Xavier Pennec Univ. Côte d’Azur and Inria, France

Geometric Statistics for

Freely adapted from “Women teaching geometry”, in Adelard of Bath translation of Euclid’s elements, 1310.

ACPMS seminar 28/05/2021

ERC AdG 2018-2023 G-Statistics Science that studies the structure and the relationship in From anatomy… space of different organs and tissues in living systems [Hachette Dictionary]

2007 Visible Human Project, NLM, 1996-2000 Voxel-Man, U. Hambourg, 2001

1er cerebral atlas, Vesale, 1543 Talairach & Tournoux, 1988 Paré, 1585

Vésale (1514-1564) Sylvius (1614-1672) Gall (1758-1828) : Phrenology Galien (131-201) Paré (1509-1590) Willis (1621-1675) Talairach (1911-2007)

Revolution of observation means (1980-1990):  From dissection to in-vivo in-situ imaging  From the description of one representative individual to generative statistical models of the population

X. Pennec - ACPMS seminar 28/05/2021 2 From anatomy… to Computational Anatomy

Methods to compute statistics of organ shapes across subjects in species, populations, diseases…  Mean shape (atlas), subspace of normal vs pathologic shapes  Shape variability (Covariance)

 Model development across time (growth, ageing, ages…) Use for personalized medicine (diagnostic, follow-up, etc)

 Classical use: atlas-based segmentation X. Pennec - ACPMS seminar 28/05/2021 3 Methods of computational anatomy

Remodeling of the right ventricle of the heart in tetralogy of Fallot

 Mean shape  Shape variability

 Correlation with clinical variables

 Predicting remodeling effect

Shape of RV in 18 patients

X. Pennec - ACPMS seminar 28/05/2021 4 Diffeomorphometry: Morphometry through Deformations

Atlas

φ1

φ5 Patient 1 Patient 5 φ φ 4 φ2 3

Patient 4 Patient 3 Patient 2

Measure of deformation [D’Arcy Thompson 1917, Grenander & Miller]  Observation = “random” deformation of a reference template  Reference template = Mean (atlas)  Shape variability encoded by the deformations

Statistics on groups of transformations (Lie groups, )?

X. Pennec - ACPMS seminar 28/05/2021 5 Atlas and Deformations Joint Estimation

Method: LDDMM to compute atlas + PLS on momentum maps  Find modes that are significantly correlated to clinical variables (body surface area, tricuspid and pulmonary valve regurgitations).  Create a generative model by regressing shape vs age (BSA)

Average RV anatomy of 18 ToF patients 10 Deformation6 modes significantly Modes = 90% correlated of spectral to BSA energy

[ Mansi et al, MICCAI 2009, TMI 2011] X. Pennec - ACPMS seminar 28/05/2021 6 Statistical Remodeling of RV in Tetralogy of Fallot [ Mansi et al, MICCAI 2009, TMI 2011]

Predicted remodeling effect … has a clinical interpretation

Valve Pulmonary RV Septum RV free- Volume annuli stenosis pressure pushed wall increases deform reduces decreases inwards outwards

[ Mansi et al, MICCAI 2009, TMI 2011] X. Pennec - ACPMS seminar 28/05/2021 7 Geometric features in Computational Anatomy

Non-Euclidean geometric features  Curves, sets of curves (fiber tracts)  Surfaces  Transformations

Modeling statistical variability at the level  Simple Statistics on non-linear ?  Mean, covariance of its estimation, PCA, PLS, ICA

X. Pennec - ACPMS seminar 28/05/2021 8 Geometric Statistics for Computational Anatomy

Motivations

Simple statistics on Riemannian manifolds

Extension to transformation groups with affine spaces

Longitudinal modeling with parallel transport

Perspectives, open problems

X. Pennec - ACPMS seminar 28/05/2021 9 Which non-linear space?

Constant curvatures spaces

 Sphere,

 Euclidean,

 Hyperbolic

Homogeneous spaces, Lie groups and symmetric spaces

Riemannian and affine connection spaces

Towards non-smooth quotient and stratified spaces

X. Pennec - ACPMS seminar 28/05/2021 10 Bases of Algorithms in Riemannian Manifolds Exponential map (Normal coordinate system):  Expx = shooting parameterized by the initial tangent  Logx = unfolding the in the tangent space along  Geodesics = straight lines with Euclidean distance  Geodesic completeness: covers M \ Cut(x)

Reformulate algorithms with expx and logx Vector -> Bi-point (no more equivalence classes)

Operation Euclidean space Riemannian

Subtraction xy = y − x xy = Log x (y)

Addition y = x + xy y = Expx (xy) Distance dist(x, y) = y − x dist(x, y) = xy x = −ε ∇ x = Exp (−ε ∇C(x )) Gradient descent xt+ε xt C(xt ) t+ε xt t

X. Pennec - ACPMS seminar 28/05/2021 11 First statistical tools

Fréchet mean set  Integral only valid in Hilbert/Wiener spaces [Fréchet 44]

