Education & Training

  • Ph.D. candidate 2009-Present

    Ph.D. in Bioengineering

    University of Pennsylvania, Department of bioengineering

  • M.S.2008

    Master of Science in Mechanical engineering and Applied mechanics

    University of Pennsylvania

  • B.E.2006

    Bachelor of Engineering

    University of Mumbai, Mumbai, India

Ongoing research projects

Heterogeneity analysis using imaging

We are attempting to develop machine learning based methods to explore heterogeneity in neuropsychiatric disorders

Tumor tracking and segmentation

Segmentation of brain tumors is a unique image segmentation problem where no prior anatomical guidance is available

Spinal lesion tracking

The anatomy of the spine is particularly challenging for existing tools. This makes lesion tracking extremely challenging.

Meta data analytics

Textual meta data can sbe used to generate priors for image classification and

Imaging genomics

the combination of imaging and genomic data can yield fundamentally novel insights

Age based confounds in neuroimaging

Age can severely confound large scale neuroimaging analysis, just like heterogeneity. We are developing methods that deal with this.

Work in progress

I am currently working on several different projects, some of which are manuscripts under review and others still in lab. This list simply provides a teaser. Stay tuned for more exciting research.

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Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

Yangming Ou, Susan P Weinstein, Emily F Conant, Sarah Englander, Xiao Da, Kristin Linn, Meng‐Kang Hsieh, Mark Rosen, Angela DeMichele, Christos Davatzikos, Despina Kontos
Journal Paper Magnetic Resonance in Medicine, July 2014


The purpose of the study was to evaluate DRAMMS, an attribute-based deformable registration algorithm, compared to other intensity-based algorithms, for longitudinal breast MRI registration, and to show its applicability in quantifying tumor changes over the course of neoadjuvant chemotherapy. I was involved in data preprocessing and interacting with the primary clinician on the project.

Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification [Click to download code]

Kristin Linn, Christos Davatzikos
Journal Paper Neuroimage, Volume 78, September 2013, Pages 270-283


Multivariate pattern analysis (MVPA) methods such as support vector machines (SVMs) have been increasingly applied to fMRI and sMRI analyses, enabling the detection of distinctive imaging patterns. However, identifying brain regions that significantly contribute to the classification/group separation requires computationally expensive permutation testing. In this paper we show that the results of SVM-permutation testing can be analytically approximated. This approximation leads to more than a thousandfold speedup of the permutation testing procedure, thereby rendering it feasible to perform such tests on standard computers. The speedup achieved makes SVM based group difference analysis competitive with standard univariate group difference analysis methods.

Multi-Atlas Skull-Stripping

Kristin Linn, Christos Davatzikos
Journal Paper Academic Radiology, Volume 20, September 2013, Pages 1566-1576


A software package was created that addresses the fundamental need for skull removal as a pre requisite for downstream image processing tasks. We implemented a multi atlas registration based tool for addressing this challenge.

Identifying Multivariate Imaging Patterns: Supervised, Semi-Supervised, and Unsupervised Learning Perspectives

Roman Filipovych, Kristin Linn, Christos Davatzikos
Book Chapter Academic Press Library in Signal Processing: Image, Video Processing and Analysis, Hardware, Audio, Acoustic and Speech Processing
Perspectives on image based heterogeneity analysis were provided in this book chapter. Some of our preliminary work on heterogeneity analysis has been published here.

Deriving statistical significance maps for SVM based image classification and group comparisons [Click to download Code]

Kristin Linn Christos Davatzikos
Conference Papers Medical Image Computing and Computer-Assisted Intervention– MICCAI 2012, Oral presentation


Population based pattern analysis and classification for quantifying structural and functional differences between diverse groups has been shown to be a powerful tool for the study of a number of diseases, and is quite commonly used especially in neuroimaging. The alternative to these pattern analysis methods, namely mass univariate methods such as voxel based analysis and all related methods, cannot detect multivariate patterns associated with group differences, and are not particularly suitable for developing individual-based diagnostic and prognostic biomarkers. A commonly used pattern analysis tool is the support vector machine (SVM). Unlike univariate statistical frameworks for morphometry, analytical tools for statistical inference are unavailable for the SVM. In this paper, we show that null distributions ordinarily obtained by permutation tests using SVMs can be analytically approximated from the data. The analytical computation takes a small fraction of the time it takes to do an actual permutation test, thereby rendering it possible to quickly create statistical significance maps derived from SVMs. Such maps are critical for understanding imaging patterns of group differences and interpreting which anatomical regions are important in determining the classifier's decision.

