The top 10 results have been unveiled in the first-of-its-kind COVID-19 Lung CT Lesion Segmentation Grand Challenge, a groundbreaking research competition focused on developing artificial intelligence (AI) models to help in the visualization and measurement of COVID specific lesions in the lungs of infected patients, potentially facilitating more timely and patient-specific medical interventions.
Attracting more than 1,000 global participants, the competition was presented by the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National Hospital in collaboration with leading AI technology company NVIDIA and the National Institutes of Health (NIH). The competition’s AI models utilized a multi-institutional, multi-national data set provided by public datasets from The Cancer Imaging Archive (National Cancer Institute), NIH and the University of Arkansas, that originated from patients of different ages, genders and with variable disease severity. NVIDIA provided GPUs to the top five winners as prizes, as well as supported the selection and judging process.
“Improving COVID-19 treatment starts with a clearer understanding of the patient’s disease state. However, a prior lack of global data collaboration limited clinicians in their ability to quickly and effectively understand disease severity across both adult and pediatric patients,” says Marius George Linguraru, D.Phil., M.A., M.Sc., principal investigator at the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National, who led the Grand Challenge initiative. “By harnessing the power of AI through quantitative imaging and machine learning, these discoveries are helping clinicians better understand COVID-19 disease severity and potentially stratify and triage into appropriate treatment protocols at different stages of the disease.”
The top 10 AI algorithms were identified from a highly competitive field of participants who tested the data in November and December 2020. The results were unveiled on Jan. 11, 2021, in a virtual symposium, hosted by Children’s National, that featured presentations from top teams, event organizers and clinicians.
Developers of the 10 top AI models from the COVID-19 Lung CT Lesion Segmentation Grand Challenge are:
- Shishuai Hu, et al. Northwestern Polytechnical University, China. “Semi-supervised Method for COVID-19 Lung CT Lesion Segmentation”
- Fabian Isensee, et al. German Cancer Research Center, Germany. “nnU-Net for Covid Segmentation”
- Claire Tang, Lynbrook High School, USA. “Automated Ensemble Modeling for COVID-19 CT Lesion Segmentation”
- Qinji Yu, et al. Shanghai JiaoTong University, China. “COVID-19-20 Lesion Segmentation Based on nnUNet”
- Andreas Husch, et al. University of Luxembourg, Luxembourg. “Leveraging State-of-the-Art Architectures by Enriching Training Information – a case study”
- Tong Zheng, et al. Nagoya University, Japan. “Fully-automated COVID-19-20 Segmentation”
- Vitali Liauchuk. United Institute of Informatics Problems (UIIP), Belarus. “Semi-3D CNN with ImageNet Pretrain for Segmentation of COVID Lesions on CT”
- Ziqi Zhou, et al. Shenzhen University, China. “Automated Chest CT Image Segmentation of COVID-19 with 3D Unet-based Framework”
- Jan Hendrik Moltz, et al. Fraunhofer Institute for Digital Medicine MEVIS, Germany. “Segmentation of COVID-19 Lung Lesions in CT Using nnU-Net”
- Bruno Oliveira, et al. 2Ai – Polytechnic Institute of Cávado and Ave, Portugal. “Automatic COVID-19 Detection and Segmentation from Lung Computed Tomography (CT) Images Using 3D Cascade U-net”
Linguraru added that, in addition to an award for the top five AI models, these winning algorithms are now available to partner with clinical institutions across the globe to further evaluate how these quantitative imaging and machine learning methods may potentially impact global public health.
“Quality annotations are a limiting factor in the development of useful AI models,” said Mona Flores, M.D., global head of Medical AI, NVIDIA. “Using the NVIDIA COVID lesion segmentation model available on our NGC software hub, we were able to quickly label the NIH dataset, allowing radiologists to do precise annotations in record time.”
“I applaud the computer science, data science and image processing global academic community for rapidly teaming up to combine multi-disciplinary expertise towards development of potential automated and multi-parametric tools to better study and address the myriad of unmet clinical needs created by the pandemic,” said Bradford Wood, M.D., director, NIH Center for Interventional Oncology and chief, Interventional Radiology Section, NIH Clinical Center. “Thank you to each team for locking arms towards a common cause that unites the scientific community in these challenging times.”