TEDxGöteborg 2010
Anders Ynnerman: Visualizing the medical data explosion
Filmed:
Readability: 3.4
539,883 views
Medical scans can produce thousands of images for a single patient in seconds, but how do doctors know what's useful? Scientific visualization expert Anders Ynnerman shows us sophisticated new tools -- like virtual autopsies -- for analyzing our data, and hints at the sci-fi-sounding medical technologies coming up next. This talk contains some graphic medical imagery.
Anders Ynnerman - Scientific visualization expert
Anders Ynnerman studies the fundamental aspects of computer graphics and visualization, in particular large scale and complex data sets with a focus on volume rendering and multi-modal interaction. Full bio
Anders Ynnerman studies the fundamental aspects of computer graphics and visualization, in particular large scale and complex data sets with a focus on volume rendering and multi-modal interaction. Full bio
Double-click the English transcript below to play the video.
00:15
I will start by posing a little bit of a challenge:
0
0
4000
00:19
the challenge of dealing with data,
1
4000
3000
00:22
data that we have to deal with
2
7000
2000
00:24
in medical situations.
3
9000
2000
00:26
It's really a huge challenge for us.
4
11000
2000
00:28
And this is our beast of burden --
5
13000
2000
00:30
this is a Computer Tomography machine,
6
15000
2000
00:32
a CT machine.
7
17000
2000
00:34
It's a fantastic device.
8
19000
2000
00:36
It uses X-rays, X-ray beams,
9
21000
2000
00:38
that are rotating very fast around the human body.
10
23000
3000
00:41
It takes about 30 seconds to go through the whole machine
11
26000
2000
00:43
and is generating enormous amounts of information
12
28000
2000
00:45
that comes out of the machine.
13
30000
2000
00:47
So this is a fantastic machine
14
32000
2000
00:49
that we can use
15
34000
2000
00:51
for improving health care,
16
36000
2000
00:53
but as I said, it's also a challenge for us.
17
38000
2000
00:55
And the challenge is really found in this picture here.
18
40000
3000
00:58
It's the medical data explosion
19
43000
2000
01:00
that we're having right now.
20
45000
2000
01:02
We're facing this problem.
21
47000
2000
01:04
And let me step back in time.
22
49000
2000
01:06
Let's go back a few years in time and see what happened back then.
23
51000
3000
01:09
These machines that came out --
24
54000
2000
01:11
they started coming in the 1970s --
25
56000
2000
01:13
they would scan human bodies,
26
58000
2000
01:15
and they would generate about 100 images
27
60000
2000
01:17
of the human body.
28
62000
2000
01:19
And I've taken the liberty, just for clarity,
29
64000
2000
01:21
to translate that to data slices.
30
66000
3000
01:24
That would correspond to about 50 megabytes of data,
31
69000
2000
01:26
which is small
32
71000
2000
01:28
when you think about the data we can handle today
33
73000
3000
01:31
just on normal mobile devices.
34
76000
2000
01:33
If you translate that to phone books,
35
78000
2000
01:35
it's about one meter of phone books in the pile.
36
80000
3000
01:38
Looking at what we're doing today
37
83000
2000
01:40
with these machines that we have,
38
85000
2000
01:42
we can, just in a few seconds,
39
87000
2000
01:44
get 24,000 images out of a body,
40
89000
2000
01:46
and that would correspond to about 20 gigabytes of data,
41
91000
3000
01:49
or 800 phone books,
42
94000
2000
01:51
and the pile would then be 200 meters of phone books.
43
96000
2000
01:53
What's about to happen --
44
98000
2000
01:55
and we're seeing this; it's beginning --
45
100000
2000
01:57
a technology trend that's happening right now
46
102000
2000
01:59
is that we're starting to look at time-resolved situations as well.
47
104000
3000
02:02
So we're getting the dynamics out of the body as well.
48
107000
3000
02:05
And just assume
49
110000
2000
02:07
that we will be collecting data during five seconds,
50
112000
3000
02:10
and that would correspond to one terabyte of data --
51
115000
2000
02:12
that's 800,000 books
52
117000
2000
02:14
and 16 kilometers of phone books.
