Graphs |

Watershed by Flooding: Applied Data Structures

Fri, 01 May 2020 11:12:32 -0700

I’m applying some methods developed in this paper for testing purposes in my own thesis research. Specifically, I have some float-valued data, $F$, that varies along the cortical surface of the brain. Visually, I can see that there are areas where these scalar maps change abruptly. I want to identify these boundaries – eventually, I’ll segment out the regions I’m interested in.

Computing the Gradient Map

The authors use some conventional brain imaging software to compute the gradient of their data. The domain of this data is a triangulated mesh, described by the graph $G = (V, E)$, where $V$ are vertices in Euclidean space and $E$ the edges between these vertices. In short, for a vertex, $v_{i}$, we first need to compute the gradient vector of the scalar field at $v_{i}$. We “unfold” the 3D positions of adjacent vertices onto the tangent plane of $v_{i}$ – which we can do by orthogonally projecting the adjacent vertices onto the affine subspace at $v_{i}$ and then weighting appropriately. We then regress the graph signal (our scalar field) onto these unfolded positions. The $L_{2}$ norm of this vector is the gradient at $v_{i}$.

For each vertex, we have a normal vector to the surface $N$, its spatial 3D coordinates $v_{i} = (x, y, z)$, and a list of its adjacent vertices. We can compute the orthogonal projector onto the affine subspace spanned by $N$, $P_{N}$, and its orthogonal complement, $Q_{N}$, as:

$$\begin{align} P_{N} &= N(N^{T}N)^{-1}N^{T} \\ Q_{N} &= I - P_{N} \end{align}$$

For any vertex, $v_{j}$, we can compute the orthogonal projection onto the affine subspace spanned by $Q_{N}$ as:

$$\begin{align} q(v_{j}) = Q_{N}(v_{j} - v_{i}) + v_{i} \end{align}$$

We generate the vectors

$$ S_{i} = f(i) - \begin{bmatrix} f(v_{1}) \\ f(v_{2}) \\ \vdots \\ f(v_{j}) \end{bmatrix} \in \mathbb{R}^{j} \;\;\; R_{i} = \begin{bmatrix} q(v_{1}) \\ q(v_{2}) \\ \vdots \\ q(v_{j}) \end{bmatrix} \in \mathbb{R}^{j \times 3} $$

where $S_{i}$ is the difference between the scalar value at our vertex $f(v_{i})$ and the vector of adjacent $j$ scalar field values, and $R_{i}$ is the matrix of $j$ orthogonally projected adjacent vertex coordinates. Then we perform least squares regression to solve for $\beta$:

$$\begin{align} S_{i} = R_{i}\beta \end{align}$$

where $\beta \in \mathbb{R}^{3}$, which indicates how much each coordinate axis contributes to variation in the scalar field at $v_{i}$. The gradient value at vertex $v_{i} = \left || \beta \right||_{2}$.

Watershed By Flooding Algorithm

The new scalar field of $L_{2}$ norms is our gradient field, which describes how “quickly” our original data changes at each vertex. We can now apply the Watershed Algorithm to segment our mesh data. In brief, the watershed algorithm treats the gradient field as a topographic map: low-elevation areas (areas with a small gradient) are “water basins”. If we imagine water flooding this map from the bottom up, basins at low elevation will flood first, while areas at higher elevations will fill last. When water basins meet, the water has reached a “boundary” (or ridgeline, if we’re using the topographic map idea).

I’ve implemented an algorithm variant called “Priority Flooding”, using Python’s heapq priority queue data type class. The priority queue is an application of the binary heap data structure – as nodes are added to the heap, the branching process determines where to put nodes (left or right of a current node), based on some value – in the case of the priority queue, this value is the “priority”. We utilize the priority queue because it gives us a principled way to iterate over unlabeled vertices, and, with some auxiliary data structures, is guaranteed to converge. The algorithm proceeds as follows:

Identify local minima, and assign each minimum a unique label
Add directly adjacent vertices of local minima to priority queue (lower gradient -> higher priority)
While the queue is not empty, get the highest-priority item
- If vertices adjacent to this vertex have only one label, assign this vertex to that label
- Else assign this vertex as a boundary vertex
- Add unlabeled adjacent vertices to the queue

import numpy as np
from queue import PriorityQueue

class PriorityFlood(object):

 """
 Class to segment a scalar field using the Priority Flooding
 watershed algorithm.
 """

 def __init__(self):

 self.pq = PriorityQueue()

 def fit(self, gradient, A, M):

 """
 Fitting procedure for watershed algorithm.

