Bodenstein, Christian

Dickscheid, Timo

Glock, Philipp

Riedel, Morris

Spitzer, Hannah

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MitgliedderHelmholtz-GemeinschaftDeep Learning and Unsupervised Clustering forAnalysis of Cellular Structures in the Human BrainC. Bodenstein*, H. Spitzer**, P. Glock**, M. Riedel*, T. Dickscheid*** High Productivity Data Processing, Ju¨lich Supercomputing Center (JSC)** Big Data Analytics, Institute of Neuroscience and Medicine (INM-1)Brodmann Area 18 Brodmann Area 17Cytoarchitectonic MappingLayer structure differs between cytoarchitectonic areas [5]. Classical methods to locate bordersinclude image segmentation, mathematical morphology, and correlation of local intensity profiles.Goals:Investigate the potential of modernmachine learning techniques tosupport the analysisIncrease degree of automatization(towards high throughputprocessing)Find qualitative and quantitativemeasures for cellular distributionsGray/white mattersegmentationAutomatic Par-cellation ofCytoarchitectonicCortical RegionsCell detectionDensity clus-tering to reflectlaminar layersSuperviseddeep learningUnsupervisedclusteringGray/white matter segmentationGPUHPCUse Deep-CNNs [3] to predict cerebral cortex tissueHand crafted masks partially availablePost-processing to find optimal results (e.g. Graph Cut)Prediction for high resolution images parallel on HPCNext stepsIncrease classifier size / Improve post-processingEnhance masksprediction graph cutevaluateCell detectionCompute shape attributes foreach connected regionRandom Forest / SVM forpredictionNext stepsSplit cells using clustering,parallel for each labelCreate a unicore workflowHPCParcellation of cytoarchitectonic cortical regionsconv pool conv FCL33 ......111115001500V2V1Feasibility study with deep learning [3, 4] finished - promising findingsNext stepsImplement workflow on HPC to enable processing of larger amounts of dataGPUGroundtruth Classification SegmentationDensity clustering to reflect laminar layersCortical areas showdifferent cell densitiesUsing density basedclustering (DBSCAN[1]) to find regions ofdifferent densityScalable and parallelHPDBSCAN [2]implementation tocluster large number ofcellsHPCNext stepsCompare results with different clustering techniquesFind relations to cytoarchitectonic cortical regionsReferences[1] Martin Ester, Hans-Peter Kriegel, Jo¨rg Sander, and Xiaowei Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, volume 96, pages 226–231, 1996. [2] Markus Go¨tz, Christian Bodenstein, and Morris Riedel. Hpdbscan: highly parallel dbscan. InProceedings of the Workshop on Machine Learning in High-Performance Computing Environments, page 2. ACM, 2015. [3] Yann LeCun and Yoshua Bengio. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10):1995,1995. [4] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521(7553):436–444, 2015. [5] A Schleicher, Katrin Amunts, Stefan Geyer, P Morosan, and Karl Zilles. Observer-independent method for microstructural parcellation of cerebral cortex: a quantitative approach tocytoarchitectonics. Neuroimage, 9(1):165–177, 1999.Contact: c.bodenstein | h.spitzer | p.glock | m.riedel | t.dickscheid@fz-juelich.de - Website: www.fz-juelich.de

Deep Learning and Unsupervised Clustering for Analysis of Cellular Structures in the Human Brain

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Deep Learning and Unsupervised Clustering for Analysis of Cellular Structures in the Human Brain

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