{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:34:57Z","timestamp":1750221297419,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,8,15]],"date-time":"2018-08-15T00:00:00Z","timestamp":1534291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,8,15]]},"DOI":"10.1145\/3233547.3233552","type":"proceedings-article","created":{"date-parts":[[2018,8,24]],"date-time":"2018-08-24T12:05:17Z","timestamp":1535112317000},"page":"236-243","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Automated Mouse Organ Segmentation"],"prefix":"10.1145","author":[{"given":"Naveen","family":"Ashish","sequence":"first","affiliation":[{"name":"InferLink Corporation, El Segundo, CA, USA"}]},{"given":"Mi-Youn","family":"Brusniak","sequence":"additional","affiliation":[{"name":"Fred Hutch Cancer Research Center, Seattle, WA, USA"}]}],"member":"320","published-online":{"date-parts":[[2018,8,15]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1177\/0192623314555162"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00441-014-2093-4"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.4155\/bio.15.9"},{"key":"e_1_3_2_1_5_1","volume-title":"Computer Vision","author":"Shapiro G. C.","year":"2001","unstructured":"L. G. Shapiro and G. C. Stockman , Computer Vision ., Prentice Hall ., 2001 . L. G. Shapiro and G. C. Stockman, Computer Vision., Prentice Hall., 2001."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-008-0127-7"},{"key":"e_1_3_2_1_7_1","volume-title":"Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer,\" CoRR","author":"Yu","year":"2018","unstructured":"Yu .Gordienko, P. Gang , J. Hui , W. Zeng , Yu.Kochura, O. Alienin , O. Rokovyi and S. Stirenko , \" Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer,\" CoRR , p. abs\/1712.07632 , 2018 . Yu.Gordienko, P. Gang, J. Hui, W. Zeng, Yu.Kochura, O.Alienin, O. Rokovyi and S. Stirenko, \"Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer,\" CoRR, p. abs\/1712.07632 , 2018."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-42999-1_14"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24553-9_68"},{"key":"e_1_3_2_1_10_1","first-page":"277","article-title":"Convolutional networks for kidney segmentation in contrast-enhanced CT scans","author":"Thong W.","year":"2016","unstructured":"Thong , W. , Kadoury , S. , Pich\u00b4e , N. , Pal and C. J. , \" Convolutional networks for kidney segmentation in contrast-enhanced CT scans ,\" Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization , pp. 277 -- 282 , 2016 . Thong, W., Kadoury, S., Pich\u00b4e, N., Pal and C. J., \"Convolutional networks for kidney segmentation in contrast-enhanced CT scans,\" Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp. 277--282, 2016.","journal-title":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization"},{"key":"e_1_3_2_1_11_1","volume-title":"PatchBased Methods in Medical Image Processing Workshop","author":"Vivanti R.","year":"2015","unstructured":"Vivanti , R. , Ephrat , A. , Joskowicz , L. , Karaaslan , O. , Lev-Cohain , N. , Sosna and J. , \" Automatic liver tumor segmentation in followup CT studies using convolutional neural networks,\" in MICCAI : PatchBased Methods in Medical Image Processing Workshop , 2015 . Vivanti, R., Ephrat, A., Joskowicz, L., Karaaslan, O., Lev-Cohain, N., Sosna and J., \"Automatic liver tumor segmentation in followup CT studies using convolutional neural networks,\" in MICCAI: PatchBased Methods in Medical Image Processing Workshop , 2015."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-016-1501-5"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.12.061"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1097\/ICU.0000000000000470"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2795742"},{"key":"e_1_3_2_1_16_1","volume-title":"Machine Vision Algorithms and Applications","author":"Steger M.","year":"2018","unstructured":"C. Steger , M. Ulrich and C. Wiedemann , Machine Vision Algorithms and Applications ( 2 nd ed.), Weinheim : Wiley-VCH , 2018 . C. Steger, M. Ulrich and C. Wiedemann, Machine Vision Algorithms and Applications (2nd ed.), Weinheim: Wiley-VCH, 2018.","edition":"2"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"V. Duay N. Houhou and J.-P. Thiran \"ATLAS-BASED SEGMENTATION OF MEDICAL IMAGES LOCALLY CONSTRAINED BY LEVEL SETS \" Signal Processing Institute (ITS) Ecole Polytechnique F\u00b4ed\u00b4erale de Lausanne (EPFL) TR 07\/2005 2005 .  V. Duay N. Houhou and J.-P. Thiran \"ATLAS-BASED SEGMENTATION OF MEDICAL IMAGES LOCALLY CONSTRAINED BY LEVEL SETS \" Signal Processing Institute (ITS) Ecole Polytechnique F\u00b4ed\u00b4erale de Lausanne (EPFL) TR 07\/2005 2005 .","DOI":"10.1109\/ICIP.2005.1530298"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2487966"},{"key":"e_1_3_2_1_19_1","volume-title":"a Kernel-based approach with application to wisdom tooth segmentation from CBCT images","author":"Jud","year":"2014","unstructured":"C. Jud , \"Object segmentation by fitting statistical shape models : a Kernel-based approach with application to wisdom tooth segmentation from CBCT images ,\" 2014 . {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/https\/edoc.unibas.ch\/34098\/. C. Jud, \"Object segmentation by fitting statistical shape models : a Kernel-based approach with application to wisdom tooth segmentation from CBCT images,\" 2014. {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/https\/edoc.unibas.ch\/34098\/."