53
119000
2000
02:16
That's one patient, one data set.
54
121000
2000
02:18
And this is what we have to deal with.
55
123000
2000
02:20
So this is really the enormous challenge that we have.
56
125000
3000
02:23
And already today -- this is 25,000 images.
57
128000
3000
02:26
Imagine the days
58
131000
2000
02:28
when we had radiologists doing this.
59
133000
2000
02:30
They would put up 25,000 images,
60
135000
2000
02:32
they would go like this, "25,0000, okay, okay.
61
137000
3000
02:35
There is the problem."
62
140000
2000
02:37
They can't do that anymore. That's impossible.
63
142000
2000
02:39
So we have to do something that's a little bit more intelligent than doing this.
64
144000
3000
02:43
So what we do is that we put all these slices together.
65
148000
2000
02:45
Imagine that you slice your body in all these directions,
66
150000
3000
02:48
and then you try to put the slices back together again
67
153000
3000
02:51
into a pile of data, into a block of data.
68
156000
2000
02:53
So this is really what we're doing.
69
158000
2000
02:55
So this gigabyte or terabyte of data, we're putting it into this block.
70
160000
3000
02:58
But of course, the block of data
71
163000
2000
03:00
just contains the amount of X-ray
72
165000
2000
03:02
that's been absorbed in each point in the human body.
73
167000
2000
03:04
So what we need to do is to figure out a way
74
169000
2000
03:06
of looking at the things we do want to look at
75
171000
3000
03:09
and make things transparent that we don't want to look at.
76
174000
3000
03:12
So transforming the data set
77
177000
2000
03:14
into something that looks like this.
78
179000
2000
03:16
And this is a challenge.
79
181000
2000
03:18
This is a huge challenge for us to do that.
80
183000
3000
03:21
Using computers, even though they're getting faster and better all the time,
81
186000
3000
03:24
it's a challenge to deal with gigabytes of data,
82
189000
2000
03:26
terabytes of data
83
191000
2000
03:28
and extracting the relevant information.
84
193000
2000
03:30
I want to look at the heart.
85
195000
2000
03:32
I want to look at the blood vessels. I want to look at the liver.
86
197000
2000
03:34
Maybe even find a tumor,
87
199000
2000
03:36
in some cases.
88
201000
2000
03:39
So this is where this little dear comes into play.
89
204000
2000
03:41
This is my daughter.
90
206000
2000
03:43
This is as of 9 a.m. this morning.
91
208000
2000
03:45
She's playing a computer game.
92
210000
2000
03:47
She's only two years old,
93
212000
2000
03:49
and she's having a blast.
94
214000
2000
03:51
So she's really the driving force
95
216000
3000
03:54
behind the development of graphics-processing units.
96
219000
3000
03:58
As long as kids are playing computer games,
97
223000
2000
04:00
graphics is getting better and better and better.
98
225000
2000
04:02
So please go back home, tell your kids to play more games,
99
227000
2000
04:04
because that's what I need.
100
229000
2000
04:06
So what's inside of this machine
101
231000
2000
04:08
is what enables me to do the things that I'm doing
102
233000
2000
04:10
with the medical data.
103
235000
2000
04:12
So really what I'm doing is using these fantastic little devices.
104
237000
3000
04:15
And you know, going back
105
240000
2000
04:17
maybe 10 years in time
106
242000
2000
04:19
when I got the funding
107
244000
2000
04:21
to buy my first graphics computer --
108
246000
2000
04:23
it was a huge machine.
109
248000
2000
04:25
It was cabinets of processors and storage and everything.
110
250000
3000
04:28
I paid about one million dollars for that machine.
111
253000
3000
04:32
That machine is, today, about as fast as my iPhone.
112
257000
3000
04:37
So every month there are new graphics cards coming out,
113
262000
2000
04:39
and here is a few of the latest ones from the vendors --
114
264000
3000
04:42
NVIDIA, ATI, Intel is out there as well.
