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Holm, D. Risholm, S. Pieper, E. Samset and W. Background paper: P. Risholm et al. Varoquaux, F. Baronnet, A. Kleinschmidt, P. Fillard, and B. In particular, we will just be looking at pages in the book PDF pages Notes by George: reading-group-anovanotes. Aug 17th: Please read chapter 12, pgs. Also, please read pgs. Aug 24th and 31st pick one to attend : Please read chapter 13, pgs.

Sep 7th: Multilevel regression applied to fMRI. Gerber et al. Manifold modeling for brain population analysis. Medical Image Analysis 14 — Jia et al. NeuroImage 51 — The rough schedule is as follows:. Please read Sections 5. In addition, please skim 5. July 20th: We will talk about back propagation.

Please read Section 5. If time permits, we may go over computing the Hessian Section 5. Exerts from course notes: variational. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein. Introduction to Algorithms, Second Edition minmax. We will discuss primal-dual algorithms for solving linear programs. We will discuss primal-dual formulation in more detail. Book pages , correspond to PDF pages , Olivier Commowick, Simon K.

Warfield, and Gregoire Malandain. Davis, B. ICCV Rohlfing et al. Regression models for atlas appearance. Breimann 01 Random Forests: breiman01randomforests. IBP and the hierarchical Beta processes. Stick-breaking Construction for the IBP. Discriminative Shape Alignment. Marco Loog and Marleen de Bruijne. Proof of complex representations: procrusts-complex-representation. Two tutorials found by Tammy: procrustes-ch This paper references the work by Behrens et.

Behrens, Propagation of Uncertainty. Since both papers are fairly straightforward, if time permits, we will talk about both models. Finsler Active Contours , J. Melonakos, E. Pichon, S. Angenent, A. Melonakos, V. Mohan, M. Niethammer, K. Smith, M. Kubicki, A. Fisher, W. Wells, J. Levitt, A. Dirichlet Processes contd. We plan to read and understand the variational approximations from Kurihara et al. We are continuing with the hierarchical Dirichlet processes from Teh et al.

Dirichlet Processes. We plan to read and understand Teh et al. Klassen, A. Srivastava, W. Mio, and S. We will start by finishing the paper from last time. The homework is to derive the coefficients from the next level of splines from the coefficients of the previous level.

We will then discuss the thin-plate splines. FRED L. JUNE We will finish the registration paper and discuss multi-level splines in a bit more detail. Michal will lead the discussion on the registration paper. Bhatia, P. Aljabar, J. Boardman, L. Srinivasan, M. Murgasova, S. Counsell, M. Rutherford, J. Hajnal, A. Edwards, and D. We will work on details of geometry of the cost function and Lemma 2 in the sparse bayesian learning paper. David P. Wipf and Bhaskar D.

Sparse Bayesian Learning for Basis Selection. In particular, IV A proposes a very interesting proof. Durrleman, X. Pennec, A. Trouve, P. Thompson, N. Vaillant and J. It is closely related to some other learning schemes. Using the logarithm of odds to define a vector space on probabilistic atlases. Kilian M. McCarley , W. Eric L. Grimson , Ron Kikinis , William M. Wells Medical Image Analysis IEEE Trans.

Reiss, Rebecca A. Dutton, Agatha D. Lee, Albert M. Galaburda, Ivo D. Dinov, Paul M. Thompson, Arthur W. Friston, L. Human Brain Mapping, Friston, K. Neuroimage, A tutorial on Generalized Linear Models. Journal of Quality Technology, Joshua E. Cates, P. The goal is to find a geometric interpretation of the first term in the sum in eq.

It will be an easier start than understanding the more theoretical underpinnings of the other LDDMM papers. Note that the first lemma is the hardest part of the paper, but things get a lot easier after that.

We will discuss the Nystrom Method, which can be used to approximate the eigendecomposition of large matrices. An application of this technique to Spectral Clustering is presented in Fowlkes et al. New paper rao-sankhyasera. We will discuss Beckmann et al. We will discuss Blei et al.

