscopic data, we provide a benchmark for future research in this field., Comment: Accepted at MedIA (Medical Image Analysis)
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- 2022
7. FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset
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Bano, Sophia, Casella, Alessandro, Vasconcelos, Francisco, Moccia, Sara, Attilakos, George, Wimalasundera, Ruwan, David, Anna L., Paladini, Dario, Deprest, Jan, De Momi, Elena, Mattos, Leonardo S., and Stoyanov, Danail
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Fetoscopy laser photocoagulation is a widely used procedure for the treatment of Twin-to-Twin Transfusion Syndrome (TTTS), that occur in mono-chorionic multiple pregnancies due to placental vascular anastomoses. This procedure is particularly challenging due to limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to fluid turbidity, variability in light source, and unusual position of the placenta. This may lead to increased procedural time and incomplete ablation, resulting in persistent TTTS. Computer-assisted intervention may help overcome these challenges by expanding the fetoscopic field of view through video mosaicking and providing better visualization of the vessel network. However, the research and development in this domain remain limited due to unavailability of high-quality data to encode the intra- and inter-procedure variability. Through the \textit{Fetoscopic Placental Vessel Segmentation and Registration (FetReg)} challenge, we present a large-scale multi-centre dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms for the fetal environment with a focus on creating drift-free mosaics from long duration fetoscopy videos. In this paper, we provide an overview of the FetReg dataset, challenge tasks, evaluation metrics and baseline methods for both segmentation and registration. Baseline methods results on the FetReg dataset shows that our dataset poses interesting challenges, offering large opportunity for the creation of novel methods and models through a community effort initiative guided by the FetReg challenge.
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- 2021
8. Placental vessel segmentation and registration in fetoscopy: Literature review and MICCAI FetReg2021 challenge findings
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Bano, Sophia, Casella, Alessandro, Vasconcelos, Francisco, Qayyum, Abdul, Benzinou, Abdesslam, Mazher, Moona, Meriaudeau, Fabrice, Lena, Chiara, Cintorrino, Ilaria Anita, De Paolis, Gaia Romana, Biagioli, Jessica, Grechishnikova, Daria, Jiao, Jing, Bai, Bizhe, Qiao, Yanyan, Bhattarai, Binod, Gaire, Rebati Raman, Subedi, Ronast, Vazquez, Eduard, Płotka, Szymon, Lisowska, Aneta, Sitek, Arkadiusz, Attilakos, George, Wimalasundera, Ruwan, David, Anna L., Paladini, Dario, Deprest, Jan, De Momi, Elena, Mattos, Leonardo S., Moccia, Sara, and Stoyanov, Danail
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- 2024
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9. Deep-Learning Architectures for Placenta Vessel Segmentation in TTTS Fetoscopic Images
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Casella, Alessandro, Moccia, Sara, Cintorrino, Ilaria Anita, De Paolis, Gaia Romana, Bicelli, Alexa, Paladini, Dario, De Momi, Elena, Mattos, Leonardo S., 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, Mazzeo, Pier Luigi, editor, Frontoni, Emanuele, editor, Sclaroff, Stan, editor, and Distante, Cosimo, editor
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- 2022
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10. Learning-based keypoint registration for fetoscopic mosaicking
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Casella, Alessandro, primary, Bano, Sophia, additional, Vasconcelos, Francisco, additional, David, Anna L., additional, Paladini, Dario, additional, Deprest, Jan, additional, De Momi, Elena, additional, Mattos, Leonardo S., additional, Moccia, Sara, additional, and Stoyanov, Danail, additional
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- 2023
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11. Placental vessel segmentation and registration in fetoscopy: Literature review and MICCAI FetReg2021 challenge findings
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Bano, Sophia, primary, Casella, Alessandro, additional, Vasconcelos, Francisco, additional, Qayyum, Abdul, additional, Benzinou, Abdesslam, additional, Mazher, Moona, additional, Meriaudeau, Fabrice, additional, Lena, Chiara, additional, Cintorrino, Ilaria Anita, additional, De Paolis, Gaia Romana, additional, Biagioli, Jessica, additional, Grechishnikova, Daria, additional, Jiao, Jing, additional, Bai, Bizhe, additional, Qiao, Yanyan, additional, Bhattarai, Binod, additional, Gaire, Rebati Raman, additional, Subedi, Ronast, additional, Vazquez, Eduard, additional, Płotka, Szymon, additional, Lisowska, Aneta, additional, Sitek, Arkadiusz, additional, Attilakos, George, additional, Wimalasundera, Ruwan, additional, David, Anna L., additional, Paladini, Dario, additional, Deprest, Jan, additional, De Momi, Elena, additional, Mattos, Leonardo S., additional, Moccia, Sara, additional, and Stoyanov, Danail, additional
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- 2023
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12. Reducing Workload During Brain Surgery with Robot-Assisted Autonomous Exoscope
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Iovene, Elisa, primary, Casella, Alessandro, additional, Fu, Junling, additional, Pessina, Federico, additional, Riva, Marco, additional, Ferrigno, Giancarlo, additional, and De Momi, Elena, additional
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- 2023
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13. Inter-foetus Membrane Segmentation for TTTS Using Adversarial Networks
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Casella, Alessandro, Moccia, Sara, Frontoni, Emanuele, Paladini, Dario, De Momi, Elena, and Mattos, Leonardo S.
