Brain Tumor Segmentation Using Dense Fully Convolutional Neural Network View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-02-17

AUTHORS

Mazhar Shaikh , Ganesh Anand , Gagan Acharya , Abhijit Amrutkar , Varghese Alex , Ganapathy Krishnamurthi

ABSTRACT

Manual segmentation of brain tumor is often time consuming and the performance of the segmentation varies based on the operators experience. This leads to the requisition of a fully automatic method for brain tumor segmentation. In this paper, we propose the usage of the 100 layer Tiramisu architecture for the segmentation of brain tumor from multi modal MR images, which is evolved by integrating a densely connected fully convolutional neural network (FCNN), followed by post-processing using a Dense Conditional Random Field (DCRF). The network consists of blocks of densely connected layers, transition down layers in down-sampling path and transition up layers in up-sampling path. The method was tested on dataset provided by Multi modal Brain Tumor Segmentation Challenge (BraTS) 2017. The training data is composed of 210 high-grade brain tumor and 74 low-grade brain tumor cases. The proposed network achieves a mean whole tumor, tumor core & active tumor dice score of 0.87, 0.68 & 0.65. Respectively on the BraTS ’17 validation set and 0.83, 0.65 & 0.65 on the Brats ’17 test set. More... »

PAGES

309-319

Book

TITLE

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

ISBN

978-3-319-75237-2
978-3-319-75238-9

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-75238-9_27

DOI

http://dx.doi.org/10.1007/978-3-319-75238-9_27

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1101082128


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/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India", 
          "id": "http://www.grid.ac/institutes/grid.417969.4", 
          "name": [
            "Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shaikh", 
        "givenName": "Mazhar", 
        "id": "sg:person.015174516220.27", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015174516220.27"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India", 
          "id": "http://www.grid.ac/institutes/grid.417969.4", 
          "name": [
            "Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Anand", 
        "givenName": "Ganesh", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India", 
          "id": "http://www.grid.ac/institutes/grid.417969.4", 
          "name": [
            "Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Acharya", 
        "givenName": "Gagan", 
        "id": "sg:person.016567457220.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016567457220.08"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India", 
          "id": "http://www.grid.ac/institutes/grid.417969.4", 
          "name": [
            "Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Amrutkar", 
        "givenName": "Abhijit", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India", 
          "id": "http://www.grid.ac/institutes/grid.417969.4", 
          "name": [
            "Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Alex", 
        "givenName": "Varghese", 
        "id": "sg:person.010634710437.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010634710437.18"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India", 
          "id": "http://www.grid.ac/institutes/grid.417969.4", 
          "name": [
            "Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Krishnamurthi", 
        "givenName": "Ganapathy", 
        "id": "sg:person.013774661125.64", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013774661125.64"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2018-02-17", 
    "datePublishedReg": "2018-02-17", 