We will also discuss this. We will continue on the Wisharts paper; the homework is to get more comfortable with that particular distribution. Bing Jian, Baba C. Torralba, Murphy and Freeman. Sharing visual features for multiclass and multiview object detection.

Chris McIntosh and Ghassan Hamarneh. Is a Single Energy Functional Sufficient? Adaptive Energy Functionals and Automatic Initialization. Shaohua Kevin Zhou and Dorin Comaniciu. Shape Regression Machine. We will discuss the two simple examples defined in last meeting: a closed curve and an open curve. Please work through the examples and come with beatiful matlab figures of the embedding.

We will also talk about wavelets. The first paper is what we already looked at; the other two are longer, more detailed versions. Geometric diffusions as a tool for harmonic analysis and structure definition of data: Multiscale methods.

Additional papers on the topic: Diffusion wavelets and their use in spectral clustering: nadler Laplacian-Eigenmaps by Belkin and Niyogi laplacianeigenmaps. Further papers on discrete Laplace-Beltrami Operators overview : xudiscretelaplace. We will finish the diffusion map discussion and will talk about the second paper.

The week after that, we will come back to the operators in the first paper. We will start by discussing the first paper and go on to the second one. You only need to read the first paper for this meeting, but we will end up reading both by the end of this series. Danial will present: Mahony, R.

Manton, J. Biz will present. Our main reference is: Constructing free-energy approximations and generalized belief propagation algorithms: J. Yedidia, et al. The basic mathematical motivation of belief propagation: A. Aji and J. McEliece gendislaw. To see examples: Y. Weiss and W. Kschischang, et al. And a couple of other popular review papers by the same authors as of our main paper: genbp. William T.

Freeman, Thouis R. Jones, and Egon C. Freeman, E. Pasztor, O. InfoMax ICA algorithm and its connection to projection pursuit. An information-maximization approach to blind separation and blind deconvolution. Series A General , Vol. Dhillon, Subramanyam Mallela, and Dharmendra S. Modha pdhillon. Jonathan Richard Shewchuk. We will continue with the same paper. Please read Section 4 and the appendices. Danial: The following paper makes a nice connection between exponential-family-mixture-model and distance-measure-based clustering methods.

Please read the first three sections for the first meeting. We will go through the key concepts: Bregman divergence, and information in more detail and try to understand the Bregman hard clustering algorithm. Thirion et al. MMBIA Serdar will lead the discussion on the multi-modal not in a classical sense nature of atlases:.

Sobolev Active Contours: sobolevactivecontours. We will continue our asymptotic quest. Here is my last version of summary: Reading: summarydoob. Reading: doob Papoulis pp. See also his statistics chapter pp. The original papers for the asymptotics of the likelihood ratio are:. We also made a note that we need to look into asympotic statistics results mentioned in the paper in the future. Kinh might take a lead on that.

Penny W. Modelling functional integration: a comparison of structural equation and dynamic causal models. We will continue our discussion on effective connectivity in neuroimaging. We will focus on the second half of the first paper from last time Friston If time is allowed, we will discuss the third paper from last time as well Friston We will discuss functional and effective connectivity in neuroimaging. The first paper is the most general paper.

The second paper is a book chapter version of the first paper on functional connectivity and goes a bit deeper. The third paper deals with more exotic topic in effective connectivity. We will mainly discuss the first paper, so if you have limited time, the first one is the paper to read. Functional and effective connectivity in neuroimaging: A synthesis.

Human Brain Mapping, 2, Psychophysiological and modulatory interactions in Neuroimaging. NeuroImage, 6, We will start the meetings with two papers that Wanmei claims use the same model in two somewhat unrelated applications. Both papers consider the fundamental problem of evidence integration from independent sources.