 = = , ( ) Maurice Fréchet 2 2 (1878-1973)  𝑔𝑔 [1948]2 = global minima of Mean Sq. Dev. 𝜎𝜎Fréchet𝑥𝑥 mean𝑇𝑇𝑟𝑟 𝔐𝔐 𝑥𝑥 ∫𝑀𝑀 𝑑𝑑𝑑𝑑𝑑𝑑𝑡𝑡 𝑥𝑥 𝑧𝑧 𝑃𝑃 𝑑𝑑𝑧𝑧  Exponential barycenters [Emery & Mokobodzki 1991] = ( ) = 0 [critical points if P(C) =0]

𝔐𝔐1 𝑥𝑥̅ ∫𝑀𝑀 𝑥𝑥̅𝑧𝑧 𝑃𝑃 𝑑𝑑𝑧𝑧 Moments of a random variable: tensor fields

 = ( ) Tangent mean: (0,1) tensor field

 𝔐𝔐1(𝑥𝑥) = ∫𝑀𝑀 𝑥𝑥𝑥𝑥 𝑃𝑃 𝑑𝑑𝑧𝑧 ( ) Second moment: (0,2) tensor field  Tangent covariance field: = ( ) ( ) ( ) 𝔐𝔐2 𝑥𝑥 ∫𝑀𝑀 𝑥𝑥𝑥𝑥 ⊗ 𝑥𝑥𝑥𝑥 𝑃𝑃 𝑑𝑑𝑧𝑧  ( ) = ( ) 𝐶𝐶𝐶𝐶𝐶𝐶 𝑥𝑥 𝔐𝔐2k-𝑥𝑥contravariant− 𝔐𝔐1 𝑥𝑥 ⊗ tensor𝔐𝔐1 𝑥𝑥 field X. Pennec - ACPMS seminar 28/05/2021 𝔐𝔐𝑘𝑘 𝑥𝑥 ∫𝑀𝑀 𝑥𝑥𝑥𝑥 ⊗ 𝑥𝑥𝑥𝑥 ⊗ ⋯ ⊗ 𝑥𝑥𝑥𝑥 𝑃𝑃 𝑑𝑑𝑧𝑧 13 Estimation of Fréchet mean

Uniqueness of p-means with convex support [Karcher 77 / Buser & Karcher 1981 / Kendall 90 / Afsari 10 / Le 11]

 Non-positively curved metric spaces (Aleksandrov): OK [Gromov, Sturm]

 Positive curvature: [Karcher 77 & Kendall 89] concentration conditions: Support in a regular geodesic ball of radius < = min , / ∗ 1 𝑟𝑟 𝑟𝑟 2 𝑖𝑖𝑖𝑖𝑖𝑖 𝑀𝑀 𝜋𝜋 𝜅𝜅 Law of large numbers and CLT in manifolds  Under Kendall-Karcher concentration conditions: FM is a consistent estimator

(0, ) if = , invertible 2 −1 −1 𝐻𝐻� 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝜎𝜎 𝑋𝑋 𝑥𝑥̅𝑛𝑛 [Bhattacharya𝑛𝑛 𝑙𝑙𝑙𝑙𝑙𝑙 𝑥𝑥&̅ Patrangenaru𝑥𝑥̅𝑛𝑛 → 𝑁𝑁 2005,𝐻𝐻� Bhatt.𝛴𝛴 𝐻𝐻 �& Bhatt. 2008; Kendall & Le 2011]

 Expression for Hessian? interpretation of covariance modulation?  What happens for a small sample size?

X. Pennec - ACPMS seminar 28/05/2021 14 Taylor expansion in affine connection manifolds Key idea: use parallel transport instead of the chart to relate to

𝑇𝑇𝑥𝑥𝑀𝑀 𝑇𝑇𝑥𝑥𝑣𝑣𝑀𝑀 Gavrilov’s double exponential is a tensor series (2006): , = log (exp ( ( ) )) 1 1 exp𝑥𝑥 𝑣𝑣 =𝑥𝑥 + + 𝑥𝑥 , exp+x v 𝑥𝑥, ℎ 𝑣𝑣 𝑢𝑢 6 3 Π 𝑢𝑢 1 1 + 𝑣𝑣 𝑢𝑢 , 𝑅𝑅 𝑢𝑢 𝑣𝑣+𝑣𝑣 +𝑅𝑅 𝑢𝑢 𝑣𝑣 𝑢𝑢 , + + 5 24 24 𝛻𝛻𝑣𝑣𝑅𝑅 𝑢𝑢 𝑣𝑣 2𝑣𝑣 5𝑢𝑢 𝛻𝛻𝑢𝑢𝑅𝑅 𝑢𝑢 𝑣𝑣 𝑣𝑣 2𝑢𝑢 𝑂𝑂

Neighboring log expansion (new, XP 2019) [XP, arXiv:1906.07418 ]