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- 2020
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14. Classifying Vocal Folds Fixation from Endoscopic Videos with Machine Learning
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Villani, Francesca Pia, primary, Paderno, Alberto, additional, Fiorentino, Maria Chiara, additional, Casella, Alessandro, additional, Piazza, Cesare, additional, and Moccia, Sara, additional
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- 2023
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15. Towards Exoscope Automation in Neurosurgery: A Markerless Visual-Servoing Approach
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Iovene, Elisa, primary, Casella, Alessandro, additional, Iordache, Alice Valeria, additional, Fu, Junling, additional, Pessina, Federico, additional, Riva, Marco, additional, Ferrigno, Giancarlo, additional, and Momi, Elena De, additional
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- 2023
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16. Biomedical image analysis competitions:The state of current participation practice
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Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Godau, Patrick, Cheplygina, Veronika, Kozubek, Michal, Ali, Sharib, Gupta, Anubha, Kybic, Jan, Noble, Alison, Solórzano, Carlos Ortiz de, Pachade, Samiksha, Petitjean, Caroline, Sage, Daniel, Wei, Donglai, Wilden, Elizabeth, Alapatt, Deepak, Andrearczyk, Vincent, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bawa, Vivek Singh, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Choi, Jinwook, Commowick, Olivier, Daum, Marie, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Eichhorn, Hannah, Engelhardt, Sandy, Ganz, Melanie, Girard, Gabriel, Hansen, Lasse, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Kim, Hyunjeong, Thambawita, Vajira, Zhao, Xin, Lund, Christina B., Ren, Jintao, and Yang, Lin
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cs.LG ,cs.CV - Abstract
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an internationalsurvey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants’ expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive(70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing step The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.
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- 2022
17. Envisioning Robotic Exoscope: Concept and Preliminary Results
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Iordache, ALICE VALERIA, Casella, Alessandro, Iovene, Elisa, Fu, Junling, Federico, Pessina, Marco, Riva, Ferrigno, Giancarlo, Mattos, Leonardo S., and DE MOMI, Elena
- Published
- 2022
18. A shape-constraint adversarial framework with instance-normalized spatio-temporal features for inter-fetal membrane segmentation
- Author
-
Casella, Alessandro, primary, Moccia, Sara, additional, Paladini, Dario, additional, Frontoni, Emanuele, additional, De Momi, Elena, additional, and Mattos, Leonard S., additional
- Published
- 2021
- Full Text
- View/download PDF
19. NephCNN: A deep-learning framework for vessel segmentation in nephrectomy laparoscopic videos
- Author
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Casella, Alessandro, primary, Moccia, Sara, additional, Carlini, Chiara, additional, Frontoni, Emanuele, additional, De Momi, Elena, additional, and Mattos, Leonardo S., additional
