U-Net: Convolutional Networks for Biomedical Image Segmentation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2015

AUTHORS

Olaf Ronneberger , Philipp Fischer , Thomas Brox

ABSTRACT

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net . More... »

PAGES

234-241

Book

TITLE

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015

ISBN

978-3-319-24573-7
978-3-319-24574-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-24574-4_28

DOI

http://dx.doi.org/10.1007/978-3-319-24574-4_28

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1017774818


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "name": [
            "Computer Science Department and BIOSS Centre for Biological Signalling Studies, University of Freiburg"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ronneberger", 
        "givenName": "Olaf", 
        "id": "sg:person.0625370723.52", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0625370723.52"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Computer Science Department and BIOSS Centre for Biological Signalling Studies, University of Freiburg"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fischer", 
        "givenName": "Philipp", 
        "id": "sg:person.012106015125.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012106015125.15"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Computer Science Department and BIOSS Centre for Biological Signalling Studies, University of Freiburg"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Brox", 
        "givenName": "Thomas", 
        "id": "sg:person.012443225372.65", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012443225372.65"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1162/neco.1989.1.4.541", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008345178"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btu080", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018267701"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pbio.1000502", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037466020"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2647868.2654889", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052031051"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iccv.2013.269", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1079004280"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2015.7298965", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093626237"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iccv.2015.123", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093828312"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2015.7298642", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095686079"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2015", 
    "datePublishedReg": "2015-01-01", 
    "description": "There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .", 
    "editor": [
      {
        "familyName": "Navab", 
        "givenName": "Nassir", 
        "type": "Person"
      }, 
      {
        "familyName": "Hornegger", 
        "givenName": "Joachim", 
        "type": "Person"
      }, 
      {
        "familyName": "Wells", 
        "givenName": "William M.", 
        "type": "Person"
      }, 
      {
        "familyName": "Frangi", 
        "givenName": "Alejandro F.", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-24574-4_28", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-319-24573-7", 
        "978-3-319-24574-4"
      ], 
      "name": "Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015", 
      "type": "Book"
    }, 
    "name": "U-Net: Convolutional Networks for Biomedical Image Segmentation", 
    "pagination": "234-241", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-24574-4_28"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "54b0c42b42b8589e4e47cb7de95064f16751a7d529f6fa6c8182c4cd5a015c29"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1017774818"
        ]
      }
    ], 
    "publisher": {
      "location": "Cham", 
      "name": "Springer International Publishing", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-24574-4_28", 
      "https://app.dimensions.ai/details/publication/pub.1017774818"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T19:08", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8684_00000254.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-319-24574-4_28"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-24574-4_28'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-24574-4_28'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-24574-4_28'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-24574-4_28'


 

This table displays all metadata directly associated to this object as RDF triples.