, = log exp ( ) 1𝑥𝑥 1 =𝑥𝑥 + 𝑥𝑥𝑣𝑣 , 𝑥𝑥𝑣𝑣 𝑥𝑥 + , 𝑙𝑙 𝑣𝑣 𝑤𝑤 Π6 𝑤𝑤 24 1 + 𝑤𝑤 − 𝑣𝑣 ,𝑅𝑅 𝑤𝑤 𝑣𝑣 𝑣𝑣 −+2𝑤𝑤 5 𝛻𝛻𝑣𝑣𝑅𝑅 𝑤𝑤 𝑣𝑣 2𝑣𝑣 − 3𝑤𝑤 24 𝛻𝛻𝑤𝑤𝑅𝑅 𝑤𝑤 𝑣𝑣 𝑣𝑣 − 2𝑤𝑤 𝑂𝑂 X. Pennec - ACPMS seminar 28/05/2021 15 Asymptotic behavior of empirical Fréchet mean

Moments of the Fréchet mean of a n-sample  Surprising Bias in 1/n on the empirical Fréchet mean (gradient of curvature) 1 ( ) = = : : + , 1/ 5 2  No𝐛𝐛𝐛𝐛𝐛𝐛𝐛𝐛 bias in𝑥𝑥 symmetric̅𝑛𝑛 𝑬𝑬 spaces𝑙𝑙𝑙𝑙𝑙𝑙𝑥𝑥̅ 𝑥𝑥 (̅𝑛𝑛covariantly constant𝔐𝔐2 𝛻𝛻 curvature𝑅𝑅 𝔐𝔐2 ) 𝑂𝑂 𝜖𝜖 𝑛𝑛 6𝑛𝑛  Concentration rate: term in 1/n modulated by the curvature: 1 1 ( ) = = + : : + , 1/ 5 2 𝑪𝑪𝑪𝑪𝑪𝑪 Negative𝑥𝑥̅𝑛𝑛 curvature:𝑬𝑬 𝑙𝑙𝑙𝑙𝑙𝑙𝑥𝑥̅ 𝑥𝑥faster̅𝑛𝑛 ⊗ CV𝑙𝑙𝑙𝑙 than𝑙𝑙𝑥𝑥̅ 𝑥𝑥Euclidean,̅𝑛𝑛 prelude𝔐𝔐2 to stickiness𝔐𝔐2 𝑅𝑅 𝔐𝔐2 𝑂𝑂 𝜖𝜖 𝑛𝑛  Positive curvature: slower CV than Euclidean,𝑛𝑛 prelude to3 𝑛𝑛smeariness

LLN / CLT in manifolds [Bhattacharya & Bhattacharya 2008; Kendall & Le 2011]  Under Kendall-Karcher concentration conditions: ( ) (0, ) if = , invertible 𝐷𝐷 −1 −1 2  Hessian:𝑛𝑛 𝑙𝑙𝑙𝑙𝑙𝑙𝑥𝑥 ̅ 𝑥𝑥̅𝑛𝑛 =→ 𝑁𝑁+ 𝐻𝐻 : 𝛴𝛴 𝐻𝐻+ : 𝐻𝐻 +𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 ,𝜎𝜎1/ 𝑋𝑋 𝑥𝑥̅𝑛𝑛 1 1 1 4 2  Same expansion2 𝐻𝐻� 𝐼𝐼𝐼𝐼 for 3large𝑅𝑅 𝔐𝔐 n:2 modulation12 𝛻𝛻훻 𝔐𝔐3 of𝑂𝑂 the𝜖𝜖 CV 𝑛𝑛rate by curvature [XP, Curvature effects on the empirical mean in Manifolds 2019, arXiv:1906.07418 ] X. Pennec - ACPMS seminar 28/05/2021 16 Beyond the mean: principal components?

Maximize the explained variance  Tangent PCA (tPCA): eigenvectors of covariance in generate a geodesic subspace , , , … 𝑇𝑇𝑥𝑥̅𝑀𝑀 𝐺𝐺𝐺𝐺 𝑥𝑥̅ 𝑣𝑣1 𝑣𝑣2 𝑣𝑣𝑘𝑘 Minimize the sum of squared residuals to a subspace  PGA, GPCA: Geodesic subspace , , , … [Fletcher et al., 2004, Sommer et al 2014, Huckeman et al., 2010] 𝐺𝐺𝐺𝐺 𝑥𝑥̅ 𝑣𝑣1 𝑣𝑣2 𝑣𝑣𝑘𝑘  BSA: Affine span Aff , , , … Locus of weighted exponential barycenters (geodesic simplex for positive weights) 𝑥𝑥0 𝑥𝑥1 𝑥𝑥2 𝑥𝑥𝑘𝑘 Sequence of properly embedded subspaces (flags)  AUC criterion on flags generalizes PCA [XP, AoS 2018]

[XP, Barycentric subspace analysis on Manifolds, Annals of Statistics, 2018 ]

X. Pennec - ACPMS seminar 28/05/2021 17 Statistical Analysis of the Scoliotic Spine [ J. Boisvert et al. ISBI’06, AMDO’06 and IEEE TMI 27(4), 2008 ]