 Parameters:
 - - - - -
 gradient: float, array
 map of gradient values of scalar field
 A: dict of lists
 adjacency list of data domain
 M: list
 local minima in scalar field used to seed algorithm
 """

 # initialize empty label vector
 labels = np.zeros((gradient.shape))
 labels[:] = np.nan

 # keep track of items in queue
 in_queue = np.zeros((gradient.shape)).astype(np.bool)

 # assign local minima unique labels
 # add their adjacent vertices to heap
 for i, loc_min in enumerate(M):

 # label local minima
 labels[loc_min] = i+1

 # add neighbors of local minima to p-queue
 for nidx in A[loc_min]:
 in_queue[nidx] = True
 self.pq.put((gradient[nidx], nidx))

 # iterate over p-queue items
 # items assigned to a label or
 # assigned as a boundary
 while not self.pq.empty():

 # get highest priority item
 # mark as not in p-queue
 [gr, idx] = self.pq.get()
 in_queue[idx] = False

 # get item neighbors and their labels
 i_neighbors = np.asarray(A[idx])
 i_nlabels = labels[i_neighbors]

 # get labels of adjacent vertices that are not nan
 nans = np.isnan(i_nlabels)
 unique_labels = np.unique(i_nlabels[~nans])

 # if more than one unique label
 # assign current vertex as border vertex
 if len(unique_labels) > 1:
 continue

 # otherwise assign to water basin
 else:

 # basin assignment
 labels[idx] = unique_labels

 # identify neighbors without labels that arent in the p-queue
 gidx = np.where((nans) & (~in_queue[i_neighbors]))[0]

 # add these to p-queue
 for nidx in i_neighbors[gidx]:
 if not in_queue[nidx]:
 in_queue[nidx] = True
 self.pq.put((gradient[nidx], nidx))

 self.labels_ = labels

Example of PriorityFlooding algorithm, applied to gradient of inferiorparietal region. Shown on inflated and flattened cortical surfaces.

One caveat that came up is that the PriorityQueue class does not check for duplicates – that is, two vertices might share an adjacent vertex, and this vertex might have already been added to the heap. In this case, we would unnecessarily view the same vertices many times. To alleviate this, we create a boolean Numpy array, in_queue, that stores whether an item is already in the queue.

Mahalanobis Distances of Brain Connectivity

Fri, 07 Dec 2018 05:12:32 -0700

For one of the projects I’m working on, I have an array of multivariate data relating to brain connectivity patterns. Briefly, each brain is represented as a surface mesh, which we represent as a graph $G = (V,E)$, where $V$ is a set of $n$ vertices, and $E$ is the set of edges between vertices.

Additionally, for each vertex $v \in V$, we also have an associated scalar label, which we’ll denote $l(v)$, that identifies what region of the cortex each vertex belongs to, the set of regions which we define as $L = {1, 2, … k}$. And finally, for each vertex $v \in V$, we also have a multivariate feature vector $r(v) \in \mathbb{R}^{1 \times k}$, that describes the strength of connectivity between it, and every region $l \in L$.

Example of cortical map, and array of connectivity features.

I’m interested in examining how “close” the connectivity samples of one region, $l_{j}$, are to another region, $l_{k}$. In the univariate case, one way to compare a scalar sample to a distribution is to use the $t$-statistic, which measures how many standard deviations away from the mean a given sample is:

$$\begin{align} t_{s} = \frac{\bar{x} - \mu}{\frac{s}{\sqrt{n}}} \end{align}$$

where $\mu$ is the population mean, and $s$ is the sample standard deviation. If we square this, we get:

$$\begin{align} t^{2} = \frac{(\bar{x} - \mu)^{2}}{\frac{s^{2}}{n}} = \frac{n (\bar{x} - \mu)^{2}}{S^{2}} \sim F(1,n) \end{align}$$

We know the last part is true, because the numerator and denominator are independent $\chi^{2}$ distributed random variables. However, I’m not working with univariate data – I have multivariate data. The multivariate generalization of the $t$-statistic is the Mahalanobis Distance:

$$\begin{align} d &= \sqrt{(\bar{x} - \mu)\Sigma^{-1}(\bar{x}-\mu)^{T}} \end{align}$$

where the squared Mahalanobis Distance is:

$$\begin{align} d^{2} &= (\bar{x} - \mu)\Sigma^{-1}(\bar{x}-\mu)^{T} \end{align}$$

where $\Sigma^{-1}$ is the inverse covariance matrix. If our $X$’s were initially distributed with a multivariate normal distribution, $N_{p}(\mu,\Sigma)$ (assuming $\Sigma$ is non-degenerate i.e. positive definite), the squared Mahalanobis distance, $d^{2}$ has a $\chi^{2}_{p}$ distribution. We show this below.

