HPC for Image Processing Algorithms, Current work & perspectives

Transcription

1 HPC for Image Processing Algorithms, Current work & perspectives Mauricio Cerda Postdoc Fondecyt, Faculty of Medicine Universidad de Chile Fondecyt

2 Motivation What king of data generates optical microscopes?: - Stacks of images (x,y,z) ~ GB - Time series of stacks (x,y,z,t) ~ TB - Multiple channels stacks (x,y,z,t,c) ~ TB How we extract information in these volumes?: 1.- Standards algorithms 2.- New (adapted) algorithms

3 Microscopy Analysis methods Pre processing De convolution, contrast enhancement, anisotropic diffuaion Segmentation parametric and implicit boundary models, snakes, AASM, subjective boundaries, Skeletonization mesh contraction, point clouds, Motion estimation tracking, optical flow High level image analysis tasks Level Sets PDEs, diffusion, deformation, Distance Maps intra and inter region, metrics, PDEs analytical, discrete, variacional, finite differences, elements, volumes, Meshing marching cubes, ROI stacking, refinement control, Mid level mathematical-computational methods Adaptive Strategies boundary vol hierarchy, space partition, surface fairing, mesh collapse, compressed level sets, Solvers gradient methods, multigrid, matrix decomposition, cellular automata, Lattice Boltzmann, Graph Algorithms graph sampling, shortest path, minimum spanning tree, Low level numerical & combinatorial algorithms High Performance Computing cluster, multicore processing, GPUs, Computing resources

4 Microscopy Analysis methods Pre processing De convolution, contrast enhancement, anisotropic diffuaion Skeletonization mesh contraction, point clouds, High level image analysis tasks Mid level mathematical-computational methods High Performance Computing cluster, multicore processing, GPUs, Computing resources

5 1.- Standard algorithms: deconvolution A common pre-processing step in microscopy imaging is deconvolution A B Parapineal nucleus in zebrafish embrio brain. Scale bar 10 um (spinning disc) -Deconvolution algorihtms estimate B from observed data A + At the lab we use Maximum Likelihood Expectation estimation. + It takes minutes per stack I(x,y,z,t) + Fac. Medicine has 5 dedicated deconvolution servers (BNI).

6 HPC Deconvolution - Deconvolution is highly parallelizable! Node 0 Node 1 Node N Input Output -Move/store to & from cluster to Fac. Medicine (~48 min to move 2.4 GB!!) - To provide simple pipelines for Biologist (engineers work!)

7 HPC Deconvolution (levque) Input stack Estimated deconv. (Levque-CMM) In current cluster (Levque): 1 512x512x64 stack (16 MB) -> 2 [m] x512x64 stacks (2.4 GB) -> 20* [m] * Exact deconv.

8 2.- New algorithms: skeletons In large biological structures (e.g. neurons) is commonly required to quantify geometry and topology. Electroporated neuron with m-charry from the parapineal nucleus of zebrafish brain (spinning disc) + Open problem with multiple algorithms. + We propose a modified algorithm at the lab [Alcayaga 2012]. - It takes 1 week per stack (forget about time series!!!!).

9 HPC skeletons: optimization Our skeleton algorithm is composed of 3 steps: Input mesh Output skeleton Step 1-14 [s] Step 2-3 [hours] Step 3-1 [s] - Before thinking about HPC we realize that optimizing demanding steps was crucial (hours in a very simple case).

10 HPC skeletons: optimization The most demanding step: assign a cost (F) for each edge (m) in the mesh select lowest cost edge collapse the edge -Implemented in O(m 2 log(m)) [Alcayaga 2012] - We have shown that it requires only O(m log(m)) operations [Rojas 2014]. - In neurons its represents a 98% time reduction, i.e. from 3 hours to 2 minutes

11 HPC skeletons: parallelization - Can HPC further improve computation time? - One direct approach is to parallelize operations (like step 1) Step 1: Move vertices to minimize volume As function of near vertices

12 HPC skeletons: parallelization - Vertex displacement for the complete mesh can be written as a matrix product. Vertices vector A sparse matrix k iterations - Parallelization provides a 50% in a GPU (what we have at the lab ). - Going from weeks to hours make the method practical to use. - Delivers new insights of microcopy images for biologists.

13 Summary - Common image processing methods can use HPC to reduce computation time and infrastructure (Fac. Medicine). - The big challenge for the lab is to provide friendly interfaces to biologist. - Optimization and parallelization of computationally expensive algorithms, like skeleton, can deliver new insights to biologists. - These methods are not available without using HPC, but they require adaptation.

14 SCIAN-Lab LEO-Lab Prof. Steffen Hartel (PI) Miguel Concha Dr Omar Ramírez Carmen Lemus Dr Mauricio Cerda Karina Palma Dr Víctor Castañeda Dr German Reig Dr (c) Jorge Jara Dr (c) Rodrigo Rojas Couve-Lab Andres Couve Ca-Signalling-Lab Dr (c) Violeta Chang Cecilia Hidalgo Dr (c) Raquel Pezoa Cell Communication-Lab MS (c) Alejandra García Lisette Leyton MS Susana Vargas MS Pamela Weber MS Sandra de la Fuente Felipe Santibáñez Luis Briones Jorge Mansilla Carolina Figueroa DCC-FCFM Nancy Hitschfeld Ivan Rojas DIM/CMM-FCFM Alejandro Maass / Takeshi Asahi Facultad de Ciencias Uruguay/Montevideo Pablo Zunino / Paula Scavone Gesine Schlapp / Pablo Liddle Franco Simini / Gregory Randall F. Lecumberry /Juan Cardelino Leonel Malacrida Germany/Goettingen Christoph Schmidt / Joerg Enderlein Germany/Bonn Ulrich Kubitscheck Erika Labbé Christian Gonzales Martin Pinuer Alex Cordova Argentina /Córdoba Bruno Maggio / Laura Fanani Denmark/Odense Luis Bagatolli UK/London Claudia Linker