Database  307 Scoliotic patients from the Montreal’s Sainte-Justine Hospital.  3D Geometry from multi-planar X-rays Left invariant Mean on 𝟏𝟏𝟏𝟏  Main translation variability is axial𝟑𝟑 (growth?) 𝑺𝑺𝑶𝑶𝟑𝟑 ⋉ 𝑹𝑹  Main rot. var. around anterior-posterior axis

X. Pennec - ACPMS seminar 28/05/2021 18 Statistical Analysis of the Scoliotic Spine [ J. Boisvert et al. ISBI’06, AMDO’06 and IEEE TMI 27(4), 2008 ] AMDO’06 best paper award, Best French-Quebec joint PhD 2009

PCA of the Covariance: • Mode 1: King’s class I or III • Mode 3: King’s class IV + V 4 first variation modes • Mode 2: King’s class I, II, III • Mode 4: King’s class V (+II) have clinical meaning

X. Pennec - ACPMS seminar 28/05/2021 19 Diffusion Tensor Imaging Covariance of the Brownian motion of water  Filtering, regularization  Interpolation / extrapolation  Architecture of axonal fibers Symmetric positive definite matrices  Cone in Euclidean space (not complete)  Convex operations are stable  mean, interpolation  More complex operations are not  PDEs, gradient descent… All invariant metrics under GL(n) = T + β β > W1 |W2 Id Tr(W1 W2 ) Tr(W1).Tr(W2 ) ( -1/n) − −  1/ 2 1/ 2 1/ 2 1/ 2 Exponential map ExpΣ (ΣΨ) = Σ exp(Σ .ΣΨ.Σ )Σ 1/ 2 −1/ 2 −1/ 2 1/ 2  Log map ΣΨ = LogΣ (Ψ) = Σ log(Σ .Ψ.Σ )Σ 2  Distance dist(Σ,Ψ)2 = ΣΨ | ΣΨ = log(Σ−1/ 2.Ψ.Σ−1/ 2 ) Σ Id X. Pennec - ACPMS seminar 28/05/2021 20 Manifold-valued image algorithms Integral or sum in M: weighted Fréchet mean

 Interpolation  Linear between 2 elements: interpolation geodesic  Bi- or tri-linear or spline in images: weighted means

 Gaussian filtering: convolution = weighted mean Σ = − 2 Σ Σ (x) min ∑i Gσ (x xi ) dist ( , i )

PDEs for regularization and extrapolation: the exponential map (partially) accounts for curvature

 Gradient of Harmonic energy = Laplace-Beltrami ∆Σ = 1 Σ Σ + ε + ε 2 (x) ε ∑u∈S (x) (x u) O( ) 2  Anisotropic regularization using robust functions Reg(Σ) = Φ ∇Σ(x) dx ∫ ( Σ(x) )

 Simple intrinsic numerical schemes thanks the exponential maps! [ Pennec, Fillard, Arsigny, IJCV 66(1), 2005, ISBI 2006] X. Pennec - ACPMS seminar 28/05/2021 23 Filtering and anisotropic regularization of DTI Raw Euclidean Gaussian smoothing

Riemann Gaussian smoothing Riemann anisotropic smoothing

X. Pennec - ACPMS seminar 28/05/2021 24 Rician MAP estimation with Riemannian spatial prior N 2 2  Sˆ  Sˆ + S (Σ)   S (Σ)Sˆ  2 MAP(Σ) = − log i exp− i i I  i i .dx + Φ ∇Σ(x) .dx ∑∫  2  2  0  2  ∫ ( Σ(x) ) i=1 σ  2σ   σ 

FA

Estimated tensors

Standard ML Rician MAP Rician [ Fillard, Arsigny, Pennec, Ayache ISBI’06, TMI 26(11) 2007 ] X. Pennec - ACPMS seminar 28/05/2021 25 Geometric Statistics for Computational Anatomy

Motivations

Simple statistics on Riemannian manifolds

Extension to transformation groups with affine spaces  The bi-invariant affine Cartan connection structure  Extending statistics without a metric  The SVF framework for

Longitudinal modeling with parallel transport

Perspectives, open problems

X. Pennec - ACPMS seminar 28/05/2021 26 Atlas

φ1 φ Diffeomorphometry 5 Patient 1 Patient 5 φ φ 4 φ2 3

Patient 2 Patient 4 Patient 3 Lie group: Smooth manifold with group structure  Composition g o h and inversion g-1 are smooth

 Left and Right translation Lg(f) = g o f Rg (f) = f o g  Natural Riemannian metric choices : left or right-invariant metrics

Lift statistics to transformation groups  [D’Arcy Thompson 1917, Grenander & Miller]  LDDMM = right invariant kernel metric (Trouvé, Younes, Joshi, etc.)

No bi-invariant metric in general for Lie groups  Incompatibility of the Fréchet mean with the group structure  Examples with simple 2D rigid transformations Is there a more natural structure for statistics on Lie groups? X. Pennec - ACPMS seminar 28/05/2021 27 Longitudinal deformation analysis Dynamic obervations

time

Patient A

Template ? ?

