Diffusion Tensor Analysis
Adaptive Riemannian Metrics for Improved Geodesic Tracking of White Matter|
X. Hao, R.T. Whitaker, P.T. Fletcher. In Information Processing in Medical Imaging (IPMI), Lecture Notes in Computer Science (LNCS), Vol. 6801/2011, pp. 13--24. 2011.
Estimation of Smooth Growth Trajectories with Controlled Acceleration from Time Series Shape Data|
J. Fishbaugh, S. Durrleman, G. Gerig. In Lecture Notes in Computer Science, LNCS 6892, Springer, pp. 401--408. 2011.
Longitudinal shape analysis often relies on the estimation of a realistic continuous growth scenario from data sparsely distributed in time. In this paper, we propose a new type of growth model parameterized by acceleration, whereas standard methods typically control the velocity. This mimics the behavior of biological tissue as a mechanical system driven by external forces. The growth trajectories are estimated as smooth flows of deformations, which are twice differentiable. This differs from piecewise geodesic regression, for which the velocity may be discontinuous. We evaluate our approach on a set of anatomical structures of the same subject, scanned 16 times between 4 and 8 years of age. We show our acceleration based method estimates smooth growth, demonstrating improved regularity compared to piecewise geodesic regression. Leave-several-out experiments show that our method is robust to missing observations, as well as being less sensitive to noise, and is therefore more likely to capture the underlying biological growth.
Three-dimensional reconstruction of serial mouse brain sections using high-resolution large-scale mosaics|
M.L. Berlanga, S. Phan, E.A. Bushong, S. Lamont, S. Wu, O. Kwon, B.S. Phung, M. Terada, T. Tasdizen, E. Martone, M.H. Ellisman. In Frontiers in Neuroscience Methods, Vol. 5, pp. (published online). March, 2011.
Detection of Neuron Membranes in Electron Microscopy Images using Multi-scale Context and Radon-like Features|
M. Seyedhosseini, R. Kumar, E. Jurrus, R. Guily, M. Ellisman, H. Pfister, T. Tasdizen. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011, Lecture Notes in Computer Science (LNCS), Vol. 6891, pp. 670--677. 2011.
A rapid 2-D centerline extraction method based on tensor voting|
Z. Leng, J.R. Korenberg, B. Roysam, T. Tasdizen. In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1000--1003. 2011.
Quantifying variability in radiation dose due to respiratory-induced tumor motion|
S.E. Geneser, J.D. Hinkle, R.M. Kirby, Bo Wang, B. Salter, S. Joshi. In Medical Image Analysis, Vol. 15, No. 4, pp. 640--649. 2011.
Fast AdaBoost training using weighted novelty selection|
M. Seyedhosseini, A.R.C. Paiva, T. Tasdizen. In Proc. IEEE Intl. Joint Conf. on Neural Networks, San Jose, CA, USA pp. 1245--1250. August, 2011.
In this paper, a new AdaBoost learning framework, called WNS-AdaBoost, is proposed for training discriminative models. The proposed approach significantly speeds up the learning process of adaptive boosting (AdaBoost) by reducing the number of data points. For this purpose, we introduce the weighted novelty selection (WNS) sampling strategy and combine it with AdaBoost to obtain an efficient and fast learning algorithm. WNS selects a representative subset of data thereby reducing the number of data points onto which AdaBoost is applied. In addition, WNS associates a weight with each selected data point such that the weighted subset approximates the distribution of all the training data. This ensures that AdaBoost can trained efficiently and with minimal loss of accuracy. The performance of WNS-AdaBoost is first demonstrated in a classification task. Then, WNS is employed in a probabilistic boosting-tree (PBT) structure for image segmentation. Results in these two applications show that the training time using WNS-AdaBoost is greatly reduced at the cost of only a few percent in accuracy.
Point Set Registration Using Havrda–Charvat–Tsallis Entropy Measures|
N.J. Tustison, S.P. Awate, G. Song, T.S. Cook, J.C. Gee. In IEEE Transactions on Medical Imaging, Vol. 30, No. 2, pp. 451--460. 2011.
FADTTS: Functional Analysis of Diffusion Tensor Tract Statistics|
H. Zhu, L. Kong, R. Li, M.S. Styner, G. Gerig, W. Lin, J.H. Gilmore. In NeuroImage, Vol. 56, No. 3, pp. 1412--1425. 2011.
PubMed ID: 21335092
DTI registration in atlas based fiber analysis of infantile Krabbe disease|
Y. Wang, A. Gupta, Z. Liu, H. Zhang, M.L. Escolar, J.H. Gilmore, S. Gouttard, P. Fillard, E. Maltbie, G. Gerig, M. Styner. In Neuroimage, pp. (in print). 2011.
PubMed ID: 21256236
Twin-Singleton Differences in Neonatal Brain Structure|
R.C. Knickmeyer, C. Kang, S. Woolson, K.J. Smith, R.M. Hamer, W. Lin, G. Gerig, M. Styner, J.H. Gilmore. In Twin Research and Human Genetics, Vol. 14, No. 3, pp. 268--276. 2011.
Early Brain Overgrowth in Autism Associated with an Increase in Cortical Surface Area Before Age 2|
H.C. Hazlett, M. Poe, G. Gerig, M. Styner, C. Chappell, R.G. Smith, C. Vachet, J. Piven. In Arch of Gen Psych, Vol. 68, No. 5, pp. 467--476. 2011.
Optimal data-driven sparse parameterization of diffeomorphisms for population analysis|
S. Durrleman, M.W. Prastawa, G. Gerig, S. Joshi. In Proceedings of the IPMI 2011 conference, Springer LNCS, Vol. 6801/2011, pp. 123--134. July, 2011.
