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

arXiv.org Artificial Intelligence

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 criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that undergone 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.


AI can detect COVID-19 in the lungs like a virtual physician, new study shows

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The new UCF co-developed algorithm can accurately identify COVID-19 cases, as well as distinguish them from influenza. ORLANDO, Sept. 30, 2020 - A University of Central Florida researcher is part of a new study showing that artificial intelligence can be nearly as accurate as a physician in diagnosing COVID-19 in the lungs. The study, recently published in Nature Communications, shows the new technique can also overcome some of the challenges of current testing. Researchers demonstrated that an AI algorithm could be trained to classify COVID-19 pneumonia in computed tomography (CT) scans with up to 90 percent accuracy, as well as correctly identify positive cases 84 percent of the time and negative cases 93 percent of the time. CT scans offer a deeper insight into COVID-19 diagnosis and progression as compared to the often-used reverse transcription-polymerase chain reaction, or RT-PCR, tests.


UCSF Launches Center for Intelligent Imaging to Accelerate AI Adoption in Radiology -

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UC San Francisco (UCSF) announced the launch of the Center for Intelligence or ci2 that focuses on accelerating the adoption of artificial intelligence (AI) technology to radiology, leveraging advanced computational techniques and industry collaborations to improve patient diagnoses and care. As part of the center, Investigators in ci2 will collaborate with NVIDIA Corp to build infrastructure and tools focused on enabling the translation of AI into clinical practice. The Center is comprised of clinical radiologists, imaging scientists, engineers, machine learning scientists, data engineers, clinicians, post-doctoral fellows, and students collaborating to develop and deploy artificial intelligence that will solve critical clinical problems by advancing the way in which healthcare professionals are able to utilize and deliver imaging. Tools under development include organ and tissue segmentation, automated volumetry and morphological quantification, and disease visualization. NVIDIA engineers and data scientists will work alongside UCSF investigators to develop clinical AI tools, applying powerful computational resources that are available in a few medical institutions, with the goal of accelerating the AI development cycle and integrating it seamlessly in the clinic.


Artificial Intelligence in Radiology: The Computer's Helping Hand Needs Guidance

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See also the article by Tadavarthi et al in this issue. Evis Sala, MD, PhD, is the professor of oncological imaging at the University of Cambridge, UK and co-leads the Advanced Cancer Imaging Programme and the Integrative Cancer Medicine Programme for the Cancer Research UK Cambridge Centre. Her current research focuses on radiogenomics through multiomics data integration for evaluation of spatial and temporal tumor heterogeneity and on the applications of AI methods for image reconstruction, segmentation, and data integration. Stephan Ursprung, MD, is a 3rd-year PhD student in the department of radiology at the University of Cambridge, UK. His research focuses on the development of AI models for automated segmentation, lesion classification, and treatment response prediction in renal cancer. Dr Ursprung's interests include health information technology, molecular and physiologic imaging, as well as multiomics data integration.


Postdoctoral Position - Quantitative Magnetic Resonance Imaging, Medical Imaging, Sweden 2022

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Eligible to be employed as a postdoctoral fellow is a person who has completed a doctoral degree, or a foreign degree that is considered comparable to a doctoral degree, in medical physics, physics, statistics, computer science, mathematics or a similar subject. This eligibility requirement must be met at the latest at the time when the employment decision is made. A very good command of written and spoken English is required. Previous experience of image processing and programming is also required. Since employment as a postdoctoral fellow constitutes a merit-based employment for junior researchers, we primarily target those who have a doctoral degree that is not older than three years from the last application date.