Patient B

How to transport longitudinal deformation across subjects?

X. Pennec - ACPMS seminar 28/05/2021 28 Basics of Lie groups

Flow of a left invariant vector field = . from identity  exists for all time 𝑋𝑋� 𝐷𝐷𝐷𝐷 𝑥𝑥  One parameter subgroup: + = . 𝛾𝛾𝑥𝑥 𝑡𝑡 𝑥𝑥 𝑥𝑥 𝑥𝑥 Lie group exponential 𝛾𝛾 𝑠𝑠 𝑡𝑡 𝛾𝛾 𝑠𝑠 𝛾𝛾 𝑡𝑡

 Definition:  = 1  Diffeomorphism from a neighborhood of 0 in g to a 𝑥𝑥 ∈ 𝔤𝔤 𝐸𝐸𝐸𝐸𝐸𝐸 𝑥𝑥 𝛾𝛾𝑥𝑥 𝜖𝜖 𝐺𝐺 neighborhood of e in G (not true in general for inf. dim)

3 curves parameterized by the same tangent vector

 Left / Right-invariant geodesics, one-parameter subgroups

Question: Can one-parameter subgroups be geodesics?

X. Pennec - ACPMS seminar 28/05/2021 29 Drop the metric, use connection to define geodesics

Affine Connection (infinitesimal parallel transport)  Acceleration = derivative of the tangent vector along a curve  Projection of a tangent space on a neighboring tangent space

Geodesics = straight lines  Null acceleration: = 0  2nd order differential equation: 𝛻𝛻𝛾𝛾̇ 𝛾𝛾̇ Normal coordinate system  Local exp and log maps Adapted from Lê Nguyên Hoang, science4all.org

[XP & Arsigny, 2012, XP & Lorenzi, Beyond Riemannian Geometry, 2019] [Lorenzi, Pennec. Geodesics, Parallel Transport & One-parameter Subgroups for Diffeomorphic Image Registration. Int. J. of Computer Vision, 105(2):111-127, 2013. ]

X. Pennec - ACPMS seminar 28/05/2021 30 Canonical Affine Connections on Lie Groups A unique Cartan-Schouten connection  Bi-invariant and symmetric (no torsion)  Geodesics through Id are one-parameter subgroups (group exponential)  Matrices : M(t) = A exp(t.V)  Diffeos : translations of Stationary Velocity Fields (SVFs)

Levi-Civita connection of a bi-invariant metric (if it exists)  Continues to exists in the absence of such a metric (e.g. for rigid or affine transformations)

Symmetric space with central symmetry =  Matrix geodesic symmetry: = exp =−𝟏𝟏( ) 𝝍𝝍 𝑺𝑺 𝝓𝝓 −1𝝍𝝍𝝓𝝓 𝝍𝝍 𝐴𝐴 [Lorenzi, Pennec. Geodesics, Parallel Transport𝑆𝑆 𝑀𝑀 𝑡𝑡& One-𝐴𝐴parameter−𝑡𝑡𝑡𝑡 Subgroups𝐴𝐴 𝐴𝐴 𝑀𝑀for −𝑡𝑡 Diffeomorphic Image Registration. Int. J. of Computer Vision, 105(2):111-127, 2013. ]

X. Pennec - ACPMS seminar 28/05/2021 31 Geometric Statistics for Computational Anatomy

Motivations

Simple statistics on Riemannian manifolds

Extension to transformation groups with affine spaces  The bi-invariant affine Cartan connection structure  Extending statistics without a metric  The SVF framework for diffeomorphisms

Longitudinal modeling with parallel transport

Perspectives, open problems

X. Pennec - ACPMS seminar 28/05/2021 32 Statistics on an affine connection space

Fréchet mean: exponential barycenters

 = 0 [Emery, Mokobodzki 91, Corcuera, Kendall 99]

 Existence & local uniqueness if local convexity [Arnaudon & Li, 2005] ∑𝑖𝑖 𝐿𝐿𝐿𝐿𝐿𝐿𝑥𝑥 𝑦𝑦𝑖𝑖

For Cartan-Schouten connections [Pennec & Arsigny, 2012]

 Locus of points x such that . = 0 −1  Algorithm: fixed point iteration ( ) ∑ 𝐿𝐿𝐿𝐿𝐿𝐿local𝑥𝑥 convergence𝑦𝑦𝑖𝑖 1 = . −1 𝑥𝑥𝑡𝑡+1 𝑥𝑥𝑡𝑡 ∘ 𝐸𝐸𝐸𝐸𝐸𝐸 � 𝐿𝐿𝐿𝐿𝐿𝐿 𝑥𝑥𝑡𝑡 𝑦𝑦𝑖𝑖  Mean stable by left / right composition𝑛𝑛 and inversion  If is a mean of and is any group element, then ( ) ( ) 𝑚𝑚 = mean 𝑔𝑔𝑖𝑖 , ℎ = mean and = mean −1 𝑔𝑔 −1 ℎ ∘ 𝑚𝑚 ℎ ∘ 𝑔𝑔𝑖𝑖 𝑚𝑚 ∘ ℎ 𝑔𝑔𝑖𝑖 ∘ ℎ 𝑚𝑚 𝑖𝑖 X. Pennec - ACPMS seminar 28/05/2021 33 Special matrix groups