We will discuss the details of the models and the relationship between them. If you have time to read just one paper, the first one is lighter. Genovese, C. The longer paper discusses the method in details, and will be the basis for our discussion. The shorter one is a nice overview. We will discuss the Dirichlet process mixture model, as presented in Teh 04, , which develops a variant of the DP mixture for grouped data. Other papers all cited by Teh are provided here for those interested in a deeper theoretical background: Ferguson 73 and Antoniak 74 are the seminal papers with fairly technical measure-theoretic proofs , while Sethuraman 94, followed by Ishwaran and James 01 and Ishwaran and Zarepour 02, are more constructive.

Yee Whye Teh et al. Hierarchical Dirichlet Processes teh04hdp. Hemant Ishwaran and Mahmoud Zarepour Exact and approximate sum-representations for the Dirichlet process ishwaran02exact. We will continue the discussion from the last time. I posted some questions in the Discussion section. Feel free to add comments and more questions. The second paper describes the information bottleneck approach and is a background reading for the first paper.

Bertrand Thirion, Olivier Faugeras. Naftali Tishby, Fernando C. Pereira, William Bialek. The Information Bottleneck Method tishby99information. I Thomas am posting the writeups for Apr Gheorghe has also kindly provided his short writeup on EM. Happy Reading!! However, the tutorial is still nice if you have no experience with mixture fitting or EM. We will NOT go over the theorems about convergence, i.

The idea is to give everyone an intuitive feel about the concept of mixture fitting, not limiting oneself to using only gaussians or the EM technique. Michael Collins michaelcollinstutorialonem. And then move onto the Genovese et al. Manly, D. Nettleton, and J. Benjamini, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing.

B 57 , Christopher R. Genovese, Nicole A. Lazar, Thomas Nichols. NeuroImage , Yoav Benjamini and Daniel Yekutieli. The Annals of Statistics , Vol. We also finshed the proof that the sample mean and the sample variance are independent for Gaussian iid case.

Thomas E. Nichols and Andrew P. Human Brain Mapping Notes for the group meeting. Duygu Tosun and Jerry L. Additional papers: Duygu Tosun, Maryam E. Rettmann, Jerry L. Mapping techniques for aligning sulci across multiple brains. Medical Image Analysis 8 Xiao Han, Dzung L. Pham, Duygu Tosun, Maryam E.

Rettmann, Chenyang Xu, and Jerry L. NeuroImage 23 Also on the list for future reading: Thompson, P. Toga, A. A surface-based technique for warping three-dimensional images of the brain. Bruce Fischl, Martin I. Sereno and Anders M. Sereno, Roger B. Tootell, Anders M. High-resolution inter-subject averaging and a coordinate system for the cortical surface. Anders M. Dale, Bruce Fischl and Martin I. Cortical Surface-Based Analysis: I.

Segmentation and Surface Reconstruction. Neuroimage, 9 2 , We will continue the discussion on the permutation tests. The future meetings will be shifted by one week. Timothy B. Terriberry, Sarang C. Joshi, and Guido Gerig. Hypothesis Testing with Nonlinear Shape Models. Blair, R. A study of multivariate permutation tests which may replace Hotelling T2 test in prescribed circumstances. Multivariate Behavioral Research 29 Pesarin, Fortunato Multivariate permutation tests : with applications in biostatistics.

Ain A. Blei, A. Ng, and M. Latent Dirichlet allocation. Journal of Machine Learning Research, 3, , Additional papers: Thomas Hofmann. Probabilistic Latent Semantic Analysis. UAI Josef Sivic, Bryan C. Russell, Alexei A. Efros, Andrew Zisserman, William T. Discovering objects and their location in images.

Mert R. Sabuncu and Peter J. Gradient based optimization of an EMST registration function. Graph theoretic image registration using prior examples. Ma, A. Hero, J. Gorman and O. Hero, B. Ma, O. Michel and J. Applications of entropic spanning graphs. Beirlant, J. Nonparametric entropy estimation: An overview. International Journal of the Mathematical Statistics Sciences, 6, , A minimum description length approach to statistical shape modeling.