Pattern based morphometry

Kristin Linn,Kilian Pohl, Christos Davatzikos
Conference Papers Medical Image Computing and Computer-Assisted Intervention– MICCAI 2011


Voxel based morphometry (VBM) is widely used in the neuroimaging community to infer group differences in brain morphology. VBM is effective in quantifying group differences highly localized in space. However it is not equally effective when group differences might be based on interactions between multiple brain networks. We address this by proposing a new framework called pattern based morphometry (PBM). PBM is a data driven technique. It uses a dictionary learning algorithm to extract global patterns that characterize group differences. We test this approach on simulated and real data obtained from ADNI . In both cases PBM is able to uncover complex global patterns effectively.

Automated segmentation of brain lesions by combining intensity and spatial information

Kristin Linn,Erus Guray, Nick Bryan, Christos Davatzikos
Conference Papers IEEE International symposium on biomedical imaging 2010


Quantitative analysis of brain lesions in large clinical trials is becoming more and more important. We present a new automated method, that combines intensity based lesion segmentation with a false positive elimination method based on the spatial distribution of lesions. A Support Vector Regressor (SVR) is trained on expert-defined lesion masks using image histograms as features, in order to obtain an initial lesion segmentation. A lesion probability map that represents the spatial distribution of true and false positives on the intensity based segmentation is constructed using the segmented lesions and manual masks. A k-Nearest Neighbor (kNN) classifier based on the lesion probability map is applied to refine the segmentation.

Automated segmentation of cortical necrosis using awavelet based abnormality detection system

Kristin Linn, Güray Erus, Kilian M Pohl, Manoj Tanwar, Stefan Margiewicz, R Nick Bryan, Christos Davatzikos
Conference Papers IEEE International symposium on biomedical imaging 2011


We propose an automated method to segment cortical necrosis from brain FLAIR-MR Images. Cortical necrosis are regions of dead brain tissue in the cortex caused by cerebrovascular disease (CVD). The accurate segmentation of these regions is difficult as their intensity patterns are similar to the adjoining cerebrospinal fluid (CSF). We generate a model of normal variation using MR scans of healthy controls. The model is based on the Jacobians of warps obtained by registering scans of normal subjects to a common coordinate system. For each patient scan a Jacobian is obtained by warping it to the same coordinate system. Large deviations between the model and subject-specific Jacobians are flagged as `abnormalities'. Abnormalities are segmented as cortical necrosis if they are in the cortex and have the intensity profile of CSF. We evaluate our method by using a set of 72 healthy subjects to model cortical variation. We use this model to successfully detect and segment cortical necrosis in a set of 37 patients with CVD. A comparison of the results with segmentations from two independent human experts shows that the overlap between our approach and either of the human experts is in the range of the overlap between the two human experts themselves.

Classifying medical images using morphological appearance manifold

Erdem Varol, Kristin Linn, Christos Davatzikos
Conference Papers IEEE International symposium on biomedical imaging 2013


Input features for medical image classification algorithms are extracted from raw images using a series of pre processing steps. One common preprocessing step in computational neuroanatomy and functional brain mapping is the nonlinear registration of raw images to a common template space. Typically, the registration methods used are parametric and their output varies greatly with changes in parameters. Most results reported previously perform registration using a fixed parameter setting and use the results as input to the subsequent classification step. The variation in registration results due to choice of parameters thus translates to variation of performance of the classifiers that depend on the registration step for input. Analogous issues have been investigated in the computer vision literature, where image appearance varies with pose and illumination, thereby making classification vulnerable to these confounding parameters. The proposed methodology addresses this issue by sampling image appearances as registration parameters vary, and shows that better classification accuracies can be obtained this way, compared to the conventional approach.

Deriving Statistical Significance Maps for Support Vector Regression Using Medical Imaging Data

Kristin Linn, Aristeidis Sotiras, Christos Davatzikos
Conference Papers Pattern Recognition in Neuroimaging (PRNI), 2013


Regression analysis involves predicting a continuos variable using imaging data. The Support Vector Regression (SVR) algorithm has previously been used in addressing regression analysis in neuroimaging. However, identifying the regions of the image that the SVR uses to model the dependence of a target variable remains an open problem. It is an important issue when one wants to biologically interpret the meaning of a pattern that predicts the variable(s) of interest, and therefore to understand normal or pathological process. One possible approach to the identification of these regions is the use of permutation testing. Permutation testing involves 1) generation of a large set of 'null SVR models' using randomly permuted sets of target variables, and 2) comparison of the SVR model trained using the original labels to the set of null models. These permutation tests often require prohibitively long computational time. Recent work in support vector classification shows that it is possible to analytically approximate the results of permutation testing in medical image analysis. We propose an analogous approach to approximate permutation testing based analysis for support vector regression with medical imaging data. In this paper we present 1) the theory behind our approximation, and 2) experimental results using two real datasets.

