[ITK-users] Insight-users Digest, Vol 163, Issue 10

Erin McKay

2017-11-26 00:34:04 UTC

Not without an adapter, but my Mac Pro might be able to

Erin McKay

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1. Re: watershed on surface meshs (Richard Beare)
2. Re: watershed on surface meshs (Richard Beare)
3. U-Net: Convolutional Networks for image segmentation
(Abdelkhalek Bakkari)
----------------------------------------------------------------------
Message: 1
Date: Sat, 25 Nov 2017 07:53:49 +1100
Subject: Re: [ITK-users] watershed on surface meshs
Content-Type: text/plain; charset="utf-8"
I don't recall an implementation of this anywhere. However you may be able
to hack something together reasonably quickly. Watersheds are built around
minimal paths, so depending on what complexity is available in the
mesh-based shortest path tools. In the worst cast you could create a copy
of the mesh for each seed region, compute the minimal distance to each non
seed vertex, then do a vertex-wise min, tracking which mesh/seed region has
the minimum, and that's your label. Not very efficient because you compute
the entire mesh distance each time, and no obvious way to do a watershed
line consistently, but perhaps that doesn't matter for testing the idea.
On Fri, Nov 24, 2017 at 10:26 PM, Grothausmann, Roman Dr. <

Dear mailing list members,
I need to separate a mesh at "curved corners" (see attached PNG, using the
colored labels from a facet analysis do not suffice but go in the right
direction). So my current thought is to run vtkCurvature to get a Gaussian
curvature value per point/vertex and then try to separate regions of
positive values around local maxima. Just thresholding the result of
vtkCurvature does not fully separate each local max from neighboring ones,
but to my understanding a surface watershed would. I found two publications
http://teacher.en.rmutt.ac.th/ktw/Resources/Full%20paper%20P
DF/Partitioning%203D%20surface%20meshes%20using%20watershed%
20segmentation.pdf
https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd
=4&cad=rja&uact=8&ved=0ahUKEwjD0by1lafWAhVUGsAKHZ2MAbUQFgg_
MAM&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%
2Fdownload%3Fdoi%3D10.1.1.464.2788%26rep%3Drep1%26type%
3Dpdf&usg=AFQjCNGX-p9-ElQFcpsUyBRO0pCjBKCmNg
Does anybody know about an implementation for this in VTK/ITK or another
open-source library? If not, would it be possible to transfer the ITK
watershed implementation for voxel data to mesh data, e.g. to crate a
VTKmorphWatershedFilter?
Thanks for any help or hints.
Roman
--
Dr. Roman Grothausmann
Tomographie und Digitale Bildverarbeitung
Tomography and Digital Image Analysis
Medizinische Hochschule Hannover
Institut f?r Funktionelle und Angewandte Anatomie
OE 4120, Carl-Neuberg-Str. 1, 30625 Hannover, Deutschland
Tel. +49 511 532-2900
http://www.mh-hannover.de/anatomie.html
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Message: 2
Date: Sat, 25 Nov 2017 14:16:42 +1100
Subject: Re: [ITK-users] watershed on surface meshs
Content-Type: text/plain; charset="utf-8"
Just remembered something that may be useful for testing. I wrote the
following for doing watersheds on geospatial graphs using R/igraph.
Certainly isn't efficient, but may be useful for testing if you can turn
your mesh+curvature into something like a text file.
This was set up for allocating points on maps to destinations based on
travel time. It performed a kind of smoothing to smooth non-sensical travel
time estimates, but the basic algorithm is there. It will take some
fiddling. You could probably do something equivalent with python, probably
doing better than this priorty queue, but using much the same approach with
igraph.
library(liqueueR)
## Mostly a copy of PriorityQueue from liqueueR
StablePriorityQueue <- setRefClass("StablePriorityQueue",
contains = "Queue",
fields = list(
count = "numeric",
entries = "numeric",
priorities = "numeric",
prioritise = "function"
),
methods = list(
sort_ = function() {
order = order(priorities, entries,
decreasing = TRUE, partial = size():1)
#
data <<- data[order]
priorities <<- priorities[order]
entries <<- entries[order]
},
push = function(item, priority = NULL)
{
'Inserts element into the queue,
reordering according to priority.'
callSuper(item)
#
if (is.null(priority)) priority =
prioritise(item)
#
priorities <<- c(priorities,
priority)
entries <<- c(entries, count)
count <<- count - 1
#
sort_()
},
pop = function(N = 1) {
# 'Removes and returns head of queue
(or raises error if queue is empty).'
if (N > size()) stop("insufficient
items in queue!")
priorities <<-
priorities[-seq_len(N)]
entries <<- entries[-seq_len(N)]
callSuper(N)
},
initialize = function(prioritise =
NULL, ...) {
'Creates a PriorityQueue object.'
callSuper(...)
#
## to enforce FIFO order
count <<- 0
if (is.null(prioritise))
.self$prioritise = function(x) 0
else
.self$prioritise = prioritise
#
.self
}
)
)
igraph.watershed <- function(Gr, labelfield, unlabelled, vertexid, alltimes)
{
Gres <- Gr
## Watershed, without marking boundary (Beucher)
## 1. find all marker nodes that have a background neighbour
lablist <- which(vertex_attr(Gr, labelfield) != unlabelled)
nlist <- ego(Gr, 1, lablist)
uu <- which(map_lgl(nlist, ~any(vertex_attr(Gr, labelfield,
.x)==unlabelled)))
## uu indexes the labelled vertexes
## need indexes into all vertexes
uu <- lablist[uu]
## create priority queue
qq <- StablePriorityQueue$new()
## Insert the boundary markers
kk <- lapply(uu, qq$push, priority=0)
all.labels <- get.vertex.attribute(Gr, labelfield)
all.ids <- get.vertex.attribute(Gr, vertexid)
alltimes <- subset(alltimes, from %in% all.ids, select=c("from",
"Hospital", "minutes"))
dd <- duplicated(alltimes[, c("from", "Hospital")])
alltimes <- alltimes[!dd,]
alltimes.wide <- spread_(alltimes, key="Hospital", value="minutes")
## Make the order the same as all.ids - so now we'll be able to index by
number
rownames(alltimes.wide) <- alltimes.wide$from
alltimes.wide <- alltimes.wide[all.ids, ]
cc <- 1:ncol(alltimes.wide)
names(cc) <- colnames(alltimes.wide)
while (qq$size() > 0) {
vid <- qq$pop()
## Get the neighbours
nb <- neighborhood(Gr, 1, vid, mode="all", mindist=1)[[1]]
nlabs <- all.labels[nb]
## Are any neighbours unknown?
ul <- nlabs==unlabelled
if (any(ul)) {
this.label <- all.labels[vid]
nbb <- nb[ul]
all.labels[nbb] <- this.label
this.label.idx <- cc[this.label]
priorities <- alltimes.wide[[this.label.idx]][nbb]
kk <- map2(nbb, priorities, ~qq$push(.x, priority = .y* -1))
}
}
return(data.frame(PlaceID=all.ids, Hospital=all.labels, stringsAsFactors
= FALSE))
}
On Fri, Nov 24, 2017 at 10:26 PM, Grothausmann, Roman Dr. <

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Message: 3
Date: Sat, 25 Nov 2017 11:49:11 +0000
Subject: [ITK-users] U-Net: Convolutional Networks for image
segmentation
Content-Type: text/plain; charset="iso-8859-1"
Dear ITK users,
I want to know if there is any ITK filter or an example related to U-Net Convolutional Networks.
Thank you in advance.
Kind regards,
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