PubMed ID: 20516153
Spatial Intensity Prior Correction for Tissue Segmentation in the Developing human Brain|
S.H. Kim, V. Fonov, J. Piven, J. Gilmore, C. Vachet, G. Gerig, D.L. Collins, M. Styner. In Proceedings of IEEE ISBI 2011, pp. 2049--2052. 2011.
CENTS: Cortical Enhanced Neonatal Tissue Segmentation|
F. Shi, D. Shen, P.-T. Yap, Y. Fan, J.-Z. Cheng, H. An, L.L. Wald, G. Gerig, J.H. Gilmore, W. Lin. In Human Brain Mapping HBM, Vol. 32, No. 3, Note: ePub 5 Aug 2010, pp. 382--396. March, 2011.
PubMed ID: 20690143
Multi-scale Series Contextual Model for Image Parsing|
SCI Technical Report, M. Seyedhosseini, A.R.C. Paiva, T. Tasdizen. No. UUSCI-2011-004, SCI Institute, University of Utah, 2011.
Exploring the Retinal Connectome|
J.R. Anderson, B.W. Jones, C.B. Watt, M.V. Shaw, J.-H. Yang, D. DeMill, J.S. Lauritzen, Y. Lin, K.D. Rapp, D. Mastronarde, P. Koshevoy, B. Grimm, T. Tasdizen, R.T. Whitaker, R.E. Marc. In Molecular Vision, Vol. 17, pp. 355--379. 2011.
PubMed ID: 21311605
Purpose: A connectome is a comprehensive description of synaptic connectivity for a neural domain. Our goal was to produce a connectome data set for the inner plexiform layer of the mammalian retina. This paper describes our first retinal connectome, validates the method, and provides key initial findings.
Methods: We acquired and assembled a 16.5 terabyte connectome data set RC1 for the rabbit retina at .2 nm resolution using automated transmission electron microscope imaging, automated mosaicking, and automated volume registration. RC1 represents a column of tissue 0.25 mm in diameter, spanning the inner nuclear, inner plexiform, and ganglion cell layers. To enhance ultrastructural tracing, we included molecular markers for 4-aminobutyrate (GABA), glutamate, glycine, taurine, glutamine, and the in vivo activity marker, 1-amino-4-guanidobutane. This enabled us to distinguish GABAergic and glycinergic amacrine cells; to identify ON bipolar cells coupled to glycinergic cells; and to discriminate different kinds of bipolar, amacrine, and ganglion cells based on their molecular signatures and activity. The data set was explored and annotated with Viking, our multiuser navigation tool. Annotations were exported to additional applications to render cells, visualize network graphs, and query the database.
Results: Exploration of RC1 showed that the 2 nm resolution readily recapitulated well known connections and revealed several new features of retinal organization: (1) The well known AII amacrine cell pathway displayed more complexity than previously reported, with no less than 17 distinct signaling modes, including ribbon synapse inputs from OFF bipolar cells, wide-field ON cone bipolar cells and rod bipolar cells, and extensive input from cone-pathway amacrine cells. (2) The axons of most cone bipolar cells formed a distinct signal integration compartment, with ON cone bipolar cell axonal synapses targeting diverse cell types. Both ON and OFF bipolar cells receive axonal veto synapses. (3) Chains of conventional synapses were very common, with intercalated glycinergic-GABAergic chains and very long chains associated with starburst amacrine cells. Glycinergic amacrine cells clearly play a major role in ON-OFF crossover inhibition. (4) Molecular and excitation mapping clearly segregates ultrastructurally defined bipolar cell groups into different response clusters. (5) Finally, low-resolution electron or optical imaging cannot reliably map synaptic connections by process geometry, as adjacency without synaptic contact is abundant in the retina. Only direct visualization of synapses and gap junctions suffices.
Conclusions: Connectome assembly and analysis using conventional transmission electron microscopy is now practical for network discovery. Our surveys of volume RC1 demonstrate that previously studied systems such as the AII amacrine cell network involve more network motifs than previously known. The AII network, primarily considered a scotopic pathway, clearly derives ribbon synapse input from photopic ON and OFF cone bipolar cell networks and extensive photopic GABAergic amacrine cell inputs. Further, bipolar cells show extensive inputs and outputs along their axons, similar to multistratified nonmammalian bipolar cells. Physiologic evidence of significant ON-OFF channel crossover is strongly supported by our anatomic data, showing alternating glycine-to-GABA paths. Long chains of amacrine cell networks likely arise from homocellular GABAergic synapses between starburst amacrine cells. Deeper analysis of RC1 offers the opportunity for more complete descriptions of specific networks.
Keywords: neuroscience, retina, vision, blindness, visus, crcns
Trace Driven Registration of Neuron Confocal Microscopy Stacks|
L. Hogrebe, A. Paiva, E. Jurrus, C. Christensen, M. Bridge, J.R. Korenberg, T. Tasdizen. In IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1345--1348. 2011.
The Viking Viewer: Scalable Multiuser Annotation and Summarization of Large Volume Datasets|
J.R. Anderson, B.C. Grimm, S. Mohammed, B.W. Jones, T. Tasdizen, J. Spaltenstein, P. Koshevoy, R.T. Whitaker, R.E. Marc. In Journal of Microscopy, Vol. 241, No. 1, pp. 13--28. 2010.
Edge enhanced spatio-temporal constrained reconstruction of undersampled dynamic contrast enhanced radial MRI|
S.K. Iyer, E. DiBella, T. Tasdizen. In IEEE International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, pp. 704--707. 2010.