Heisenberg Group (resp. Scaled Upper Unitriangular Matrix Group)  No bi-invariant metric  Group geodesics defined globally, all points are reachable  Existence and uniqueness of bi-invariant mean (closed form resp. solvable) Rigid-body transformations

 Logarithm well defined iff log of rotation part is well defined, i.e. if the Givens rotation have angles <  Existence and uniqueness with same criterion as for rotation 𝜃𝜃𝑖𝑖 𝜋𝜋 parts (same as Riemannian) SU(n) and GL(n)

 Logarithm does not always exists (need 2 exp to cover the group)  If it exists, it is unique if no complex eigenvalue on the negative real line  Generalization of geometric mean X. Pennec - ACPMS seminar 28/05/2021 34 Generalization of the Statistical Framework

Covariance matrix & higher order moments  Defined as tensors in tangent space = ( )

Σ 𝐿𝐿𝐿𝐿𝐿𝐿𝑥𝑥 𝑦𝑦 ⊗ 𝐿𝐿𝐿𝐿𝐿𝐿𝑥𝑥 𝑦𝑦 𝜇𝜇 𝑑𝑑𝑑𝑑  Matrix expression∫ changes according to the basis

Other statistical tools  Previous therorem on CLT holds  Mahalanobis distance well defined and bi-invariant  Tangent Principal Component Analysis (t-PCA)  PGA & BSA, provided a data likelihood

X. Pennec - ACPMS seminar 28/05/2021 35 Geometric Statistics for Computational Anatomy

Motivations

Simple statistics on Riemannian manifolds

Extension to transformation groups with affine spaces  The bi-invariant affine Cartan connection structure  Extending statistics without a metric  The SVF framework for diffeomorphisms

Longitudinal modeling with parallel transport

Perspectives, open problems

X. Pennec - ACPMS seminar 28/05/2021 36 The SVF framework for Diffeomorphisms Idea: [Arsigny MICCAI 2006, Bossa MICCAI 2007, Ashburner Neuroimage 2007]  Exponential of a smooth vector field is a diffeomorphism  Use time-varying Stationary Velocity Fields to parameterize deformation

•exp

Stationary velocity field Diffeomorphism

Efficient numerical algorithms  Recursive Scaling and squaring algorithm [Arsigny MICCAI 2006]  Deformation: exp(v)=exp(v/2) o exp(v/2)  Jacobian: Dexp(v) = Dexp(v/2) o exp(v/2) . Dexp(v/2)

 Optimize deformation parameters: BCH formula [Bossa MICCAI 2007]  exp(v) ○ exp(εu) = exp( v + εu + [v,εu]/2 + [v,[v,εu]]/12 + … ) where [v,u](p) = Jac(v)(p).u(p) - Jac(u)(p).v(p)

X. Pennec - ACPMS seminar 28/05/2021 38 Measuring Temporal Evolution with deformations: Deformation-based morphometry Fast registration with deformation parameterized by SVF

https://team.inria.fr/asclepios/software/lcclogdemons/ [LCC log-demons for longitudinal brain imaging. Lorenzi, Ayache, Frisoni, Pennec, Neuroimage 81, 1 (2013) 470-483 ] X. Pennec - ACPMS seminar 28/05/2021 - 39 A powerful framework for statistics

Parametric diffeomorphisms [Arsigny et al., MICCAI 06, JMIV 09]

 One affine transformation per region (polyaffines transformations)  Cardiac motion tracking for each subject [McLeod, Miccai 2013] Log demons projected but with 204 parameters instead of a few millions

expp

AHA regions Stationary velocity fields Diffeomorphism with 204 parameters [McLeod, Miccai 2013] X. Pennec - ACPMS seminar 28/05/2021 40 Geometric Statistics for Computational Anatomy

Motivations

Simple statistics on Riemannian manifolds

Extension to transformation groups with affine spaces

Longitudinal modeling with parallel transport

Perspectives, open problems

X. Pennec - ACPMS seminar 28/05/2021 42 Alzheimer’s Disease

 Most common form of dementia  18 Million people worldwide  Prevalence in advanced countries  65-70: 2%  70-80: 4%  80 - : 20%  If onset was delayed by 5 years, number of cases worldwide would be halved

X. Pennec - ACPMS seminar 28/05/2021 43 Analysis of longitudinal datasets

Single-subject, two time points Log-Demons (LCC criteria)

Single-subject, multiple time points 4D registration of time series within the Log-Demons registration: geodesic regression

Multiple subjects, multiple time points Population trend with parallel transport of SVF along inter-subject trajectories

[Lorenzi et al, IPMI 2011, JMIV 2013]