Davies, R. Erik Learned-Miller. Efficient population registration of 3D data. Rissanen, J. Stochastic Complexity and Modeling. Annals of Statistics, Vol 14, , Schwarz, G. Estimating the dimension of a model. Annals of Statistics, 6, Akaike, H.

A new look at the statistical model identification. Carole J. Twining, Stephen Marsland, and Chris Taylor. BMVC Carole Twining and Stephen Marsland. First meeting, general intros. Internal Link. April 23th. April 9th. We meet on Fridays pm in D Feel free to add papers to the paper stack. To join the reading group, feel free to: subscribe to v-golland email list at csail.

To get access to the wiki, please contact Clinton Wang at clintonw at csail. Spectral kernels for probabilistic analysis and clustering of shapes; Folgoc et al. A multi-armed bandit to smartly select a training set from big medical data; Becker et al. X-Ray in-depth decomposition: revealing the latent structures; Albarqouni et al.

Towards automatic semantic segmentation in volumetric ultrasound; Yang et al. The active atlas: combining 3D anatomical models with texture detectors; Chen et al. Robust nonrigid registration through agent-based action learning; Krebs et al. Learning and incorporating shape models for semantic segmentation; Ravishankar et al.

A survey on slice to volume reconstruction: Slice-to-volume medical image registration: a survey by Ferrante et al. Deep Compressed Sensing Wu et. Gabriel Maicas, Andrew P. Bradley, Jacinto C. Interpreting deep visual representations via network dissection. The subscript i corresponds to a specific window occurrence as the window is scanned through the image space. Prior to scanning, the hearts were washed under running cold water and then immersed in water bath for 15 minutes to hydrate the myocardium.

Three ex-vivo porcine hearts were used to evaluate the effectiveness of the filtering technique in preserving abnormal myocardial fiber orientation in MI-induced hearts. Infarction was created using ethanol injection in the left anterior descending artery LAD [ 29 ]. Post-infarction the animals were monitored for three weeks to allow maximum fibrosis and remodeling in the infarcted region [ 30 ].

Prior to scanning, the same hydration procedure performed in the healthy hearts was followed in the MI-induced hearts. A two-dimensional bipolar diffusion-weighted echo planar imaging sequence was used to acquire multi-slice short-axis views of the heart. Number of slices acquired ranged anywhere between 32 and 42, based on the individual heart size.

The average time required to implement AAGF was 0. The acquired images were masked to segment the LV. A pixel from the edge was left out of the mask from both the epicardium and endocardium to avoid boundary effects caused due to air-tissue interface. Diffusion tensors were generated for all 21 acquisitions shown in Table 1 and subsequently diagonalized to determine the eigenvalues and eigenvectors.

All units were defined based on pixel length. Several 3D filter window-lengths were explored on a randomly selected heart to find the optimal filter length. The same optimal window length was used for the isotropic filters. The mean normalized RMSE for unfiltered and filtered maps for each DED-NEX setting was estimated and plotted in the same figure to compare the performance of our proposed filtering technique.

Additionally, narrow limits of agreement from the Bland Altman's plot were taken into account to assess agreement between HA AAGF and reference standard maps. Three ROIs were investigated in the diseased myocardium; one corresponded to the infarcted area ROI 1 , the second corresponded to a remote region on the same slice as the infarct ROI 2 , and the third ROI 3 corresponded to a slice remote from the site of the infarct Fig 1e.

Mean and SD for error in HA was determined in the 3 ROIs from the percentage difference maps and the result was used to evaluate the sensitivity of the filter to pathology influenced remodeling. Optimization of filter length was performed by randomly selecting one porcine heart for all DED-NEX combinations, which was then used as a sample representation for further analysis in all the other hearts. These maps represent the trend observed in all the other hearts.