},{"key":"e_1_3_2_1_20_1","volume-title":"Deep Learning","author":"Goodfellow Y.","year":"2016","unstructured":"I. Goodfellow , Y. Bengio and A. Courville , Deep Learning , MIT Press , 2016 . I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MIT Press, 2016."},{"key":"e_1_3_2_1_21_1","volume-title":"Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge,\" CoRR","author":"Kuan M.","year":"2017","unstructured":"K. Kuan , M. Ravaut , G. Manek , H. Chen , J. Lin , B. Nazir , C. Chen , T. C. Howe , Z. Zeng and V. Chandrasekhar , \" Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge,\" CoRR , p. abs\/1705.09435 , 2017 . K. Kuan, M. Ravaut, G. Manek, H. Chen, J. Lin, B. Nazir, C. Chen, T. C. Howe, Z. Zeng and V. Chandrasekhar, \"Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge,\" CoRR, p. abs\/1705.09435 , 2017."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.01.025"},{"key":"e_1_3_2_1_23_1","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger P.","year":"2015","unstructured":"O. Ronneberger , P. Fischer and T. Brox , \" U-net: Convolutional networks for biomedical image segmentation ,\" Medical Image Computing Computer Assisted Interventions , pp. 234 - 241 , 2015 . O. Ronneberger, P.Fischer and T. Brox, \"U-net: Convolutional networks for biomedical image segmentation,\" Medical Image Computing Computer Assisted Interventions, pp. 234 - 241, 2015.","journal-title":"Medical Image Computing Computer Assisted Interventions"},{"key":"e_1_3_2_1_24_1","volume-title":"ImageNet classification with deep convolutional neural networks,\" in Proceedings of the 25th International Conference on Neural Information Processing Systems","author":"Krizhevsky I.","year":"2012","unstructured":"A. Krizhevsky , I. Sutskever and G. E. Hinton , \" ImageNet classification with deep convolutional neural networks,\" in Proceedings of the 25th International Conference on Neural Information Processing Systems , 2012 . A. Krizhevsky, I. Sutskever and G. E. Hinton, \"ImageNet classification with deep convolutional neural networks,\" in Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012."},{"key":"e_1_3_2_1_25_1","volume-title":"Going Deeper with Convolutions,\" in Computer Vision and Pattern Recognition (CVPR)","author":"Szegedy W.","year":"2015","unstructured":"C. Szegedy , W. Liu , Y. Jia , P. Sermanet , S. Reed , D. Anguelov , D. Erhan , V. Vanhoucke and A. Rabinovich , \" Going Deeper with Convolutions,\" in Computer Vision and Pattern Recognition (CVPR) , 2015 . C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich, \"Going Deeper with Convolutions,\" in Computer Vision and Pattern Recognition (CVPR), 2015."},{"key":"e_1_3_2_1_26_1","volume-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition,\" CoRR","author":"Simonyan A.","year":"2015","unstructured":"K. Simonyan and A. Zisserman , \" Very Deep Convolutional Networks for Large-Scale Image Recognition,\" CoRR , p. abs\/1409.1556, 2015 . K. Simonyan and A. Zisserman, \"Very Deep Convolutional Networks for Large-Scale Image Recognition,\" CoRR, p. abs\/1409.1556, 2015."},{"key":"e_1_3_2_1_27_1","unstructured":"\"Open Source Computer Vision Library (OpenCV) \" 2018. {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/https\/opencv.org\/.  \"Open Source Computer Vision Library (OpenCV) \" 2018. {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/https\/opencv.org\/."},{"key":"e_1_3_2_1_28_1","unstructured":"\"Python programming language \" 2018. {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/http\/www.python.org.  \"Python programming language \" 2018. {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/http\/www.python.org."},{"key":"e_1_3_2_1_29_1","unstructured":"\"Pandas \" 2018. {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/https\/pandas.pydata.org\/.  \"Pandas \" 2018. {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/https\/pandas.pydata.org\/."},{"key":"e_1_3_2_1_30_1","unstructured":"\"NumPy \" 2018. {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/http\/www.numpy.org\/.  \"NumPy \" 2018. {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/http\/www.numpy.org\/."},{"key":"e_1_3_2_1_31_1","volume-title":"Image Processing and Analysis in Java","author":"Image J","year":"2018","unstructured":"\" Image J : Image Processing and Analysis in Java ,\" 2018 . {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/https\/imagej.nih.gov\/ij. \"ImageJ: Image Processing and Analysis in Java,\" 2018. {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/https\/imagej.nih.gov\/ij."