We know that $(X-\mu)$ is distributed $N_{p}(0,\Sigma)$. We also know that, since $\Sigma$ is symmetric and real, that we can compute the eigendecomposition of $\Sigma$ as:

$$\begin{align} \Sigma = U \Lambda U^{T} \end{align}$$

and consequently, because $U$ is an orthogonal matrix, and because $\Lambda$ is diagonal, we know that $\Sigma^{-1}$ is:

$$\begin{align} \Sigma^{-1} &= (U \Lambda U^{T})^{-1} \\ &= U \Lambda^{-1} U^{T} \\ &= (U \Lambda^{\frac{-1}{2}}) (U \Lambda^{\frac{-1}{2}})^{T} \\ &= R R^{T} \end{align}$$

Therefore, we know that $R^{T}(X-\mu) \sim N_{p}(0,I_{p})$:

$$\begin{align} X &\sim N_{p}(\mu,\Sigma) \\ (X-\mu) = Y &\sim N_{p}(0,\Sigma)\\ R^{T}Y = Z &\sim N_{p}(0, R^{T} \Sigma R) \\ &\sim N_{p}(0, \Lambda^{\frac{-1}{2}} U^{T} (U \Lambda U^{T}) U \Lambda^{\frac{-1}{2}}) \\ &\sim N_{p}(0, \Lambda^{\frac{-1}{2}} I_{p} \Lambda I_{p} \Lambda^{\frac{-1}{2}}) \\ &\sim N_{p}(0,I_{p}) \end{align}$$

so that we have

$$\begin{align} &= (X-\mu)\Sigma^{-1}(X-\mu)^{T} \\ &= (X-\mu)RR^{T}(X-\mu)^{T} \\ &= Z^{T}Z \end{align}$$

the sum of $p$ squared standard Normal random variables, which is the definition of a $\chi_{p}^{2}$ distribution with $p$ degrees of freedom. So, given that we start with a $MVN$ random variable, the squared Mahalanobis distance is $\chi^{2}_{p}$ distributed. Because the sample mean and sample covariance are consistent estimators of the population mean and population covariance parameters, we can use these estimates in our computation of the Mahalanobis distance.

Also, of particular importance is the fact that the Mahalanobis distance is not symmetric. That is to say, if we define the Mahalanobis distance as:

$$\begin{align} M(A, B) = \sqrt{(A - \mu(B))\Sigma(B)^{-1}(A-\mu(B))^{T}} \end{align}$$

then $M(A,B) \neq M(B,A)$, clearly. Because the parameter estimates are not guaranteed to be the same, it’s straightforward to see why this is the case.

Now, back to the task at hand. For a specified target region, $l_{T}$, with a set of vertices, $V_{T} = {v \; : \; l(v) \; = \; l_{T}, \; \forall \; v \in V}$, each with their own distinct connectivity fingerprints, I want to explore which areas of the cortex have connectivity fingerprints that are different from or similar to $l_{T}$’s features, in distribution. I can do this by using the Mahalanobis Distance. And based on the analysis I showed above, we know that the data-generating process of these distances is related to the $\chi_{p}^{2}$ distribution.

First, I’ll estimate the covariance matrix, $\Sigma_{T}$, of our target region, $l_{T}$, using the Ledoit-Wolf estimator (the shrunken covariance estimate has been shown to be a more reliable estimate of the population covariance), and mean connectivity fingerprint, $\mu_{T}$. Then, I’ll compute $d^{2} = M^{2}(A,A)$ for every $\{v: v \in V_{T}\}$. The empirical distribution of these distances should follow a $\chi_{p}^{2}$ distribution. If we wanted to do hypothesis testing, we would use this distribution as our null distribution.

Next, in order to assess whether this intra-regional similarity is actually informative, I’ll also compute the similarity of $l_{T}$ to every other region, $\{ l_{k} \; : \; \forall \; k \in L \setminus \{T\} \}$ – that is, I’ll compute $M^{2}(A, B) \; \forall \; B \in L \setminus T$. If the connectivity samples of our region of interest are as similar to one another as they are to other regions, then $d^{2}$ doesn’t really offer us any discriminating information – I don’t expect this to be the case, but we need to verify this.

Then, as a confirmation step to ensure that our empirical data actually follows the theoretical $\chi_{p}^{2}$ distribution, I’ll compute the location and scale Maximum Likelihood(MLE) parameter estimates of our $d^{2}$ distribution, keeping the d.o.f. (i.e. $p$) fixed.