115
267000
3000
04:45
And you know, for a few hundred bucks
116
270000
2000
04:47
you can get these things and put them into your computer,
117
272000
2000
04:49
and you can do fantastic things with these graphics cards.
118
274000
3000
04:52
So this is really what's enabling us
119
277000
2000
04:54
to deal with the explosion of data in medicine,
120
279000
3000
04:57
together with some really nifty work
121
282000
2000
04:59
in terms of algorithms --
122
284000
2000
05:01
compressing data,
123
286000
2000
05:03
extracting the relevant information that people are doing research on.
124
288000
3000
05:06
So I'm going to show you a few examples of what we can do.
125
291000
3000
05:09
This is a data set that was captured using a CT scanner.
126
294000
3000
05:12
You can see that this is a full data [set].
127
297000
3000
05:15
It's a woman. You can see the hair.
128
300000
3000
05:18
You can see the individual structures of the woman.
129
303000
3000
05:21
You can see that there is [a] scattering of X-rays
130
306000
3000
05:24
on the teeth, the metal in the teeth.
131
309000
2000
05:26
That's where those artifacts are coming from.
132
311000
3000
05:29
But fully interactively
133
314000
2000
05:31
on standard graphics cards on a normal computer,
134
316000
3000
05:34
I can just put in a clip plane.
135
319000
2000
05:36
And of course all the data is inside,
136
321000
2000
05:38
so I can start rotating, I can look at it from different angles,
137
323000
3000
05:41
and I can see that this woman had a problem.
138
326000
3000
05:44
She had a bleeding up in the brain,
139
329000
2000
05:46
and that's been fixed with a little stent,
140
331000
2000
05:48
a metal clamp that's tightening up the vessel.
141
333000
2000
05:50
And just by changing the functions,
142
335000
2000
05:52
then I can decide what's going to be transparent
143
337000
3000
05:55
and what's going to be visible.
144
340000
2000
05:57
I can look at the skull structure,
145
342000
2000
05:59
and I can see that, okay, this is where they opened up the skull on this woman,
146
344000
3000
06:02
and that's where they went in.
147
347000
2000
06:04
So these are fantastic images.
148
349000
2000
06:06
They're really high resolution,
149
351000
2000
06:08
and they're really showing us what we can do
150
353000
2000
06:10
with standard graphics cards today.
151
355000
3000
06:13
Now we have really made use of this,
152
358000
2000
06:15
and we have tried to squeeze a lot of data
153
360000
3000
06:18
into the system.
154
363000
2000
06:20
And one of the applications that we've been working on --
155
365000
2000
06:22
and this has gotten a little bit of traction worldwide --
156
367000
3000
06:25
is the application of virtual autopsies.
157
370000
2000
06:27
So again, looking at very, very large data sets,
158
372000
2000
06:29
and you saw those full-body scans that we can do.
159
374000
3000
06:32
We're just pushing the body through the whole CT scanner,
160
377000
3000
06:35
and just in a few seconds we can get a full-body data set.
161
380000
3000
06:38
So this is from a virtual autopsy.
162
383000
2000
06:40
And you can see how I'm gradually peeling off.
163
385000
2000
06:42
First you saw the body bag that the body came in,
164
387000
3000
06:45
then I'm peeling off the skin -- you can see the muscles --
165
390000
3000
06:48
and eventually you can see the bone structure of this woman.
166
393000
3000
06:51
Now at this point, I would also like to emphasize
167
396000
3000
06:54
that, with the greatest respect
168
399000
2000
06:56
for the people that I'm now going to show --
169
401000
2000
06:58
I'm going to show you a few cases of virtual autopsies --
170
403000
2000
07:00
so it's with great respect for the people
171
405000
2000
07:02
that have died under violent circumstances
172
407000
2000
07:04
that I'm showing these pictures to you.
173
409000
3000
07:08
In the forensic case --
174
413000
2000
07:10
and this is something
175
415000
2000
07:12
that ... there's been approximately 400 cases so far
176
417000
2000
07:14
just in the part of Sweden that I come from
177
419000
2000
07:16
that has been undergoing virtual autopsies
178
421000
2000
07:18
in the past four years.
