{"id":949,"date":"2012-07-15T16:23:52","date_gmt":"2012-07-15T20:23:52","guid":{"rendered":"https:\/\/engineering.jhu.edu\/magazine-archive\/?p=949"},"modified":"2017-07-28T16:28:07","modified_gmt":"2017-07-28T20:28:07","slug":"new-language-anatomy","status":"publish","type":"post","link":"https:\/\/engineering.jhu.edu\/magazine-archive\/2012\/07\/new-language-anatomy\/","title":{"rendered":"The New Language of Anatomy"},"content":{"rendered":"
\"miller\"<\/a>
Michael I. Miller, Hershel and Ruth Seder Professor of Biomedical Engineering and director of the Whiting School’s Center for Imaging Science. Photo Credit: Peter Howard<\/figcaption><\/figure>\n

Back in 2003, a pair of economists estimated that roughly 2 billion medical images were being created annually by hospitals worldwide. In the nine years since then, that pace may have doubled. The vast majority of those billions of images are glanced at briefly by a radiologist and then placed in the patient’s records, never to be studied again.<\/p>\n

But what if those images could be fed into computer systems that would slowly be trained in the language of human anatomy, much as the software that underlies Apple’s Siri system has slowly learned the rules of human language by processing millions of spoken utterances? If computers studied the lexicon of the body-if they learned the thousands of tiny anatomical signs that mark various diseases across thousands of individuals-they could offer revolutionary new tools for early diagnosis and treatment.<\/p>\n

That, at least, is the dream of Michael I. Miller, MS ’79, PhD ’84<\/a>, the Hershel and Ruth Seder Professor of Biomedical Engineering and director of the Whiting School’s Center for Imaging Science<\/a>. In the late 1990s, Miller had an insight that has transformed the mathematical analysis of human anatomy. Fifteen years later, that insight is beginning to bear fruit. With the help of physicians and other biomedical scientists from throughout Hopkins, Miller and his engineering colleagues have built computing systems that are studying small-scale anatomical features associated with heart failure, Alzheimer’s disease, schizophrenia, and a long list of other conditions.<\/p>\n

https:\/\/youtu.be\/_VOtir1G1RY
\nWhat does the work of painter Pablo Picasso and linguist Noam Chomsky share in common with Miller’s work? Watch this video<\/strong> to find out.<\/p><\/blockquote>\n

At age 57, Miller is an imposingly tall man with warm eyes. He first came to Hopkins as a graduate student in 1976, and he has spent the bulk of his adult life here. (From 1983 to 1998, he worked as a researcher and faculty member at Washington University in St. Louis.) As he sits on a bench in front of Clark Hall, where his center is housed, he gestures at the quad and recalls how small the Hopkins biomedical engineering program (then one of the few such programs in the world) was when he arrived during the Ford administration.<\/p>\n

“I believe there were only four doctoral students who started with me,” he says. “Today there are 20 or 30 every year. Back then, there was no undergraduate program in biomedical engineering. Now we have 400 undergraduates, and I think it’s one of the healthiest undergraduate majors in all of Hopkins.” But ask Miller if he foresaw any of this spectacular growth back in 1976, and he says no: He’s as surprised as anyone by the field’s explosion. In any case, he says, that’s not the kind of topic that he tends to dwell on. As he tells it, he’s been extremely fortunate to find and solve one interesting intellectual problem after another, and he’s spent 36 years keeping his head down, looking for new solutions.<\/p>\n

<\/p>\n

For the last two decades, the problem that has obsessed Miller is how to mathematically analyze the three-dimensional space of human anatomy. Much as Noam Chomsky and his colleagues at MIT sketched a universal framework for human grammar, Miller would like to create the simplest possible equations and statistical models that can describe human anatomy in all its variety and multitude. That quest has led him into collaborations with a huge range of scholars, including theoretical mathematicians, imaging engineers, radiologists, cardiologists, and neuroscientists.<\/p>\n

At this early stage of his center’s development, Miller’s computers generally can’t tell doctors anything that they don’t already know. Enlargement of the left ventricle is associated with heart failure? Old news. A thinning of the brain’s white-matter tracts is a sign of dementia? Been there, done that. But after his team has digested many thousands of images, Miller hopes that the computers will begin to discover subtle anatomical markers and patterns that had previously gone unnoticed by scientists. And that, in turn, could lead to much earlier diagnostic tests for certain diseases-and even point the way toward new treatments.<\/p>\n