- Published
- 2021
- Full Text
- View/download PDF
20. ENHANCED MONOCULAR DEPTH ESTIMATION FROM MOTION IN COLONOSCOPY.
- Author
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Lena, Chiara, Milesi, Davide, Casella, Alessandro, Norton, Joseph, Martin, James, Scaglioni, Bruno, Obstein, Keith, Valdastri, Pietro, and De Momi, Elena
- Published
- 2024
- Full Text
- View/download PDF
21. Inter-foetus Membrane Segmentation for TTTS Using Adversarial Networks
- Author
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Casella, Alessandro, primary, Moccia, Sara, additional, Frontoni, Emanuele, additional, Paladini, Dario, additional, De Momi, Elena, additional, and Mattos, Leonardo S., additional
- Published
- 2019
- Full Text
- View/download PDF
22. MAO ET LA REVOLUTION CULTURELLE
- Author
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Casella, Alessandro
- Published
- 1977
23. L'opera degli stuccatori comaschi e ticinesi
- Author
-
Casella, Alessandro, Riva, Galeazzo, Silva, Francesco, and Silva, Agostino
- Published
- 1989
24. U.S.-Thai Relations
- Author
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Casella, Alessandro
- Published
- 1970
25. Communism and Insurrection in Thailand
- Author
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Casella, Alessandro
- Published
- 1970
26. The effects of match-playing aspects and situational variables on achieving score-box possessions in Euro 2012 Football Championship
- Author
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Sgrò, Francesco, primary, Aiello, Fabio, additional, Casella, Alessandro, additional, and Lipoma, Mario, additional
- Published
- 2017
- Full Text
- View/download PDF
27. Offensive strategies in the European Football Championship 2012
- Author
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Sgrò, Francesco, primary, Aiello, Fabio, additional, Casella, Alessandro, additional, and Lipoma, Mario, additional
- Published
- 2016
- Full Text
- View/download PDF
28. With Sihanouk in Peking.
- Author
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Casella, Alessandro
- Subjects
PRINCES ,VIETNAMESE people ,REVOLUTIONARIES - Abstract
The article presents an interview with Chinese Prince Sihanouk, which took place early this winter. The two-hour conversation took place at Sihanouk's villa, situated in a large compound near Fisherman's Court in Peking, China. Answering a question on whether he visited Moscow, Soviet Union, to obtain assurances from Vietnamese revolutionaries, Sihanouk said that Vietnamese are extremely independent people. North Vietnam is independent both from Soviet Union and from China. If one wishes to insure that the Vietnamese respect Cambodia's borders, it is not with Moscow or with Peking that one should negotiate but with Hanoi, Vietnam.
- Published
- 1971
29. Sketch-based multidimensional IDS: A new approach for network anomaly detection
- Author
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Callegari, Christian, primary, Casella, Alessandro, additional, Giordano, Stefano, additional, Pagano, Michele, additional, and Pepe, Teresa, additional
- Published
- 2013
- Full Text
- View/download PDF
30. LETTERS.
- Author
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TICKTIN, HAROLD, RAVITCH, NORMAN, GLASSER, PAUL, GORNICK, VIVIAN, DELGADILLO, ELAN, CHAPMAN, GORDON A., EL SAHLY, HANA, CASELLA, ALESSANDRO, and GERTZMAN, JAY A.
- Subjects
LETTERS to the editor - Abstract
Several letters to the editor are presented in response to articles published in previous issues, including "Before the Law," by Vivian Gornick, in the March 5, 2007 issue, and "Made in USA," by Perry Anderson, in the April 2, 2007 issue.
- Published
- 2007
31. The militant mood
- Author
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Casella, Alessandro
- Published
- 1968
- Full Text
- View/download PDF
32. China más cerca
- Author
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Casella, Alessandro
- Subjects
Sociedad ,China ,Política y gobierno - Published
- 1971
33. A visit to inner Mongolia
- Author
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Casella, Alessandro, primary
- Published
- 1968
- Full Text
- View/download PDF
34. Refuge.
- Author
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Casella, Alessandro
- Published
- 2017
35. Classifying Vocal Folds Fixation from Endoscopic Videos with Machine Learning.
- Author
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Villani FP, Paderno A, Fiorentino MC, Casella A, Piazza C, and Moccia S
- Subjects
- Humans, Glottis, Videotape Recording, Machine Learning, Vocal Cords diagnostic imaging, Endoscopy
- Abstract
Vocal folds motility evaluation is paramount in both the assessment of functional deficits and in the accurate staging of neoplastic disease of the glottis. Diagnostic endoscopy, and in particular videoendoscopy, is nowadays the method through which the motility is estimated. The clinical diagnosis, however, relies on the examination of the videoendoscopic frames, which is a subjective and professional-dependent task. Hence, a more rigorous, objective, reliable, and repeatable method is needed. To support clinicians, this paper proposes a machine learning (ML) approach for vocal cords motility classification. From the endoscopic videos of 186 patients with both vocal cords preserved motility and fixation, a dataset of 558 images relative to the two classes was extracted. Successively, a number of features was retrieved from the images and used to train and test four well-grounded ML classifiers. From test results, the best performance was achieved using XGBoost, with precision = 0.82, recall = 0.82, F1 score = 0.82, and accuracy = 0.82. After comparing the most relevant ML models, we believe that this approach could provide precise and reliable support to clinical evaluation.Clinical Relevance- This research represents an important advancement in the state-of-the-art of computer-assisted otolaryngology, to develop an effective tool for motility assessment in the clinical practice.
- Published
- 2023
- Full Text
- View/download PDF
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