179
423000
2000
07:20
So this will be the typical workflow situation.
180
425000
3000
07:23
The police will decide --
181
428000
2000
07:25
in the evening, when there's a case coming in --
182
430000
2000
07:27
they will decide, okay, is this a case where we need to do an autopsy?
183
432000
3000
07:30
So in the morning, in between six and seven in the morning,
184
435000
3000
07:33
the body is then transported inside of the body bag
185
438000
2000
07:35
to our center
186
440000
2000
07:37
and is being scanned through one of the CT scanners.
187
442000
2000
07:39
And then the radiologist, together with the pathologist
188
444000
2000
07:41
and sometimes the forensic scientist,
189
446000
2000
07:43
looks at the data that's coming out,
190
448000
2000
07:45
and they have a joint session.
191
450000
2000
07:47
And then they decide what to do in the real physical autopsy after that.
192
452000
3000
07:52
Now looking at a few cases,
193
457000
2000
07:54
here's one of the first cases that we had.
194
459000
2000
07:56
You can really see the details of the data set.
195
461000
3000
07:59
It's very high-resolution,
196
464000
2000
08:01
and it's our algorithms that allow us
197
466000
2000
08:03
to zoom in on all the details.
198
468000
2000
08:05
And again, it's fully interactive,
199
470000
2000
08:07
so you can rotate and you can look at things in real time
200
472000
2000
08:09
on these systems here.
201
474000
2000
08:11
Without saying too much about this case,
202
476000
2000
08:13
this is a traffic accident,
203
478000
2000
08:15
a drunk driver hit a woman.
204
480000
2000
08:17
And it's very, very easy to see the damages on the bone structure.
205
482000
3000
08:20
And the cause of death is the broken neck.
206
485000
3000
08:23
And this women also ended up under the car,
207
488000
2000
08:25
so she's quite badly beaten up
208
490000
2000
08:27
by this injury.
209
492000
2000
08:29
Here's another case, a knifing.
210
494000
3000
08:32
And this is also again showing us what we can do.
211
497000
2000
08:34
It's very easy to look at metal artifacts
212
499000
2000
08:36
that we can show inside of the body.
213
501000
3000
08:39
You can also see some of the artifacts from the teeth --
214
504000
3000
08:42
that's actually the filling of the teeth --
215
507000
2000
08:44
but because I've set the functions to show me metal
216
509000
3000
08:47
and make everything else transparent.
217
512000
2000
08:49
Here's another violent case. This really didn't kill the person.
218
514000
3000
08:52
The person was killed by stabs in the heart,
219
517000
2000
08:54
but they just deposited the knife
220
519000
2000
08:56
by putting it through one of the eyeballs.
221
521000
2000
08:58
Here's another case.
222
523000
2000
09:00
It's very interesting for us
223
525000
2000
09:02
to be able to look at things like knife stabbings.
224
527000
2000
09:04
Here you can see that knife went through the heart.
225
529000
3000
09:07
It's very easy to see how air has been leaking
226
532000
2000
09:09
from one part to another part,
227
534000
2000
09:11
which is difficult to do in a normal, standard, physical autopsy.
228
536000
3000
09:14
So it really, really helps
229
539000
2000
09:16
the criminal investigation
230
541000
2000
09:18
to establish the cause of death,
231
543000
2000
09:20
and in some cases also directing the investigation in the right direction
232
545000
3000
09:23
to find out who the killer really was.
233
548000
2000
09:25
Here's another case that I think is interesting.
234
550000
2000
09:27
Here you can see a bullet
235
552000
2000
09:29
that has lodged just next to the spine on this person.
236
554000
3000
09:32
And what we've done is that we've turned the bullet into a light source,
237
557000
3000
09:35
so that bullet is actually shining,
238
560000
2000
09:37
and it makes it really easy to find these fragments.