    "description": "Manual segmentation of brain tumor is often time consuming and the performance of the segmentation varies based on the operators experience. This leads to the requisition of a fully automatic method for brain tumor segmentation. In this paper, we propose the usage of the 100 layer Tiramisu architecture for the segmentation of brain tumor from multi modal MR images, which is evolved by integrating a densely connected fully convolutional neural network (FCNN), followed by post-processing using a Dense Conditional Random Field (DCRF). The network consists of blocks of densely connected layers, transition down layers in down-sampling path and transition up layers in up-sampling path. The method was tested on dataset provided by Multi modal Brain Tumor Segmentation Challenge (BraTS) 2017. The training data is composed of 210 high-grade brain tumor and 74 low-grade brain tumor cases. The proposed network achieves a mean whole tumor, tumor core & active tumor dice score of 0.87, 0.68 & 0.65. Respectively on the BraTS \u201917 validation set and 0.83, 0.65 & 0.65 on the Brats \u201917 test set.", 
    "editor": [
      {
        "familyName": "Crimi", 
        "givenName": "Alessandro", 
        "type": "Person"
      }, 
      {
        "familyName": "Bakas", 
        "givenName": "Spyridon", 
        "type": "Person"
      }, 
      {
        "familyName": "Kuijf", 
        "givenName": "Hugo", 
        "type": "Person"
      }, 
      {
        "familyName": "Menze", 
        "givenName": "Bjoern", 
        "type": "Person"
      }, 
      {
        "familyName": "Reyes", 
        "givenName": "Mauricio", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-75238-9_27", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-75237-2", 
        "978-3-319-75238-9"
      ], 
      "name": "Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries", 
      "type": "Book"
    }, 
    "keywords": [
      "dense conditional random field", 
      "convolutional neural network", 
      "brain tumor segmentation", 
      "tumor segmentation", 
      "neural network", 
      "Conditional Random Fields", 
      "connected layer", 
      "Dice score", 
      "training data", 
      "automatic method", 
      "manual segmentation", 
      "segmentation", 
      "random fields", 
      "network", 
      "test set", 
      "MR images", 
      "set", 
      "architecture", 
      "datasets", 
      "validation set", 
      "path", 
      "tumor core", 
      "images", 
      "usage", 
      "brain tumor cases", 
      "whole tumor", 
      "performance", 
      "method", 
      "operator experience", 
      "requisition", 
      "block", 
      "data", 
      "high-grade brain tumors", 
      "Brat", 
      "layer", 
      "time", 
      "core", 
      "experience", 
      "field", 
      "dense", 
      "brain tumors", 
      "tumor cases", 
      "cases", 
      "scores", 
      "varies", 
      "transition", 
      "tumors", 
      "paper", 
      "segmentation varies", 
      "layer Tiramisu architecture", 
      "Tiramisu architecture", 
      "multi modal MR images", 
      "modal MR images", 
      "Multi modal Brain Tumor Segmentation Challenge (BraTS) 2017", 
      "modal Brain Tumor Segmentation Challenge (BraTS) 2017", 
      "Brain Tumor Segmentation Challenge (BraTS) 2017", 
      "Tumor Segmentation Challenge (BraTS) 2017", 
      "Segmentation Challenge (BraTS) 2017", 
      "Challenge (BraTS) 2017", 
      "low-grade brain tumor cases", 
      "mean whole tumor", 
      "active tumor dice score", 
      "tumor dice score"
    ], 
    "name": "Brain Tumor Segmentation Using Dense Fully Convolutional Neural Network", 
    "pagination": "309-319", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1101082128"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-75238-9_27"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-75238-9_27", 
      "https://app.dimensions.ai/details/publication/pub.1101082128"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2021-11-01T18:52", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211101/entities/gbq_results/chapter/chapter_252.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-319-75238-9_27"
  }
]
 