See below for Python code and figures…

Step 1: Compute Parameter Estimates

%matplotlib inline
import matplotlib.pyplot as plt

from matplotlib import rc
rc('text', usetex=True)

import numpy as np
from scipy.spatial.distance import cdist
from scipy.stats import chi2, probplot

from sklearn import covariance

# lab_map is a dictionary, mapping label values to sample indices
# our region of interest has a label of 8
LT = 8

# get indices for region LT, and rest of brain
lt_indices = lab_map[LT]
rb_indices = np.concatenate([lab_map[k] for k in lab_map.keys() if k != LT])

data_lt = conn[lt_indices, :]
data_rb = conn[rb_indices, :]

# fit covariance and precision matrices
# Shrinkage factor = 0.2
cov_lt = covariance.ShrunkCovariance(assume_centered=False, shrinkage=0.2)
cov_lt.fit(data_lt)
P = cov_lt.precision_

Next, compute the Mahalanobis Distances:

# LT to LT Mahalanobis Distance
dist_lt = cdist(data_lt, data_lt.mean(0)[None,:], metric='mahalanobis', VI=P)
dist_lt2 = dist_lt**2

# fit covariance estimate for every region in cortical map
EVs = {l: covariance.ShrunkCovariance(assume_centered=False,
 shrinkage=0.2) for l in labels}

for l in lab_map.keys():
 EVs[l].fit(conn[lab_map[l],:])

# compute d^2 from LT to every cortical region
# save distances in dictionary
lt_to_brain = {}.fromkeys(labels)
for l in lab_map.keys():

 temp_data = conn[label_map[l], :]
 temp_mu = temp_data.mean(0)[None, :]

 temp_mh = cdist(data_lt, temp_mu, metric='mahalanobis', VI=EVs[l].precision_)
 temp_mh2 = temp_mh**2

 lt_to_brain[l] = temp_mh2

# plot distributions seperate (scales differ)
fig = plt.subplots(2,1, figsize=(12,12))
plt.subplot(2,1,1)
plt.hist(lt_to_brain[LT], 50, density=True, color='blue',
 label='Region-to-Self', alpha=0.7)

plt.subplot(2,1,2)
for l in labels:
 if l != LT:
 plt.hist(lt_to_brain[l], 50, density=True, linewidth=2,
 alpha=0.4, histtype='step')

Empirical distributions of within-region (top) and between-region (bottom) $d^{2}$ values. Each line is the distribution of the distance of samples in our ROI to a whole region.

As expected, the distribution of $d^{2}$, the distance of samples in our region of interest, $l_{T}$, to distributions computed from other regions, is (considerably) larger and much more variable, while the profile of points within $l_{T}$ looks to have much smaller variance – this is good! This means that we have high intra-regional similarity when compared to inter-regional similarities. This fits what’s known in neuroscience as the “cortical field hypothesis”.

Step 2: Distributional QC-Check

Because we know that our data should follow a $\chi^{2}_{p}$ distribution, we can fit the MLE estimate of our location and scale parameters, while keeping the $df$ parameter fixed.

p = data_lt.shape[1]
mle_chi2_theory = chi2.fit(dist_lt2, fdf=p)

xr = np.linspace(data_lt.min(), data_lt.max(), 1000)
pdf_chi2_theory(xr, *mle_chi2_theory)

fig = plt.subplot(1,2,2,figsize=(18, 6))

# plot theoretical vs empirical null distributon
plt.subplot(1,2,1)
plt.hist(data_lt, density=True, color='blue', alpha=0.6,
 label = 'Empirical')
plt.plot(xr, pdf_chi2_theory, color='red',
 label = '$\chi^{2}_{p}')

# plot QQ plot of empirical distribution
plt.subplot(1,2,2)
probplot(D2.squeeze(), sparams=mle_chi2_theory, dist=chi2, plot=plt);

Density and QQ plot of null distribution.

From looking at the QQ plot, we see that the empirical density fits the theoretical density pretty well, but there is some evidence that the empirical density has heavier tails. The heavier tail of the upper quantile could probability be explained by acknowledging that our starting cortical map is not perfect (in fact there is no “gold-standard” cortical map). Cortical regions do not have discrete cutoffs, although there are reasonably steep gradients in connectivity. If we were to include samples that were considerably far away from the the rest of the samples, this would result in inflated densities of higher $d^{2}$ values.

Likewise, we also made the distributional assumption that our connectivity vectors were multivariate normal – this might not be true – in which case our assumption that $d^{2}$ follows a $\chi^{2}_{p}$ would also not hold.

Finally, let’s have a look at some brains! Below, is the region we used as our target – the connectivity profiles from vertices in this region were used to compute our mean vector and covariance matrix – we compared the rest of the brain to this region.

Region of interest.

Estimated squared Mahalanobis distances, overlaid on cortical surface.

Here, larger $d^{2}$ values are in red, and smaller $d^{2}$ are in black. Interestingly, we do see pretty large variance of $d^{2}$ spread across the cortex – however the values are smoothly varying, but there do exists sharp boundaries. We kind of expected this – some regions, though geodesically far away, should have similar connectivity profiles if they’re connected to the same regions of the cortex. However, the regions with connectivity profiles most different than our target region are not only contiguous (they’re not noisy), but follow known anatomical boundaries, as shown by the overlaid boundary map.

This is interesting stuff – I’d originally intended on just learning more about the Mahalanobis Distance as a measure, and exploring its distributional properties – but now that I see these results, I think it’s definitely worth exploring further!