239
562000
3000
09:40
During a physical autopsy,
240
565000
2000
09:42
if you actually have to dig through the body to find these fragments,
241
567000
2000
09:44
that's actually quite hard to do.
242
569000
2000
09:48
One of the things that I'm really, really happy
243
573000
2000
09:50
to be able to show you here today
244
575000
3000
09:53
is our virtual autopsy table.
245
578000
2000
09:55
It's a touch device that we have developed
246
580000
2000
09:57
based on these algorithms, using standard graphics GPUs.
247
582000
3000
10:00
It actually looks like this,
248
585000
2000
10:02
just to give you a feeling for what it looks like.
249
587000
3000
10:05
It really just works like a huge iPhone.
250
590000
3000
10:08
So we've implemented
251
593000
2000
10:10
all the gestures you can do on the table,
252
595000
3000
10:13
and you can think of it as an enormous touch interface.
253
598000
4000
10:17
So if you were thinking of buying an iPad,
254
602000
2000
10:19
forget about it. This is what you want instead.
255
604000
3000
10:22
Steve, I hope you're listening to this, all right.
256
607000
3000
10:26
So it's a very nice little device.
257
611000
2000
10:28
So if you have the opportunity, please try it out.
258
613000
2000
10:30
It's really a hands-on experience.
259
615000
3000
10:33
So it gained some traction, and we're trying to roll this out
260
618000
3000
10:36
and trying to use it for educational purposes,
261
621000
2000
10:38
but also, perhaps in the future,
262
623000
2000
10:40
in a more clinical situation.
263
625000
3000
10:43
There's a YouTube video that you can download and look at this,
264
628000
2000
10:45
if you want to convey the information to other people
265
630000
2000
10:47
about virtual autopsies.
266
632000
3000
10:50
Okay, now that we're talking about touch,
267
635000
2000
10:52
let me move on to really "touching" data.
268
637000
2000
10:54
And this is a bit of science fiction now,
269
639000
2000
10:56
so we're moving into really the future.
270
641000
3000
10:59
This is not really what the medical doctors are using right now,
271
644000
3000
11:02
but I hope they will in the future.
272
647000
2000
11:04
So what you're seeing on the left is a touch device.
273
649000
3000
11:07
It's a little mechanical pen
274
652000
2000
11:09
that has very, very fast step motors inside of the pen.
275
654000
3000
11:12
And so I can generate a force feedback.
276
657000
2000
11:14
So when I virtually touch data,
277
659000
2000
11:16
it will generate forces in the pen, so I get a feedback.
278
661000
3000
11:19
So in this particular situation,
279
664000
2000
11:21
it's a scan of a living person.
280
666000
2000
11:23
I have this pen, and I look at the data,
281
668000
3000
11:26
and I move the pen towards the head,
282
671000
2000
11:28
and all of a sudden I feel resistance.
283
673000
2000
11:30
So I can feel the skin.
284
675000
2000
11:32
If I push a little bit harder, I'll go through the skin,
285
677000
2000
11:34
and I can feel the bone structure inside.
286
679000
3000
11:37
If I push even harder, I'll go through the bone structure,
287
682000
2000
11:39
especially close to the ear where the bone is very soft.
288
684000
3000
11:42
And then I can feel the brain inside, and this will be the slushy like this.
289
687000
3000
11:45
So this is really nice.
290
690000
2000
11:47
And to take that even further, this is a heart.
291
692000
3000
11:50
And this is also due to these fantastic new scanners,
292
695000
3000
11:53
that just in 0.3 seconds,
293
698000
2000
11:55
I can scan the whole heart,
294
700000
2000
11:57
and I can do that with time resolution.
295
702000
2000
11:59
So just looking at this heart,
296
704000
2000
12:01
I can play back a video here.
297
706000
2000
12:03
And this is Karljohan, one of my graduate students
298
708000
2000
12:05
who's been working on this project.
299
710000
2000
12:07
And he's sitting there in front of the Haptic device, the force feedback system,
300
712000
3000
12:10
and he's moving his pen towards the heart,
301
715000
3000
12:13
and the heart is now beating in front of him,
302
718000
2000
12:15
so he can see how the heart is beating.
