I am a graduate student at MIT in the EECS department, advised by Polina Golland. I am interested in models for medical image analysis and characterizing genetic effects on imaging phenotypes. I have been lucky to work with wonderful collaborators.
I serve on the MICCAI Society Student Board, and participate in the MIT-MGH SITECOR program, which allows engineers to observe surgical procedures in an effort to improve O.R. technologies. I also have a side passion for computational photography and vision.
My wife, Monica, is a graduate student at MIT in the Biology department doing exciting research on cancer biology.
Current Main Projects
Predictive Modeling of Imaging and Genetics
I'm interested in the prediction of an entire medical image (e.g. MRI) given a subject's genotype and environmental factors. Towards this end, we are currently developing predictive mathematical models, the first of which will be presented at MICCAI 2015 in Munich.
- A.V. Dalca, R. Sridharan, M.R. Sabuncu, P. Golland.
Predictive Modeling of Anatomy with Genetic and Clinical Data.
To Appear In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2015.
- Co-organizing the second MICGen: MICCAI Workshop on Imaging Genetics at MICCAI 2014 after a very successful inaugural MICGen 2014
- K.N. Batmanghelich, A.V. Dalca, M.R. Sabuncu, P. Golland.
Joint Modeling of Imaging and Genetics,
In Proc. IPMI: International Conference on Information Processing and Medical Imaging, LNCS 79
17, pp. 766–777, 2013.
Stroke is one of the top causes of death and debilitating injury. We are developing models for extracting important phenotypes from medical stroke imaging to aid in prediction, risk assesment and genetic exploration. Stroke is also a main applications of our imaging genetics models mentioned above.
- A.V. Dalca, R. Sridharan, L. Cloonan, K. M. Fitzpatrick, A. Kanakis, K.L. Furie, J.Rosand, O.Wu, M.Sabuncu, N.S. Rost, P.Golland. Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), LNCS 8674, pp. 773-780, 2014.
- R. Sridharan‡, A.V. Dalca‡, K.M. Fitzpatrick, L.
Cloonan, A. Kanakis, O. Wu, K.L. Furie, J. Rosand,
N.S. Rost, P. Golland.
Quantification and Analysis of Large Multimodal Clinical Image Studies:
Application to Stroke. In Proc. MICCAI International Workshop
on Multimodal Brain Image Analysis (MBIA), pp. 18–30, 2013.
‡ equal contribution
tipiX is a new approach for fast and effective visualization of large image collections. This applies to both natural images as well as medical volumes in population studies. The key insight is to collapse inherently high-dimensional imaging data onto an interactive two-dimensional canvas native to a computer screen in a way that enables intuitive browsing of the image data. Several examples will get you started in various domains. The code is available on github.
- A.V. Dalca, R. Sridharan, N.S. Rost, P. Golland.
tipiX: Rapid Visualization of
Large Image Collections In MICCAI-IMIC Interactive Medical Image Computing Workshop, 2014.
Best paper award for impact and usability.
Finalist CSAIL Amazing Research Highlight Competition.
Current Side Projects
SciEx is an initiative started by Zoya Bylinskii and myself to promote excitement in science. We are starting with a video competition where entries will aim to be stimulate interest in science for the younger generation in a way that x-games youtube videos stimulate interest in sports.