{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T10:43:35Z","timestamp":1761648215468,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319104034"},{"type":"electronic","value":"9783319104041"}],"license":[{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"DOI":"10.1007\/978-3-319-10404-1_13","type":"book-chapter","created":{"date-parts":[[2014,8,31]],"date-time":"2014-08-31T12:21:17Z","timestamp":1409487677000},"page":"97-105","source":"Crossref","is-referenced-by-count":10,"title":["Optree: A Learning-Based Adaptive Watershed Algorithm for Neuron Segmentation"],"prefix":"10.1007","author":[{"given":"Mustafa G\u00f6khan","family":"Uzunba\u015f","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitris","family":"Metaxsas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"13_CR1","unstructured":"ISBI Challenge, \n                    \n                      http:\/\/brainiac2.mit.edu\/isbi_challenge\/leaders-board"},{"key":"13_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1007\/978-3-642-33712-3_56","volume-title":"Computer Vision \u2013 ECCV 2012","author":"B. Andres","year":"2012","unstructured":"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.\u00a07574, pp. 778\u2013791. Springer, Heidelberg (2012)"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"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\u00a08(10) (2010)","DOI":"10.1371\/journal.pbio.1000502"},{"issue":"5","key":"13_CR4","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1016\/j.conb.2010.08.002","volume":"20","author":"D.B. Chklovskii","year":"2010","unstructured":"Chklovskii, D.B., Vitaladevuni, S., Scheffer, L.K.: Semi-automated reconstruction of neural circuits using electron microscopy. Current Opinion in Neurobiology\u00a020(5), 667\u2013675 (2010)","journal-title":"Current Opinion in Neurobiology"},{"key":"13_CR5","unstructured":"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.\u00a025, pp. 2852\u20132860 (2012)"},{"issue":"5","key":"13_CR6","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/j.conb.2010.07.004","volume":"20","author":"V. Jain","year":"2010","unstructured":"Jain, V., Seung, H.S., Turaga, S.C.: Machines that learn to segment images: a crucial technology for connectomics. Current Opinion in Neurobiology\u00a020(5), 653\u2013666 (2010)","journal-title":"Current Opinion in Neurobiology"},{"issue":"12","key":"13_CR7","doi-asserted-by":"publisher","first-page":"2959","DOI":"10.1523\/JNEUROSCI.3189-07.2008","volume":"28","author":"G. Knott","year":"2008","unstructured":"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\u00a028(12), 2959\u20132964 (2008)","journal-title":"The Journal of Neuroscience"},{"key":"13_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1007\/978-3-642-40763-5_52","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"T. Kroeger","year":"2013","unstructured":"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.\u00a08150, pp. 419\u2013427. Springer, Heidelberg (2013)"},{"issue":"12","key":"13_CR9","doi-asserted-by":"publisher","first-page":"1163","DOI":"10.1109\/34.546254","volume":"18","author":"L. Najman","year":"1996","unstructured":"Najman, L., Schmitt, M.: Geodesic saliency of watershed contours and hierarchical segmentation. PAMI\u00a018(12), 1163\u20131173 (1996)","journal-title":"PAMI"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"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\u00a08 (2013)","DOI":"10.1371\/journal.pone.0071715"},{"issue":"1","key":"13_CR11","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.patcog.2013.05.012","volume":"47","author":"B. Ravi Kiran","year":"2014","unstructured":"Ravi Kiran, B., Serra, J.: Global\u2013local optimizations by hierarchical cuts and climbing energies. Pattern Recognition\u00a047(1), 12\u201324 (2014)","journal-title":"Pattern Recognition"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Sommer, C., Straehle, C., Koethe, U., Hamprecht, F.A.: Ilastik: Interactive learning and segmentation toolkit. In: 8th IEEE Int. Symposium (ISBI) (2011)","DOI":"10.1109\/ISBI.2011.5872394"},{"key":"13_CR13","unstructured":"Turaga, S.C., Briggman, K.L., Helmstaedter, M., Denk, W., Seung, H.S.: Maximin affinity learning of image segmentation. In: NIPS, pp. 1865\u20131873 (2009)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2014"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-10404-1_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T20:09:39Z","timestamp":1568405379000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-10404-1_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"ISBN":["9783319104034","9783319104041"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-10404-1_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2014]]}}}