303
720000
2000
12:17
He's taken the pen, and he's moving it towards the heart,
304
722000
2000
12:19
and he's putting it on the heart,
305
724000
2000
12:21
and then he feels the heartbeats from the real living patient.
306
726000
3000
12:24
Then he can examine how the heart is moving.
307
729000
2000
12:26
He can go inside, push inside of the heart,
308
731000
2000
12:28
and really feel how the valves are moving.
309
733000
3000
12:31
And this, I think, is really the future for heart surgeons.
310
736000
3000
12:34
I mean it's probably the wet dream for a heart surgeon
311
739000
3000
12:37
to be able to go inside of the patient's heart
312
742000
3000
12:40
before you actually do surgery,
313
745000
2000
12:42
and do that with high-quality resolution data.
314
747000
2000
12:44
So this is really neat.
315
749000
2000
12:47
Now we're going even further into science fiction.
316
752000
3000
12:50
And we heard a little bit about functional MRI.
317
755000
3000
12:53
Now this is really an interesting project.
318
758000
3000
12:56
MRI is using magnetic fields
319
761000
2000
12:58
and radio frequencies
320
763000
2000
13:00
to scan the brain, or any part of the body.
321
765000
3000
13:03
So what we're really getting out of this
322
768000
2000
13:05
is information of the structure of the brain,
323
770000
2000
13:07
but we can also measure the difference
324
772000
2000
13:09
in magnetic properties of blood that's oxygenated
325
774000
3000
13:12
and blood that's depleted of oxygen.
326
777000
3000
13:15
That means that it's possible
327
780000
2000
13:17
to map out the activity of the brain.
328
782000
2000
13:19
So this is something that we've been working on.
329
784000
2000
13:21
And you just saw Motts the research engineer, there,
330
786000
3000
13:24
going into the MRI system,
331
789000
2000
13:26
and he was wearing goggles.
332
791000
2000
13:28
So he could actually see things in the goggles.
333
793000
2000
13:30
So I could present things to him while he's in the scanner.
334
795000
3000
13:33
And this is a little bit freaky,
335
798000
2000
13:35
because what Motts is seeing is actually this.
336
800000
2000
13:37
He's seeing his own brain.
337
802000
3000
13:40
So Motts is doing something here,
338
805000
2000
13:42
and probably he is going like this with his right hand,
339
807000
2000
13:44
because the left side is activated
340
809000
2000
13:46
on the motor cortex.
341
811000
2000
13:48
And then he can see that at the same time.
342
813000
2000
13:50
These visualizations are brand new.
343
815000
2000
13:52
And this is something that we've been researching for a little while.
344
817000
3000
13:55
This is another sequence of Motts' brain.
345
820000
3000
13:58
And here we asked Motts to calculate backwards from 100.
346
823000
3000
14:01
So he's going "100, 97, 94."
347
826000
2000
14:03
And then he's going backwards.
348
828000
2000
14:05
And you can see how the little math processor is working up here in his brain
349
830000
3000
14:08
and is lighting up the whole brain.
350
833000
2000
14:10
Well this is fantastic. We can do this in real time.
351
835000
2000
14:12
We can investigate things. We can tell him to do things.
352
837000
2000
14:14
You can also see that his visual cortex
353
839000
2000
14:16
is activated in the back of the head,
354
841000
2000
14:18
because that's where he's seeing, he's seeing his own brain.
355
843000
2000
14:20
And he's also hearing our instructions
356
845000
2000
14:22
when we tell him to do things.
357
847000
2000
14:24
The signal is really deep inside of the brain as well,
358
849000
2000
14:26
and it's shining through,
359
851000
2000
14:28
because all of the data is inside this volume.
360
853000
2000
14:30
And in just a second here you will see --
361
855000
2000
14:32
okay, here. Motts, now move your left foot.
362
857000
2000
14:34
So he's going like this.