121 TRIPLES      23 PREDICATES      35 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-24574-4_28 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N26544163cf1c4865b57f53bba361b160
4 schema:citation https://doi.org/10.1093/bioinformatics/btu080
5 https://doi.org/10.1109/cvpr.2015.7298642
6 https://doi.org/10.1109/cvpr.2015.7298965
7 https://doi.org/10.1109/iccv.2013.269
8 https://doi.org/10.1109/iccv.2015.123
9 https://doi.org/10.1145/2647868.2654889
10 https://doi.org/10.1162/neco.1989.1.4.541
11 https://doi.org/10.1371/journal.pbio.1000502
12 schema:datePublished 2015
13 schema:datePublishedReg 2015-01-01
14 schema:description There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .
15 schema:editor N909b577282144c2f9c0e3abaf1e97876
16 schema:genre chapter
17 schema:inLanguage en
18 schema:isAccessibleForFree true
19 schema:isPartOf N32a567156de749b1acd557a13769348c
20 schema:name U-Net: Convolutional Networks for Biomedical Image Segmentation
21 schema:pagination 234-241
22 schema:productId N00d0de0ceea547e586d49d1a33059f17
23 N6ccd35a5233f4d8a996407559cb5a338
24 Nb527c609cdc14d70958e154b764e4dec
25 schema:publisher N6a27e54822fe477db1b24775ff554908
26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017774818
27 https://doi.org/10.1007/978-3-319-24574-4_28
28 schema:sdDatePublished 2019-04-15T19:08
29 schema:sdLicense https://scigraph.springernature.com/explorer/license/
30 schema:sdPublisher N05616e43f6484660aee35e533b4efd0d
31 schema:url http://link.springer.com/10.1007/978-3-319-24574-4_28
32 sgo:license sg:explorer/license/
33 sgo:sdDataset chapters
34 rdf:type schema:Chapter
35 N00d0de0ceea547e586d49d1a33059f17 schema:name readcube_id
36 schema:value 54b0c42b42b8589e4e47cb7de95064f16751a7d529f6fa6c8182c4cd5a015c29
37 rdf:type schema:PropertyValue
38 N05616e43f6484660aee35e533b4efd0d schema:name Springer Nature - SN SciGraph project
39 rdf:type schema:Organization
40 N2279d84846c244d3aa23ecb9048ae361 schema:familyName Wells
41 schema:givenName William M.
42 rdf:type schema:Person
43 N26544163cf1c4865b57f53bba361b160 rdf:first sg:person.0625370723.52
44 rdf:rest N2e8ae4f829034096881b12ae9383e47d
45 N2e8ae4f829034096881b12ae9383e47d rdf:first sg:person.012106015125.15
46 rdf:rest N60f59ef089664af3b6c6ac45138bf00f
47 N32a567156de749b1acd557a13769348c schema:isbn 978-3-319-24573-7
48 978-3-319-24574-4
49 schema:name Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015
50 rdf:type schema:Book
51 N378c1e4f554f4fb8aeae410f07504c27 schema:familyName Frangi
52 schema:givenName Alejandro F.
53 rdf:type schema:Person
54 N53ccdb337a544b47809030e54566318a schema:familyName Hornegger
55 schema:givenName Joachim
56 rdf:type schema:Person
57 N5a21d9b69a46419b87026bf79aa18c40 schema:name Computer Science Department and BIOSS Centre for Biological Signalling Studies, University of Freiburg
58 rdf:type schema:Organization
59 N60f59ef089664af3b6c6ac45138bf00f rdf:first sg:person.012443225372.65
60 rdf:rest rdf:nil
61 N6a27e54822fe477db1b24775ff554908 schema:location Cham
62 schema:name Springer International Publishing
63 rdf:type schema:Organisation
64 N6ccd35a5233f4d8a996407559cb5a338 schema:name dimensions_id
65 schema:value pub.1017774818
66 rdf:type schema:PropertyValue
67 N858667ff796b48bb88f90508fada3631 rdf:first N53ccdb337a544b47809030e54566318a
68 rdf:rest Nb823d93bd8f34699a3a99dacd0ced46b
69 N909b577282144c2f9c0e3abaf1e97876 rdf:first Ne6205b4680874f9fa0c09f9698c8804b
70 rdf:rest N858667ff796b48bb88f90508fada3631
71 Nb527c609cdc14d70958e154b764e4dec schema:name doi
72 schema:value 10.1007/978-3-319-24574-4_28
73 rdf:type schema:PropertyValue
74 Nb823d93bd8f34699a3a99dacd0ced46b rdf:first N2279d84846c244d3aa23ecb9048ae361
75 rdf:rest Nc6d1d880c0224537a350376c0f90af0d
76 Nc125370375804a249da2f4ae2a01671d schema:name Computer Science Department and BIOSS Centre for Biological Signalling Studies, University of Freiburg
77 rdf:type schema:Organization
78 Nc6d1d880c0224537a350376c0f90af0d rdf:first N378c1e4f554f4fb8aeae410f07504c27
79 rdf:rest rdf:nil
80 Ne6205b4680874f9fa0c09f9698c8804b schema:familyName Navab
81 schema:givenName Nassir
82 rdf:type schema:Person
83 Nf022b676b5ad4b3286b4bd47c61a9ba4 schema:name Computer Science Department and BIOSS Centre for Biological Signalling Studies, University of Freiburg
84 rdf:type schema:Organization
85 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
86 schema:name Information and Computing Sciences
87 rdf:type schema:DefinedTerm
88 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
89 schema:name Artificial Intelligence and Image Processing
90 rdf:type schema:DefinedTerm
91 sg:person.012106015125.15 schema:affiliation Nf022b676b5ad4b3286b4bd47c61a9ba4
92 schema:familyName Fischer
93 schema:givenName Philipp
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012106015125.15
95 rdf:type schema:Person
96 sg:person.012443225372.65 schema:affiliation Nc125370375804a249da2f4ae2a01671d
97 schema:familyName Brox
98 schema:givenName Thomas
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012443225372.65
100 rdf:type schema:Person
101 sg:person.0625370723.52 schema:affiliation N5a21d9b69a46419b87026bf79aa18c40
102 schema:familyName Ronneberger
103 schema:givenName Olaf
104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0625370723.52
105 rdf:type schema:Person
106 https://doi.org/10.1093/bioinformatics/btu080 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018267701
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1109/cvpr.2015.7298642 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095686079
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1109/cvpr.2015.7298965 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093626237
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1109/iccv.2013.269 schema:sameAs https://app.dimensions.ai/details/publication/pub.1079004280
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1109/iccv.2015.123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093828312
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1145/2647868.2654889 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052031051
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1162/neco.1989.1.4.541 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008345178
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1371/journal.pbio.1000502 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037466020
121 rdf:type schema:CreativeWork
 




Preview window. Press ESC to close (or click here)


...