For comparing the performance of each filter a NEX vs normalized RMSE graph for the entire heart in 9 animals was plotted Fig 3 a-c and the mean shown as colored lines on the plot from all animals for each NEX was connected. A further assessment was performed on the central slices of the myocardium where transition in HA both in plane and through plane was subtle and less critical compared to the apex.

Normalized RMSE estimation was performed for only 5 mid-ventricular slices and again the mean was generated for all the 9 animals Fig 3 d-f. We observed that the normalized RMSE for all NEX in the five mid-ventricular slices was lower than the normalized RMSE from the entire heart suggesting that the filter functioned better in the mid-ventricular slices. The different markers represent normalized RMSE from each animal.

The solid line corresponds to the reduced major axis and the dashed line corresponds to the line of perfect concordance. Three line profiles from a slice of the apex, mid and base sections of the LV with 12 DED acquisition in an animal is shown in Fig 5. The line profiles in the apex, mid-ventricle and base shows smooth transition from the epicardium to the endocardium. From the figure we also observe that the filtered profiles in all the cases are a good approximation of the unfiltered counterparts obtained from the reference standard.

Similar trends were observed in all the other animals. Therefore, we can conclude that the structural information of the LV obtained with higher NEX can be approximated by filtering the lower NEX principal eigenvectors with our proposed technique, thereby causing a reduction in TA.

HA maps of the slice in the a apex b mid and c base for which the profiles have been generated are shown in the top left hand corner of each image. Fig 7. An exponential regression analysis demonstrated that R 2 values in all the cases was greater than 0. The percentage difference between the AAGF data and the reference standard is shown in the 3 rd row of the figure. This animal had a prominent infarct in the septal wall of the apex, which extended into the mid-ventricular region.

As mentioned earlier, 3 distinct ROIs were investigated. Since the infarct was observed in the septal wall area approximated in TTC staining, confirmed by myocardial wall thinning in MRI scout images , ROI 1 corresponds to the infarct region on the septal wall as shown by the red contour. On the same slice a region ROI 2 was selected on the free-wall far from the infarct denoted by the purple contour. A third region remote to the site of the infarct was defined in a basal slice as indicated by the green contour.

From ROI 1 we can conclude that the infarcted region demonstrated a distinct loss in endocardial layer and mid-myocardial layer, as majority of the myocardium within the red contour infarcted region has HA values corresponding to the epicardial layer. ROI 2 had the usual trend of a smooth transition of HA from the endocardium to the epicardium indicating that this region was not affected by the remodeling process caused due to MI.

HA transition in ROI 3 was consistent with that from a healthy heart suggesting that the basal slices were unaffected by the pathological changes. Error map is very uniform within the three different ROI under investigation.

Our results demonstrate that a potential alternative approach towards improving LV HA estimation from diffusion tensor imaging is to apply post-processing AAGF on the principal eigenvectors. Applying AAGF allows a significant reduction in scan time by making additional image acquisition unnecessary. The average time required to implement AAGF is approximately 0.

The locally modified anisotropy of the AAGF is an important improvement over isotropic mean or median filtering because it reduces transmural blurring. This conservative smoothing scheme preserves pathological anomalies as demonstrated by the feasibility study in a MI model. We observed that the RMSE estimate was higher for the entire heart as compared to the central slices. This higher error can be attributed to the characteristic spiral geometry that forms a partial loop-8 structure at the apex [ 33 , 34 ].

Furthermore, the number of pixels available in the apical slices was fewer compared to the mid-ventricular slices and hence the same straight line approximation of the Gaussian function that works in the mid-ventricular regions may fail for the apical slices.

This can be partially resolved by modifying AAGF, and making it adaptive such that the filter specifications are varied as a function of the axial length. That is, the window size would increase with increasing distance from the apex to the base of the heart and the radial variation of the Gaussian function would also be dependent on location of the window on the long axis.

