{"id":1623,"date":"2012-11-08T11:36:00","date_gmt":"2012-11-08T11:36:00","guid":{"rendered":"https:\/\/blogs.msdn.microsoft.com\/msr_er\/2012\/11\/08\/improving-diagnostics-for-brain-tumors\/"},"modified":"2016-07-20T07:32:24","modified_gmt":"2016-07-20T14:32:24","slug":"improving-diagnostics-for-brain-tumors","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/improving-diagnostics-for-brain-tumors\/","title":{"rendered":"Improving Diagnostics for Brain Tumors"},"content":{"rendered":"

As computer scientists, we have the privilege of working on challenging problems, the solutions to which can markedly improve lives—and in some cases, even save them. It is just such a challenge that Senior Researcher Antonio Criminisi (opens in new tab)<\/span><\/a> and his team at Microsoft Research Cambridge have undertaken, as they strive to develop software to help physicians more accurately and rapidly identify the anatomy of aggressive brain tumors, a feat that will enable better-targeted therapy.<\/span><\/p>\n

\"Brain-scan
Brain-scan images depicting tumors<\/span><\/span><\/p>\n

As described in the feature article, “Coming to the Aid of Brain-Tumor Patients (opens in new tab)<\/span><\/a>,” Antonio and his colleagues are using decision forests (opens in new tab)<\/span><\/a>, an innovation in machine learning, to speed up and potentially fully automate the now time-consuming process of creating a 3-D image of brain tumors. This work has the potential to drastically reduce the amount of time a highly-trained radiotherapist needs to spend processing medical images, saving time and money in clinical care and, most importantly, getting patients into the most appropriate therapy at the soonest possible moment. Moreover, this technology—which, by the way, enables the Kinect sensor to identify players in Xbox video games—could be applied to many other challenges in medical image analysis (opens in new tab)<\/span><\/a>.<\/span><\/p>\n

To support this broader exploration, Microsoft Research Connections is establishing a medical imaging initiative, designed to compile a large, well-annotated, and sharable collection of medical images for the purpose of comparing and improving the algorithms that analyze them. Scientific advances often rely on such comparisons of different experimental approaches, which enable us to determine which is the most effective. Based on our results, over the coming year, we plan to begin providing the tools needed to accelerate innovation in the field of medical imaging. I will use this blog to provide further details of this initiative as it unfolds throughout the approaching months.<\/span><\/p>\n

—Simon Mercer (opens in new tab)<\/span><\/a>, Director of Health and Wellbeing, Microsoft Research Connections<\/em><\/span><\/p>\n

Learn More<\/strong><\/span><\/p>\n