Optree: A learning-based adaptive watershed algorithm for neuron segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8673 LNCS, Issue PART 1, 2014, Pages 97-105

  Uzunbaş, Mustafa Gökhan ^a   Chen, Chao ^a   Metaxsas, Dimitris ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

Conditional Random Field; EM Segmentation; User Interaction; Watershed

Indexed keywords

ALGORITHMS; FORESTRY; IMAGE SEGMENTATION; MERGING; RANDOM PROCESSES; WATERSHEDS; MEDICAL COMPUTING; MEDICAL IMAGING; TREES (MATHEMATICS);

CONDITIONAL RANDOM FIELD; INTERACTIVE SEGMENTATION; MAXIMUM A POSTERIORI; NEURON STRUCTURE; STATE-OF-THE-ART METHODS; UNDERLYING GRAPHS; USER INTERACTION; WATER-SHED ALGORITHM;

TREES (MATHEMATICS); IMAGE SEGMENTATION;

ALGORITHMS; FORESTRY; IMAGE ANALYSIS; RANDOM PROCESSES; WATER SHEDS; SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CELL CULTURE; COMPUTER ASSISTED DIAGNOSIS; ELECTRON MICROSCOPY; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; NERVE CELL; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING; ULTRASTRUCTURE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CELLS, CULTURED; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MICROSCOPY, ELECTRON; NEURONS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84906987290     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10404-1_13     Document Type: Conference Paper

References (13)

1. ISBI Challenge, http://brainiac2.mit.edu/isbi-challenge/leaders-board
2. Andres, B., Kroeger, T., Briggman, K.L., Denk, W., Korogod, N., Knott, G., Koethe, U., Hamprecht, F.A.: Globally optimal closed-surface segmentation for connectomics. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 778-791. Springer, Heidelberg (2012)
3. Cardona, A., Saalfeld, S., Preibisch, S., Schmid, B., Cheng, A., Pulokas, J., Tomancak, P., Hartenstein, V.: An integrated micro-and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy. PLoS biology 8(10) (2010)
4. Chklovskii, D.B., Vitaladevuni, S., Scheffer, L.K.: Semi-automated reconstruction of neural circuits using electron microscopy. Current Opinion in Neurobiology 20(5), 667-675 (2010)
5. Ciresan, D., Giusti, A., Schmidhuber, J., et al.: Deep neural networks segment neuronal membranes in electron microscopy images. In: Advances in Neural Information Processing Systems, vol. 25, pp. 2852-2860 (2012)
6. Jain, V., Seung, H.S., Turaga, S.C.: Machines that learn to segment images: a crucial technology for connectomics. Current Opinion in Neurobiology 20(5), 653-666 (2010)
7. Knott, G., Marchman, H., Wall, D., Lich, B.: Serial section scanning electron microscopy of adult brain tissue using focused ion beam milling. The Journal of Neuroscience 28(12), 2959-2964 (2008)
8. Kroeger, T., Mikula, S., Denk, W., Koethe, U., Hamprecht, F.: Learning to segment neurons with non-local quality measures. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 419-427. Springer, Heidelberg (2013)
9. Najman, L., Schmitt, M.: Geodesic saliency of watershed contours and hierarchical segmentation. PAMI 18(12), 1163-1173 (1996)
10. Nunez-Iglesias, J., Kennedy, R., Parag, T., Shi, J., Chklovskii, D.B.: Machine learning of hierarchical clustering to segment 2d and 3d images. PLoS ONE 8 (2013)
11. Ravi Kiran, B., Serra, J.: Global-local optimizations by hierarchical cuts and climbing energies. Pattern Recognition 47(1), 12-24 (2014)
12. Sommer, C., Straehle, C., Koethe, U., Hamprecht, F.A.: Ilastik: Interactive learning and segmentation toolkit. In: 8th IEEE Int. Symposium (ISBI) (2011)
13. Turaga, S.C., Briggman, K.L., Helmstaedter, M., Denk, W., Seung, H.S.: Maximin affinity learning of image segmentation. In: NIPS, pp. 1865-1873 (2009)