A Composite Multivariate Polygenic and Neuroimaging Score for Prediction of Conversion to Alzheimer's Disease

Kristin Linn, Aristeidis Sotiras, Christos Davatzikos
Conference Papers Pattern Recognition in Neuroimaging (PRNI), 2012


Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) are characterized by widespread pathological changes in the brain. At the same time, Alzheimer's disease is heritable with complex genetic underpinnings that may influence the timing of the related pathological changes in the brain and can affect the progression from MCI to AD. In this paper, we present a multivariate imaging genetics approach for prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment. We employ multivariate pattern recognition approaches to obtain neuroimaging and polygenic discriminators between the healthy individuals and AD patients. We then design, in a linear manner, a composite imaging-genetic score for prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment. We apply our approach within the Alzheimer's Disease Neuroimaging Initiative and show that the integration of polygenic and neuroimaging information improves prediction of conversion to AD.

Adaptive geodesic transform for segmentation of vertebrae on CT images

Kristin Linn, Liao Shu, Gerardo Hermosillo, Yiqiang Zhan
Conference Papers SPIE Medical imaging, 2014 (Oral)


Vertebral segmentation is a critical first step in any quantitative evaluation of vertebral pathology using CT images. This is especially challenging because bone marrow tissue has the same intensity profile as the muscle surrounding the bone. Thus simple methods such as thresholding or adaptive k-means fail to accurately segment vertebrae. While several other algorithms such as level sets may be used for segmentation any algorithm that is clinically deployable has to work in under a few seconds. To address these dual challenges we present here, a new algorithm based on the geodesic distance transform that is capable of segmenting the spinal vertebrae in under one second. To achieve this we extend the theory of the geodesic distance transforms proposed in1 to incorporate high level anatomical knowledge through adaptive weighting of image gradients. Such knowledge may be provided by the user directly or may be automatically generated by another algorithm. We incorporate information 'learnt' using a previously published machine learning algorithm2 to segment the L1 to L5 vertebrae. While we present a particular application here, the adaptive geodesic transform is a generic concept which can be applied to segmentation of other organs as well. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Teaching philosophy

My primary goal for student learning is for students to develop an independent and creative approach to problem solving in a team setting. This requires that we impart to students technological know how in conjunction with the right mix of communication and decision making skills.

In the real world setting, it is often teams of individuals who compete against one another for achieving a set target. This is true of the engineering industry as well. Whether one works for a product development boutique inside a large corporation or a small start up, teams of individuals get tasks done. My teaching philosophy is to simulate this environment in the classroom or in the lab setting.

One specific example would be a class designed to teach students the basics of medical data analysis. In a traditional setting one could go about assigning readings, projects and homeworks and follow this up with assessments through mid-term and final exams. However, this approach at best imparts only technical know how. While it is important to point students to the available learning resources and motivate them to learn, it is also important to understand that one of the best motivators is comes peer driven competition.

An alternate approach to classroom teaching would be to randomly split up the class up into teams, assign a specific data analysis problem to the class and grade team performance in addition to individual performance in tests and homeworks. This simulates the real world environment better and allows for the development of a mix of technical and people skills that makes for better engineers.

I think that an ideal approach to teaching in engineering needs to be a balance between the above two approaches. There needs to be a 'knowledge dissemination and individualized assessment' component as well as a 'problem and team centric competition' component. The degree to which one may include either component depends on factors such as subject matter, availability of resources and the level of student and instructor commitment. In the case of biomedical data analytics a team centric problem solving approach with faculty mentoring is my choice of a teaching style.

At My Office

You can find me at my office located at University of Pennsylvania, Center for biomedical image computing and analysis (Room no. 121, Suite 380, 3600 Market street, Philadelphia, PA 19104

I am at my office every day, but you may consider e-mailing to fix an appointment.

In center city Philadelphia

You can find me in center city Philadelphia during my off-work hours.

Please e-mail me and if you want to set up a time and place for a meeting

At My Lab

You can find me at my office located at University of Pennsylvania Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

I am at my office every day from 7:00 until 10:00 am, but you may consider a call to fix an appointment.