X. Pennec - ACPMS seminar 28/05/2021 44 From geodesics to parallel transport

A numerical scheme to integrate symmetric connections: Schild’s Ladder [Elhers et al, 1972]  Build geodesic parallelogramme  Iterate along the curve  1st order approximation scheme [Kheyfets et al 2000]

PN Π(u) P’N u’ P’ P1 1 λ/2 τ

P2 C τ λ/2 u u P’0 P’0 P0 P0

[ Lorenzi, Pennec: Efficient Parallel Transport of Deformations in Time Series of Images: from Schild's to pole Ladder, JMIV 50(1-2):5-17, 2013 ]

X. Pennec - ACPMS seminar 28/05/2021 45 Parallel transport along geodesics

Simpler scheme along geodesics: Pole Ladder

• Closed form expression and easy approximations for Cartan connection on Lie groups

Π(u) T’ T0 0 • What about general manifolds? −Π(u) Π(uP) 1 P’1 P’1P1 τ/2τ λ/2λ/2 C geodesic A’ C -u’ τ λ/2 λ/2 u τ/2 v P’0 P0 u u P’0 P P’0 P0 0

[ Lorenzi, Pennec: Efficient Parallel Transport of Deformations in Time Series of Images: from Schild's to pole Ladder, JMIV 50(1-2):5-17, 2013 ]

X. Pennec - ACPMS seminar 28/05/2021 46 Parallel transport along geodesics

Simpler scheme along geodesics: Pole Ladder [ XP. Parallel Transport with Pole Ladder: a Third Order Scheme in Affine Connection Spaces which is Exact in Affine Symmetric Spaces. Arxiv 1805.11436. 2018 ]

−Π(u) Numerical accuracy of one ladder step P1 • Order 4 in general affine manifolds P’1 pole(u) = ( ) + , 1 1 𝛾𝛾훾 𝑡𝑡 + Π 𝑢𝑢 , 12 𝛻𝛻𝑣𝑣𝑅𝑅 𝑢𝑢 𝑣𝑣 +5𝑢𝑢(−5)2𝑣𝑣 m 12 • Exact in symmetric𝛻𝛻𝑢𝑢𝑅𝑅 𝑢𝑢 𝑣𝑣 spaces𝑣𝑣 − 2𝑢𝑢 (transvection𝑂𝑂 )! v u P’0 P0 = exp

𝛾𝛾 𝑡𝑡 𝑃𝑃0 𝑡𝑡 𝑢𝑢

X. Pennec - ACPMS seminar 28/05/2021 47 Taylor expansion in manifolds Key idea: use parallel transport instead of the chart to relate to

𝑇𝑇𝑥𝑥𝑀𝑀 𝑇𝑇𝑥𝑥𝑣𝑣𝑀𝑀 Gavrilov’s double exponential is a tensorial series (2006): , = log (exp ( ( ) )) 1 1 exp𝑥𝑥 𝑣𝑣 =𝑥𝑥 + + 𝑥𝑥 , exp+x v 𝑥𝑥, ℎ 𝑣𝑣 𝑢𝑢 6 3 Π 𝑢𝑢 1 1 + 𝑣𝑣 𝑢𝑢 , 𝑅𝑅 𝑢𝑢 𝑣𝑣+𝑣𝑣 +𝑅𝑅 𝑢𝑢 𝑣𝑣 𝑢𝑢 , + + 5 24 24 𝛻𝛻𝑣𝑣𝑅𝑅 𝑢𝑢 𝑣𝑣 2𝑣𝑣 5𝑢𝑢 𝛻𝛻𝑢𝑢𝑅𝑅 𝑢𝑢 𝑣𝑣 𝑣𝑣 2𝑢𝑢 𝑂𝑂

Neighboring log expansion (new) [ XP, arXiv:1906.07418, 2019 ]

, = log exp ( ) 1𝑥𝑥 1 =𝑥𝑥 + 𝑥𝑥𝑣𝑣 , 𝑥𝑥𝑣𝑣 𝑥𝑥 + , 𝑙𝑙 𝑣𝑣 𝑤𝑤 Π6 𝑤𝑤 24 1 + 𝑤𝑤 − 𝑣𝑣 ,𝑅𝑅 𝑤𝑤 𝑣𝑣 𝑣𝑣 −+2𝑤𝑤 5 𝛻𝛻𝑣𝑣𝑅𝑅 𝑤𝑤 𝑣𝑣 2𝑣𝑣 − 3𝑤𝑤 24 𝛻𝛻𝑤𝑤𝑅𝑅 𝑤𝑤 𝑣𝑣 𝑣𝑣 − 2𝑤𝑤 𝑂𝑂 X. Pennec - ACPMS seminar 28/05/2021 48 Discrete ladders with approximate geodesics: nd 2 order schemes Sphere