However, this investigation is beyond the scope of this paper. Most of the anisotropic DTI filters in the literature have been developed for brain applications. Since white matter tracts are long and relatively straight fibrous structures, these filters are not suited to encounter the curved myocardial anatomy. The AAGF approach uses the curvature of the myocardium and the organization of fibers to implement a spatially dependent filter shape that adapts to the orientation and curvature of the fiber.

However, its application is not restricted to HA estimation, but can potentially be extended to all myocardial DTI metrics derived from the primary eigenvectors. For example, the filtered eigenvectors could be used for myocardial fiber tracking applications. To the best of our knowledge, there is no gold standard for the ideal DED-NEX combination in literature and all protocols are built on individual applications restricted by TA. This conclusion was drawn from the mean of 9 ex-vivo porcine hearts.

Individual analysis of the heart yielded the same results ensuring that the conclusions were not biased by the effect of a single myocardium. However, since a previous study has shown that at least 30DED is necessary for fiber tracking applications [ 35 ], if the application of AAGF is further extended for fiber tracking, additional investigation is necessary to analyze the effect of the filtering technique on fiber tracts.

However, with respect to in-vivo cardiac DTI sequences available in literature [ 36 - 39 ], we can analyze the potential impact of the filtering technique on improving DTI vector estimates and scan time. The motion compensated spin echo approach [ 38 ] used 6DEDNEX which takes 7 mins at an average heart rate of 60 bpm.

Therefore, assuming that the same trend prevails for in-vivo imaging, acquiring so many averages may not be necessary thereby decreasing the scan time considerably. Literature reports contradictory effect on HA transitions as a result of MI. We have not noticed any specific trend as such in the three MI models we investigated; exploring further on the trend of HA due to MI remodeling is beyond the scope of this work.

However, this study ensured that the filtering technique proposed could well preserve the abnormal fiber orientation observed in a diseased myocardium. In the 3 MI-induced myocardium examined here, anomalies noticed in pre-filtered reference HA maps existed in post-filtered maps. There are a few limitations in our study. First, using histology as the gold standard to validate HA was not feasible since the hearts were borrowed from another study.

However, range of HA observed in our study and the trend of HA transition from epicardium left-handed helix to endocardium right-handed helix is consistent with the literature [ 16 , 42 ] which has been previously validated histologically [ 9 ]. However, since the reference standard between the unfiltered and filtered image was kept constant, the results obtained in this study is unbiased.

Fourth, lower spatial resolution which was limited by our maximum acquisition time might have compromised the true estimation of HA at the apex due to sharp turn angles. Fifth, it is known that formalin-fixation causes de-hydration in tissues, which was mitigated by soaking the hearts in a water-bath for 15 minutes prior to each scan [ 43 ]. Finally, since all the acquisitions were performed in one scan session, signal obtained from later acquisitions can vary due to loss of moisture content in the sample, however it was compensated by repeatedly spraying saline water on the tissue.

The filtering technique could also successfully preserve pathological differences. We also thank Siemens Healthcare for supporting this project by providing us with the required pulse sequence. Conflict of Interest: The authors have no conflicts of interest to disclose.

Ethical Approval: All animal procedures were performed in accordance with the university's institutional animal care and use committee guidelines. Informed Consent: Not applicable. Int J Cardiovasc Imaging. Author manuscript; available in PMC Jun 1. Author information Copyright and License information Disclaimer. RM: ude. AK: ude. Copyright notice. The publisher's final edited version of this article is available at Int J Cardiovasc Imaging. Open in a separate window. Fig 1. Schematic of filter design and analysis a Local coordinate system for each voxel, defining the orientation of the Gaussian filter for that particular voxel.

Preparation of MI-Induced Myocardium to Evaluate Sensitivity of Filter Three ex-vivo porcine hearts were used to evaluate the effectiveness of the filtering technique in preserving abnormal myocardial fiber orientation in MI-induced hearts. Image Analysis The acquired images were masked to segment the LV. Fig 2. Fig 3.

Fig 4. Fig 5. Fig 6. Fig 8. HA maps and error profiles for infarcted myocardium.

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