Deborah Sturm
College of Staten Island
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Publication
Featured researches published by Deborah Sturm.
Medical Imaging 2006: Physiology, Function, and Structure from Medical Images | 2006
Lihong Li; Xiang Li; Xinzhou Wei; Deborah Sturm; Hongbing Lu; Zhengrong Liang
Multiple Sclerosis (MS) is an inflammatory and demyelinating disorder of the central nervous system with a presumed immune-mediated etiology. For treatment of MS, the measurements of white matter (WM), gray matter (GM), and cerebral spinal fluid (CSF) are often used in conjunction with clinical evaluation to provide a more objective measure of MS burden. In this paper, we apply a new unifying automatic mixture-based algorithm for segmentation of brain tissues to quantitatively analyze MS. The method takes into account the following effects that commonly appear in MR imaging: 1) The MR data is modeled as a stochastic process with an inherent inhomogeneity effect of smoothly varying intensity; 2) A new partial volume (PV) model is built in establishing the maximum a posterior (MAP) segmentation scheme; 3) Noise artifacts are minimized by a priori Markov random field (MRF) penalty indicating neighborhood correlation from tissue mixture. The volumes of brain tissues (WM, GM) and CSF are extracted from the mixture-based segmentation. Experimental results of feasibility studies on quantitative analysis of MS are presented.
international conference of design, user experience, and usability | 2017
Kristen Gillespie; Gabriel Goldstein; David Shane Smith; Ariana Riccio; Michael Kholodovsky; Cali Merendino; Stanislav Leskov; Rayan Arab; Hassan Elsherbini; Pavel Asanov; Deborah Sturm
We are developing a game to help autistic people collaborate with their siblings and peers while improving their social-communicative skills. Autistic college students have been involved in game design and evaluation since the project’s inception. They have provided invaluable feedback that we are incorporating with their help. By involving autistic students in game design and evaluation, we are helping them develop employment-readiness skills while ensuring that the game is well designed to teach autistic people.
ieee international conference on serious games and applications for health | 2016
Deborah Sturm; Ed Peppe; Bertram Ploog
We present an emotion recognition game, eMot-iCan, that is designed to assess and possibly remediate social accessibility in individuals with Autism Spectrum Disorders (ASDs). The game tests the theory that atypical attention patterns are at the root of several of the features that characterize ASD. These features include impaired social and communicative skills, difficulty in adapting to changing environments, and academic underachievement. Our framework applies trials designed by domain experts that allow for standard repeatable measures across sessions and players. The game is designed to go beyond drilling skills; instead it aims to assess and customize learning. We are currently piloting the game with administrators of the game and with players with a wide range of skills and abilities for the assessment and possible treatment of autism spectrum disorders (ASD).
Proceedings of SPIE | 2011
Deborah Sturm; Devorah Kletenik; Phillip Koshy
Multiple Sclerosis (MS) lesions are known to change over time. The location, size and shape characteristics of lesions are often used to diagnose and to track disease progression. We have improved our lesion-browsing tool that allows users to automatically locate successive significant lesions in a MRI stack. In addition, an automatic alignment feature was implemented to facilitate comparisons across stacks. A lesion stack is formed that can be browsed independently or in tandem with the image windows. Lesions of interest can then be measured, rendered and rotated. Multiple windows allow the viewer to compare the size and shape of lesions from the MRI images of the same patient taken at different time intervals.
international conference on information technology: new generations | 2010
Deborah Sturm; Philip Koshy; Diana Kovalerchik; Nirav Thakkar
The location, size and shape of Multiple Sclerosis (MS) lesions are often used to diagnose and track disease progression. In order to effectively compare lesions in MRI stacks for the same patient imaged at intervals, these stacks must be aligned. This automatic alignment method was designed to minimize modification of segmented pixel values. The aligned lesion stacks can be browsed independently or in tandem. This should provide a valuable tool for computer-aided diagnosis and disease tracking.
technical symposium on computer science education | 2007
Deborah Sturm; R. S. Beiss
We describe a web-based interface that facilitates entering and analyzing medical errors. It uses an interactive causal tree-building component. The interactive component allows a user to build a causal tree with any number of events and antecedents. This replaces a form-based approach that is limited to a predetermined number of events and antecedents. After the causal tree is completed, the user can save the tree to a database. Causal trees can be retrieved and rebuilt as well. We developed an algorithm that, given a data bank of reported errors, will help detect similar events. This facilitates recognizing patterns of errors.
technical symposium on computer science education | 2018
Devorah Kletenik; Deborah Sturm
We report our experience teaching elective game development courses at two colleges at a public university. Over the past nine years these courses have been taught in a variety of languages on several platforms. As the courses evolved we introduced serious games with game-based-learning as a focus for the projects and ultimately offered a special topics elective in serious game development. In this paper, we discuss the merits of using serious games as a focus in game programming, including the benefits for students without a strong interest in gaming. We also describe the novel restructuring of one colleges Computer Science elective sequence in response to recommendations from students, alumni, and an advisory board of computing professionals. By introducing 200-level electives, students are able to sample advanced topics including game development early in their academic sequence. This has led to involving more students in game-based undergraduate research which can result in increased interest and retention in Computer Science. We discuss our curriculum design and lessons learned including challenges and successes, and data from student surveys indicating student motivation and engagement.
international conference on human-computer interaction | 2017
Deborah Sturm; Jonathan Zomick; Ian Loch; Dan McCloskey
We report on a serious game that is designed to teach the functional anatomy of the human brain to undergraduate and graduate students in Psychology and Neuroscience courses. The game provides a unique, immersive, first-person experience for students to understand the discrete faculties of the human brain and the associated brain regions. In addition to our core designers and developers, we included design-feedback testers on our team to give us iterative feedback throughout the development process. Initial feedback from this group indicates that “Free Will” is effective as a game-based learning supplement.
international conference on human-computer interaction | 2017
Devorah Kletenik; Florencia Salinas; Chava Shulman; Claudia Bergeron; Deborah Sturm
In this work, we discuss our ongoing project to design a serious game to teach advanced programming concepts in C++. These concepts are challenging for students to learn; our game is a fun and motivating way for students to learn and practice their understanding. Our game, Point Mouster, was designed and developed by women Computer Science majors and is part of a study to examine whether games with specific design elements can help recruit and retain female students. We report on a pilot study of our game conducted at Brooklyn College and the College of Staten Island. Students, including an unusually high number of female participants, demonstrated educational effectiveness, and reported high levels of motivation and engagement.
conference on computers and accessibility | 2017
Deborah Sturm; Kristen Gillespie-Lynch; Michael Kholodovsky
We developed an engagement metric that is embedded in a two-player Kinect game. The game is designed to help autistic people interact and collaborate with each other and others. Each level has two phases - initially the players work on a task independently. In order to complete the task they must collaborate and agree. We added a component to assess the level of in-game cooperation. Using face tracking we developed a metric to automatically quantify collaboration based on the amount of time each player individually and together engage with one another. This can replace the time-consuming hand-coded evaluations. We also designed collaborative reward games including one that encourages players to interact with each other.