In the most tantalizing result so far, Miller and his colleagues demonstrated last year a computational technique that can analyze magnetic resonance images of the brain to distinguish between older adults who will soon suffer Alzheimer’s dementia and those who are simply experiencing the normal memory loss that comes with aging.<\/p>\n

At the very least, Miller says, techniques like that one have the potential to streamline radiologists’ work. “One of the things that we’d like to build is a machine that can see evidence of something and cue the radiologist, so that the radiologist can more efficiently review the images. We can save them time in their workflow.”<\/p>\n

Miller’s ability to shift easily from complex mathematical topics to the practical problems of radiologists might help to explain his success in luring collaborators from across Hopkins into his projects. “Mike’s style is to encourage us and to stimulate us about what we can do together,” says Susumu Mori, a professor of radiology at the School of Medicine who is working with Miller on an analysis of brain scans of young patients. “He’s not the kind of colleague who says, \u2018Just give me the data and I’ll do everything.’ He’s not like that at all.”<\/p>\n

On this particular project, Miller, Mori, and their colleagues hope to identify physiological changes over time in the brain structures of young patients who are being treated for psychiatric illness. In and of itself, this is not revolutionary-many teams of scholars across the country are studying physical correlates of schizophrenia, for example. But Miller and Mori hope that their computational-imaging system will spot subtle changes that may not have been noticed by others, and that it will build a cumulative body of knowledge about schizophrenia and the growing brain.<\/p>\n

“In clinical image-based analysis, the result is free text,” Mori says. That is, the radiologist simply writes a narrative description of what the image shows. “We’d like to supplement that with some quantitative measures,” Mori continues, “similar to what you see with a complete blood cell analysis.”<\/p>\n

Miller’s graduate work at Hopkins in the 1970s had nothing directly to do with computational anatomy. He fell into the lab of Murray B. Sachs<\/a> and Eric D. Young<\/a>, who were working on pioneering studies of how the auditory nerve processes sounds. (Both Sachs and Young remain on the biomedical engineering faculty today.)<\/p>\n

As a doctoral student, Miller helped to identify the “neural spike train”-that is, the series of electrical action potentials-generated by the auditory nerve as it conducts spoken language from the ear to the brain. “We were measuring the signals in the auditory nerve and rebuilding the code that was equivalent in information to the information that’s in these complex acoustic sounds-consonants and plosives and vowels,” Miller says. His doctoral work helped to verify a theoretical model of auditory processing that had been developed by linguists and cognitive scientists at MIT-and it also eventually helped to improve the technology of cochlear implants.<\/p>\n

After completing his degree, Miller moved to Washington University to work as a postdoctoral researcher with Donald Snyder, a professor of electrical engineering who pioneered the use of the statistical technique known as “point processes” in analyzing neurons’ behavior. Miller had drawn on Snyder’s technique in his doctoral study, and wanted to work with the man himself. At Wash U, Miller was drawn into a network of scholars who were refining the technology of positron emission tomography (PET), which creates three-dimensional images of biological processes. “It was a big team,” says Snyder, a professor of electrical engineering at Wash U who often collaborated with Miller. “There were computer people, there were people in radiation physics who were developing the hardware. And then there were those of us who wanted to help analyze the images and understand their significance.<\/p>\n

<\/p>\n

That was how Miller found his way into computational anatomy. But he found that the field was beset by technical challenges. Some of those had to do with the limitations of that era’s computers; for some of his early projects, Miller had to plead and negotiate for the use of colleagues’ large-scale parallel processors. But some of the problems had to do with the conceptual difficulty of mathematically mapping a space as dense and variable as the human body.<\/p>\n

“In map making, you’re building relations between labeled structures in two different places,” Miller says. “The GPS in your phone is constantly computing a map. It knows where you are relative to world coordinates, so it can tell you where you are. In computational anatomy, that’s fundamentally the technology we use: building a global positioning system.”<\/p>\n

But anatomical mapping carries two profound challenges that GPS engineers have been spared. First, human anatomy has no single set of “world coordinates.” Instead of mapping a single planet, computational anatomists must build models that can effectively deal with the diversity of 6 billion living human bodies. Second, to a much greater degree than GPS mapping, anatomical mapping must be concerned with three-dimensional structures.<\/p>\n

“In lots of map making that we’re familiar with, like Google Maps, the correspondence that they build is very simple,” Miller says. “In the global positioning system that Google uses, you only need to understand the position where you are in space, so that’s three dimensions, and maybe also the way you’re oriented, which is another three dimensions. And then there’s a seventh dimension, which is scale: You can look at smaller or larger fractions of the Google Map atlas.”<\/p>\n