Fanning scheme Euler forward Schild’s α=1

Schild’s α=2

Kendall shape space

Schild’s α=1

Euler

Pole ladder Sphere S Poincaré half-plane H Kendall shape space 3 [ N. Guigui, XP, Numerical2 Accuracy of Ladder2 Schemes for ParallelΣ3 Transport on Manifolds. Arxiv 2007.07585. To appear in Foundations of Computational Mathematics, 2021 ] X. Pennec - ACPMS seminar 28/05/2021 50 The Stationnary Velocity Fields (SVF) framework for diffeomorphisms

 SVF framework for diffeomorphisms is algorithmically simple  Compatible with “inverse-consistency” [Lorenzi, XP. IJCV, 2013 ]  Vector statistics directly generalized to diffeomorphisms.  Exact parallel transport using one step of pole ladder [XP arxiv 1805.11436 2018]

Longitudinal modeling of AD: 70 subjects extrapolated from 1 to 15 years

year

Extrapolated Observed Extrapolated

X. Pennec - ACPMS seminar 28/05/2021 51 Modeling Normal and AD progression

SVF parametrizing the deformation trajectory

Quadratus (control)

Mean geodesic trajectory for normal aging

Normal aging

mm/year Rutundus Mean geodesic (Reference) trajectory for AD

Addition specific Triangulus component for AD (Alzheimer)

X. Pennec - ACPMS seminar 28/05/2021 52 Study of prodromal Alzheimer’s disease Linear regression of the SVF over time: interpolation + prediction

Multivariate group-wise comparison of the transported SVFs shows ~ statistically significant differences T (t) = Exp(v (t))*T0 (nothing significant on log(det) ) [Lorenzi, Ayache, Frisoni, Pennec, in Proc. of MICCAI 2011] X. Pennec - ACPMS seminar 28/05/2021 53 Advances in Geometric Statistics

Motivations

Simple statistics on Riemannian manifolds

Extension to transformation groups with affine spaces

Conclusion

X. Pennec - ACPMS seminar 28/05/2021 54 Expx / Logx and Fréchet mean are the basis of algorithms to compute on Riemannian/affine manifolds

Simple statistics  Mean through an exponential barycenter iteration  Covariance matrices and higher order moments  Tangent PCA or more complex PGA / BSA

Efficient Discrete parallel transport using Schild / Pole ladder  Quadratic convergence in #step for general (non-closed form) geodesics

Manifold-valued image processing [XP, IJCV 2006]  Interpolation / filtering / convolution: weighted means  Diffusion, extrapolation: Discrete Laplacian in tangent space = Laplace-Beltrami

X. Pennec - ACPMS seminar 28/05/2021 55 Affine vs Riemannian connection for Lie groups

What is similar  Standard differentiable geometric structure [curved space without torsion]

 Normal coordinate system with Expx et Logx [finite dimension]

Limitations of the affine framework

 No metric (but no choice of metric to justify)  The exponential does not always cover the full group  Pathological examples close to identity in finite dimension  In practice, similar limitations for the discrete Riemannian framework What we gain with Cartan-Schouten connection

 A globally invariant structure invariant by composition & inversion  Simple geodesics, efficient computations (stationarity, group exponential)  Consistency with any bi-invariant (pseudo)-metric  The simplest linearization of transformations for statistics on Lie groups?

X. Pennec - ACPMS seminar 28/05/2021 56 http://geomstats.ai : a python library to implement generic algorithms on many Riemannian manifolds

 Mean, PCA, clustering, parallel transport…  15 manifolds / Lie groups already implemented (SPD, H(n), SE(n), etc)  Generic manifolds with geodesics by integration / optimization

 scikit-learn API (hide geometry, compatible with GPU & learning tools).

 10 introductory tutorials  ~ 35000 lines of code  ~20 academic developers  2 hackathons organized in 2020

Shild’s/pole Ladders

[ Miolane et al, JMLR 2020, in press ] Rotations-Translations SPD X. Pennec - ACPMS seminar 28/05/2021 57 Pushing the frontiers of Geometric Statistics Beyond the Riemannian / metric structure  Riemannian manifolds, Non-Positively Curved (NPC) metric spaces  Affine connection, Quotient, Stratified spaces (trees, graphs)

Beyond the mean and unimodal concentrated laws  Nested sequences (flags) of subspace in manifolds  A continuum from PCA to Principal Cluster Analysis?

Geometrization of statistics  Geometry of sample spaces [Harms, Michor, XP, Sommer, arXiv:2010.08039 ]  Stratified boundary of the smooth manifold of probability densities  Explore influence of curvature, singularities (borders, corners, stratifications) on non-asymptotic estimation theory

Make G-Statistics an effective discipline for life sciences

X. Pennec - ACPMS seminar 28/05/2021 58 Thank you for your attention

Thanks to many collaborators

• Marco Lorenzi • Jonathan Boisvert • Pierre Fillard • Vincent Arsigny • Maxime Sermesant • Nicholas Ayache • Kristin McLeod 2020, Academic Press • Nina Miolane • Loic Devillier • Marc-Michel Rohé • Yann Thanwerdas • Nicolas Guigui • …….

X. Pennec - ACPMS seminar 28/05/2021 59