Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper WeAT9.41
Segmentation of Anatomical Structures in Brain MR Images using Atlases in FSL - a Quantitative Approach
Soldea, Octavian, Sabanci Univ.
Ekin, Ahmet, Philips Res. Europe
Soldea, Diana Florentina, Sabanci Univ.
Unay, Devrim, Bahcesehir Univ.
Cetin, Mujdat, Sabanci Univ.
Ercil, Aytul, Sabanci Univ.
Uzunbas, Gokhan Mustafa, Rutgers State University
Firat, Zeynep, Yeditepe University Hospital
Cihangiroglu, Mutlu, Yeditepe University Hospital
Segmentation of brain structures from MR images is crucial in understanding the disease progress, diagnosis, and treatment
monitoring. Atlases, showing the expected locations of the structures, are commonly used to start and guide the segmentation
process. In many cases, the quality of the atlas may have a significant effect in the final result. In the literature,
commonly used atlases may be obtained from one subject’s data, only from the healthy, or depict only certain structures
that limit their accuracy. Anatomical variations, pathologies, imaging artifacts all could aggravate the problems related to
application of atlases. In this paper, we propose to use multiple atlases that are sufficiently different from each other as
much as possible to handle such problems. To this effect, we have built a library of atlases and computed their similarity
values to each other. Our study showed that the existing atlases have varying levels of similarity for different structures.
09:00-11:10, Paper WeAT9.42
Graphical Model-Based Tracking of Curvilinear Structures in Bio-Image Sequences
Koulgi, Pradeep, Univ. of California, Santa Barbara
Sargin, Mehmet Emre, Univ. of California, Santa Barbara
Rose, Kenneth, Univ. of California, Santa Barbara
Manjunath, B. S., Univ. of California, Santa Barbara
Tracking of curvilinear structures is a task of fundamental importance in the quantitative analysis of biological structures
such as neurons, blood vessels, retinal interconnects, microtubules, etc. The state of the art HMM-based contour tracking
scheme for tracking microtubules, while performing well in most scenarios, can miss the track if, during its growth, it intersects
another microtubule in its neighbourhood. In this paper we present a graphical model-based tracking algorithm
which propagates across frames information about the dynamics of all the microtubules. This allows the algorithm to faithfully
differentiate the contour of interest from others that contribute to the clutter, and maintain tracking accuracy. We
present results of experiments on real microtubule images captured using fluorescence microscopy, and show that our proposed
scheme outperforms the existing HMM-based scheme.
11:10-12:10, WePL1 Anadolu Auditorium
The Quantitative Analysis of User Behavior Online Data, Models and Algorithms
Prabhakar Raghavan Plenary Session
Yahoo! Research, USA
Prabhakar Raghavan has been the head of Yahoo! Research since 2005. His research interests include text and web mining,
and algorithm design. He is a consulting professor of Computer Science at Stanford University and editor-in-chief of the
Journal of the ACM. Prior to joining Yahoo!, he was the chief technology officer at Verity and has held a number of technical
and managerial positions at IBM Research. Prabhakar received his PhD from Berkeley and is a fellow of the ACM
and of the IEEE.
By blending principles from mechanism design, algorithms, machine learning and massive distributed computing, the search
industry has become good at optimizing monetization on sound scientific principles. This represents a successful and
growing partnership between computer science and microeconomics. When it comes to understanding how online users
respond to the content and experiences presented to them, we have more of a lacuna in the collaboration between computer
science and certain social sciences. We will use a concrete technical example from image search results presentation, developing
in the process some algorithmic and machine learning problems of interest in their own right. We then use this
example to motivate the kinds of studies that need to grow between computer science and the social sciences; a critical
element of this is the need to blend large-scale data analysis with smaller-scale eye-tracking and “individualized” lab studies.
- 193 -