{"id":47527,"date":"2019-11-19T09:32:47","date_gmt":"2019-11-19T14:32:47","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ece\/?p=47527"},"modified":"2021-07-01T10:22:20","modified_gmt":"2021-07-01T14:22:20","slug":"yufan-he-wins-prestigious-best-young-scientist-award-at-miccai-2019","status":"publish","type":"news","link":"https:\/\/engineering.jhu.edu\/ece\/news\/yufan-he-wins-prestigious-best-young-scientist-award-at-miccai-2019\/","title":{"rendered":"Yufan He wins prestigious Best Young Scientist Award at MICCAI 2019"},"content":{"rendered":"

When Yufan He entered the Best Young Scientist Award<\/a> competition at MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention<\/a>, he knew the odds of winning were slim.<\/p>\n

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Yufan He on stage after being announced as a MICCAI Society Young Scientist Award winner<\/p><\/div>\n

More than 500 papers were competing for the coveted award, which recognizes the highest quality papers that are first-authored by young scientists at the conference, which took place in mid-October in Shenzhen, China.<\/p>\n

So, when He, a doctoral candidate in the Department of Electrical and Computer Engineering<\/a>, learned that his paper\u2014\u201cFully convolutional boundary regression for retina OCT segmentation\u201d\u2014was one of five winners, he was thrilled. That victory was even sweeter as He and Zongwei Zhou of Arizona State University were the only winners representing American institutions.<\/p>\n

\u201cIt was really unexpected and exciting,\u201d He said. \u201cI didn’t expect that my work and presentation would be that highly recognized by the award committee. To me, winning this award means that I have a very good understanding of a problem and have proposed a novel method to solve it.\u201d<\/p>\n

He\u2019s work focuses on Optical Coherent Tomography (OCT) image segmentation, which is an imaging method used to take cross-section pictures of the eye\u2019s retina. Despite being a well-explored area, the practice has great limitations. To overcome these, He developed an improved method by using deep learning in a novel way.<\/p>\n

\u201cTo quantitatively analyze medical images, for example, for acquiring retinal layer thicknesses, we need to segment each retinal layer from the image,\u201d He said. \u201cAfter segmentation, we can obtain statistics about the changes of each retinal layer and better understand the disease or the treatment.\u201d<\/p>\n

He\u2019s method could have a major impact in the diagnosis of multiple sclerosis (MS). If a patient takes an OCT scan of their retina, his algorithm can produce quantitative results of their retinal thickness or lesion volume within a minute after finishing the scanning. From there, the clinicians can take the quantitative results and compare with the longitudinal data to better diagnose or monitor the disease.<\/p>\n

For big data analysis, like segment tens of thousands images in the database, a fast segmentation algorithm is needed. As a result, He\u2019s system has been used to analyze thousands of scans from people with MS in a short time.<\/p>\n

\u201cOur group has been working on OCT image analysis for people with MS for many years and I just continued working on this topic,\u201d He said. \u201cThe deep learning method is an emerging technique that has many benefits in image analysis but it has disadvantages in giving structured output. I’m interested in overcoming the disadvantages of this method while maintaining its benefits for OCT image analysis.\u201d<\/p>\n

He chose to earn his doctorate at Johns Hopkins in order to work with Jerry Prince<\/a>, the William B. Kouwenhoven Professor in electrical and computer engineering, who is also the director of the Image Analysis and Communications Lab<\/a>. The advisor-advisee relationship has blossomed into a fantastic pairing, He says, and he is excited to see where it takes him.<\/p>\n

\u201cI love the research environment at Johns Hopkins,\u201d He said. \u201cDr. Prince and Johns Hopkins encourage us to think both hard and independently. They\u2019ve provided a great research environment for me to think deeply and work on something novel.\u201d<\/p>\n

Click the video below to watch He accept his award.<\/p>\n