Until the late 1990s, most work in computational anatomy used a similar seven-dimensional system. But then Miller had an insight that has profoundly changed the field’s methods. In order to create stylized analyses of diverse human bodies, he realized, scholars should switch to infinite-dimensional mapping, called diffeomorphisms, a special kind of structure-preserving mapping.<\/p>\n

“Imagine that you take this object here”-he picks up a watch-“and you took this object here”-he picks up a sheet of paper-“and you said, well, I’m going to understand this map by just using rotation and translation. But these two objects are not necessarily connected. If I rotate one of them, the other might stay in the same position. So you need to attach some number of dimensions to every structure in the brain, or whatever other area you’re mapping. At every place on the continuum, you’d like to be able to deform it smoothly-you know, rotate it, translate it, scale it. And that’s what continuum mechanics is about. Continuum mechanics is about understanding tissue on a continuum, and diffeomorphic deformations are not thinking of it as a rigid body but rather being able to deform it smoothly and continuously.”<\/p>\n

To learn how to apply equations from continuum mechanics to the map of the human body, Miller went on a series of pilgrimages to Brown University and to Ecole Normale Sup\u00e9rieure outside Paris, where he has visited annually over the past 10 years. Brown is the home of Ulf Grenander, one of the world’s best-known applied mathematicians. Miller persuaded him to collaborate on a series of theoretical papers in computational anatomy, which later evolved into a book the two men co-authored in 2007. Ecole Normale Sup\u00e9rieure is the home of Alain Trouv\u00e9 and originally Laurent Younes, now a professor in Miller’s Center for Imaging Science at Hopkins. With Younes, Miller has formalized the field of computational anatomy and the theory of diffeomorphic shape.<\/p>\n

“I’ve had the privilege of working with a lot of excellent scientists, but this-this was a lot of fun,” Brown’s Grenander says. “Mike was very knowledgeable, very inventive. He does theoretical mathematics, but he has a very practical point of view. He’s remarkable at getting things done, and at getting his graduate students interested in their work.”<\/p>\n

One of Miller’s current graduate assistants at Hopkins, a third-year doctoral student in biomedical engineering named Daniel Tward, says he’s grateful that Miller trusts young researchers to structure their own work. “We have one good solid conversation every week or two,” Tward says. “Other than that, he’s hands-off.”<\/p>\n

Tward served as a teaching assistant this spring in Miller’s undergraduate introductory biomedical engineering course, which has roughly 130 students. “He told the TAs that more than anything else, he wanted the students to feel supported and looked after,” Tward says. And that devotion was reciprocated: When Miller expressed surprise at how well the class had done on an early test, Tward pointed out that only one student out of 130 (excluding three who dropped the course) had failed to turn in a weekly homework assignment during the first half of the semester.<\/p>\n

<\/p>\n

Each week, Miller and his team process several dozen images-sometimes as many as a hundred-that are sent by colleagues from other departments at Hopkins. One of Miller’s most important sources is Marilyn S. Albert, a professor of neurology at the School of Medicine and the director of the Johns Hopkins University Alzheimer’s Disease Research Center. Albert supervises a longitudinal study of 300 Americans who are known to be at elevated risk for Alzheimer’s, in some cases because close family members have had the disease. The participants in that study have had brain images performed at regular intervals, and Miller’s center oversees the analysis of these images and is also responsible for distributing them to outside investigators.<\/p>\n

“Some people who are still cognitively normal have Alzheimer’s pathology in their brains, but don’t yet have easily visible nerve cell loss,” Albert says. “We and a number of other people around the country are trying to identify measures that can predict which people who are cognitively normal will progress to mild impairment and beyond.”<\/p>\n

Albert says that Miller is gifted at schooling himself in the language of the biomedical scholars he works with-and also gifted at explaining to doctors the arcane elements of his mathematical models. “The more we work together,” she says, “the more comfortable we become in building these collaborations.”<\/p>\n

That kind of collaboration, Miller says, is one of the gifts of being at an institution like Hopkins. Only at a university with an ambitious medical center, he says, would it be possible to develop as many collaborative relationships as he has.<\/p>\n

“I’m just one of 500 neuroscientists in the Brain Science Institute at Johns Hopkins University doing this kind of work,” Miller says. “It’s important to keep that perspective. We are all a part of a very big play here.”<\/p>\n","protected":false},"excerpt":{"rendered":"

At the vanguard of imaging science, Michael I. 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