},{"volume-title":"Machine Learning in Python","year":"2018","key":"e_1_3_2_1_32_1","unstructured":"\"scikit-learn : Machine Learning in Python ,\" 2018 . {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/http\/scikit-learn.org. \"scikit-learn : Machine Learning in Python,\" 2018. {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/http\/scikit-learn.org."},{"key":"e_1_3_2_1_33_1","unstructured":"\"TensorFlow \" 2018. {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/https\/www.tensorflow.org\/.  \"TensorFlow \" 2018. {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/https\/www.tensorflow.org\/."},{"key":"e_1_3_2_1_34_1","unstructured":"\"Keras \" 2018. {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/https\/keras.io\/.  \"Keras \" 2018. {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/https\/keras.io\/."},{"issue":"4","key":"e_1_3_2_1_35_1","first-page":"1034","article-title":"A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons","volume":"5","author":"Sorensen","year":"1948","unstructured":"T. Sorensen , \" A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons ,\" Kongelige Danske Videnskabernes Selskab 5 ( 4 ), p. 1034 , 1948 . T. Sorensen, \"A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons,\" Kongelige Danske Videnskabernes Selskab 5(4), p. 1034, 1948.","journal-title":"Kongelige Danske Videnskabernes Selskab"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2481436"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2015.2430895"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"e_1_3_2_1_39_1","volume-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation,\" in International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Ronneberger P.","year":"2015","unstructured":"O. Ronneberger , P. Fischer and T. Brox , \" U-Net: Convolutional Networks for Biomedical Image Segmentation,\" in International Conference on Medical Image Computing and Computer-Assisted Intervention , 2015 . O. Ronneberger, P. Fischer and T. Brox, \"U-Net: Convolutional Networks for Biomedical Image Segmentation,\" in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_00990"},{"key":"e_1_3_2_1_41_1","volume-title":"Deep Learning in Radiology,\" Academic Radiology","author":"McBee O. A.","year":"2018","unstructured":"M. P. McBee , O. A. Awan , A. T. Colucci , C. W. Ghobadi , N. Kadom , A. P. Kansagra , S. Tridandapani and W. F. Auffermann , \" Deep Learning in Radiology,\" Academic Radiology , p. In press, 2018 . M. P. McBee, O. A. Awan, A. T. Colucci, C. W. Ghobadi, N. Kadom, A. P. Kansagra, S. Tridandapani and W. F. Auffermann, \"Deep Learning in Radiology,\" Academic Radiology, p. In press, 2018."},{"key":"e_1_3_2_1_42_1","volume-title":"Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network,\" Sensors 18(2)","author":"Li L.","year":"2018","unstructured":"Y. Li and L. Shen , \" Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network,\" Sensors 18(2) , 2018 . Y. Li and L. Shen, \"Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network,\" Sensors 18(2), 2018."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2628401"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.01.025"},{"key":"e_1_3_2_1_45_1","volume-title":"A Review on Deep Learning Techniques Applied to Semantic Segmentation,\" CoRR","author":"Garcia-Garcia S.","year":"2017","unstructured":"A. Garcia-Garcia , S. Orts , S. Oprea , V. Villena-Martinez and J. G. Rodriguez , \" A Review on Deep Learning Techniques Applied to Semantic Segmentation,\" CoRR , p. abs\/1704.06857, 2017 . A. Garcia-Garcia, S. Orts, S. Oprea, V. Villena-Martinez and J. G. Rodriguez, \"A Review on Deep Learning Techniques Applied to Semantic Segmentation,\" CoRR, p. abs\/1704.06857, 2017."},{"key":"e_1_3_2_1_46_1","volume-title":"Data Dictionary Reader Code","author":"Ashish A.","year":"2017","unstructured":"N. Ashish and A. Patawari , \" Data Dictionary Reader Code ,\" 2017 . {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/https\/github.com\/nashish100\/DDReading. N. Ashish and A. Patawari, \"Data Dictionary Reader Code,\" 2017. {Online}. Available: https:\/\/2.zoppoz.workers.dev:443\/https\/github.com\/nashish100\/DDReading."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24553-9_68"}],"event":{"name":"BCB '18: 9th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","sponsor":["SIGBio ACM Special Interest Group on Bioinformatics"],"location":"Washington DC USA","acronym":"BCB '18"},"container-title":["Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/3233547.3233552","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/pdf\/10.1145\/3233547.3233552","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:13:12Z","timestamp":1750212792000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/3233547.3233552"}},"subtitle":["A Deep Learning Based Solution"],"short-title":[],"issued":{"date-parts":[[2018,8,15]]},"references-count":47,"alternative-id":["10.1145\/3233547.3233552","10.1145\/3233547"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/3233547.3233552","relation":{},"subject":[],"published":{"date-parts":[[2018,8,15]]},"assertion":[{"value":"2018-08-15","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}