363
859000
2000
14:36
For 20 seconds he's going like that,
364
861000
2000
14:38
and all of a sudden it lights up up here.
365
863000
2000
14:40
So we've got motor cortex activation up there.
366
865000
2000
14:42
So this is really, really nice,
367
867000
2000
14:44
and I think this is a great tool.
368
869000
2000
14:46
And connecting also with the previous talk here,
369
871000
2000
14:48
this is something that we could use as a tool
370
873000
2000
14:50
to really understand
371
875000
2000
14:52
how the neurons are working, how the brain is working,
372
877000
2000
14:54
and we can do this with very, very high visual quality
373
879000
3000
14:57
and very fast resolution.
374
882000
3000
15:00
Now we're also having a bit of fun at the center.
375
885000
2000
15:02
So this is a CAT scan -- Computer Aided Tomography.
376
887000
3000
15:06
So this is a lion from the local zoo
377
891000
2000
15:08
outside of Norrkoping in Kolmarden, Elsa.
378
893000
3000
15:11
So she came to the center,
379
896000
2000
15:13
and they sedated her
380
898000
2000
15:15
and then put her straight into the scanner.
381
900000
2000
15:17
And then, of course, I get the whole data set from the lion.
382
902000
3000
15:20
And I can do very nice images like this.
383
905000
2000
15:22
I can peel off the layer of the lion.
384
907000
2000
15:24
I can look inside of it.
385
909000
2000
15:26
And we've been experimenting with this.
386
911000
2000
15:28
And I think this is a great application
387
913000
2000
15:30
for the future of this technology,
388
915000
2000
15:32
because there's very little known about the animal anatomy.
389
917000
3000
15:35
What's known out there for veterinarians is kind of basic information.
390
920000
3000
15:38
We can scan all sorts of things,
391
923000
2000
15:40
all sorts of animals.
392
925000
2000
15:42
The only problem is to fit it into the machine.
393
927000
3000
15:45
So here's a bear.
394
930000
2000
15:47
It was kind of hard to get it in.
395
932000
2000
15:49
And the bear is a cuddly, friendly animal.
396
934000
3000
15:52
And here it is. Here is the nose of the bear.
397
937000
3000
15:55
And you might want to cuddle this one,
398
940000
3000
15:58
until you change the functions and look at this.
399
943000
3000
16:01
So be aware of the bear.
400
946000
2000
16:03
So with that,
401
948000
2000
16:05
I'd like to thank all the people
402
950000
2000
16:07
who have helped me to generate these images.
403
952000
2000
16:09
It's a huge effort that goes into doing this,
404
954000
2000
16:11
gathering the data and developing the algorithms,
405
956000
3000
16:14
writing all the software.
406
959000
2000
16:16
So, some very talented people.
407
961000
3000
16:19
My motto is always, I only hire people that are smarter than I am
408
964000
3000
16:22
and most of these are smarter than I am.
409
967000
2000
16:24
So thank you very much.
410
969000
2000
16:26
(Applause)
411
971000
4000
ABOUT THE SPEAKER
Anders Ynnerman - Scientific visualization expertAnders Ynnerman studies the fundamental aspects of computer graphics and visualization, in particular large scale and complex data sets with a focus on volume rendering and multi-modal interaction.
Why you should listen
Professor Anders Ynnerman received a Ph.D. in physics from Gothenburg University. During the early 90s he was doing research at Oxford University and Vanderbilt University. In 1996 he started the Swedish National Graduate School in Scientific Computing, which he directed until 1999. From 1997 to 2002 he directed the Swedish National Supercomputer Centre and from 2002 to 2006 he directed the Swedish National Infrastructure for Computing (SNIC).
Since 1999 he is holding a chair in scientific visualization at Linköping University and in 2000 he founded the Norrköping Visualization and Interaction Studio (NVIS). NVIS currently constitutes one of the main focal points for research and education in computer graphics and visualization in the Nordic region. Ynnerman is currently heading the build-up of a large scale center for Visualization in Norrköping.
Anders Ynnerman | Speaker | TED.com