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.10.1007/978-3-319-75238-9_27'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-75238-9_27'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-75238-9_27'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-75238-9_27'


 

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

176 TRIPLES      23 PREDICATES      88 URIs      81 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-75238-9_27 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N34e55794c723434686d646a129a19a0b
4 schema:datePublished 2018-02-17
5 schema:datePublishedReg 2018-02-17
6 schema:description Manual segmentation of brain tumor is often time consuming and the performance of the segmentation varies based on the operators experience. This leads to the requisition of a fully automatic method for brain tumor segmentation. In this paper, we propose the usage of the 100 layer Tiramisu architecture for the segmentation of brain tumor from multi modal MR images, which is evolved by integrating a densely connected fully convolutional neural network (FCNN), followed by post-processing using a Dense Conditional Random Field (DCRF). The network consists of blocks of densely connected layers, transition down layers in down-sampling path and transition up layers in up-sampling path. The method was tested on dataset provided by Multi modal Brain Tumor Segmentation Challenge (BraTS) 2017. The training data is composed of 210 high-grade brain tumor and 74 low-grade brain tumor cases. The proposed network achieves a mean whole tumor, tumor core & active tumor dice score of 0.87, 0.68 & 0.65. Respectively on the BraTS ’17 validation set and 0.83, 0.65 & 0.65 on the Brats ’17 test set.
7 schema:editor Nf5da863c20a84ff98f779c8afaad326c
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf Ne80e9deb50864b03a26e8d191646dc27
12 schema:keywords Brain Tumor Segmentation Challenge (BraTS) 2017
13 Brat
14 Challenge (BraTS) 2017
15 Conditional Random Fields
16 Dice score
17 MR images
18 Multi modal Brain Tumor Segmentation Challenge (BraTS) 2017
19 Segmentation Challenge (BraTS) 2017
20 Tiramisu architecture
21 Tumor Segmentation Challenge (BraTS) 2017
22 active tumor dice score
23 architecture
24 automatic method
25 block
26 brain tumor cases
27 brain tumor segmentation
28 brain tumors
29 cases
30 connected layer
31 convolutional neural network
32 core
33 data
34 datasets
35 dense
36 dense conditional random field
37 experience
38 field
39 high-grade brain tumors
40 images
41 layer
42 layer Tiramisu architecture
43 low-grade brain tumor cases
44 manual segmentation
45 mean whole tumor
46 method
47 modal Brain Tumor Segmentation Challenge (BraTS) 2017
48 modal MR images
49 multi modal MR images
50 network
51 neural network
52 operator experience
53 paper
54 path
55 performance
56 random fields
57 requisition
58 scores
59 segmentation
60 segmentation varies
61 set
62 test set
63 time
64 training data
65 transition
66 tumor cases
67 tumor core
68 tumor dice score
69 tumor segmentation
70 tumors
71 usage
72 validation set
73 varies
74 whole tumor
75 schema:name Brain Tumor Segmentation Using Dense Fully Convolutional Neural Network
76 schema:pagination 309-319
77 schema:productId Nb3dacdb7a333470db4a5ddec5854db63
78 Nfa3a4d31a4e342e1a31b03e3aee6b8ea
79 schema:publisher Nc879d37e2ede4313b513016122bd5de9
80 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101082128
81 https://doi.org/10.1007/978-3-319-75238-9_27
82 schema:sdDatePublished 2021-11-01T18:52
83 schema:sdLicense https://scigraph.springernature.com/explorer/license/
84 schema:sdPublisher N7b018f375ab14bf0b07aa93fc4719b1b
85 schema:url https://doi.org/10.1007/978-3-319-75238-9_27
86 sgo:license sg:explorer/license/
87 sgo:sdDataset chapters
88 rdf:type schema:Chapter
89 N0a1e5e30b92a4388929b5b74fe2cad7d schema:affiliation grid-institutes:grid.417969.4
90 schema:familyName Anand
91 schema:givenName Ganesh
92 rdf:type schema:Person
93 N0d72184c2fb14485b0b5aa8841f79c1e rdf:first sg:person.016567457220.08
94 rdf:rest Nf1e83f767f4c461cb18846189c9baa92
95 N2aeb33ebfea748cc86b322bc03178514 rdf:first N0a1e5e30b92a4388929b5b74fe2cad7d
96 rdf:rest N0d72184c2fb14485b0b5aa8841f79c1e
97 N34e55794c723434686d646a129a19a0b rdf:first sg:person.015174516220.27
98 rdf:rest N2aeb33ebfea748cc86b322bc03178514
99 N50c34cc356be4c9e81d10ba7deabc3fd rdf:first N51ab4e5c5c154ce0bb104bb2653f12d5
100 rdf:rest rdf:nil
101 N51ab4e5c5c154ce0bb104bb2653f12d5 schema:familyName Reyes
102 schema:givenName Mauricio
103 rdf:type schema:Person
104 N5829f3fa42e04c8e979b892a64604f34 rdf:first sg:person.013774661125.64
105 rdf:rest rdf:nil
106 N646b7ba66fcb4484b3b592deaf144cef rdf:first Na4d5b802e3704e009cfe490aaa4e6ecb
107 rdf:rest Nc1ba189ae6d747d1b897d1d0ddd63fb7
108 N7b018f375ab14bf0b07aa93fc4719b1b schema:name Springer Nature - SN SciGraph project
109 rdf:type schema:Organization
110 N8460f3c2b8174c5ea98c2c1fe54ed945 rdf:first Ne6e2f6de97384cd6bb1c1054476b6304
111 rdf:rest N646b7ba66fcb4484b3b592deaf144cef
112 Na4d5b802e3704e009cfe490aaa4e6ecb schema:familyName Kuijf
113 schema:givenName Hugo
114 rdf:type schema:Person
115 Nb2b8ae6714ea4f53a84a09d4f6237711 schema:familyName Menze
116 schema:givenName Bjoern
117 rdf:type schema:Person
118 Nb3dacdb7a333470db4a5ddec5854db63 schema:name dimensions_id
119 schema:value pub.1101082128
120 rdf:type schema:PropertyValue
121 Nc1ba189ae6d747d1b897d1d0ddd63fb7 rdf:first Nb2b8ae6714ea4f53a84a09d4f6237711
122 rdf:rest N50c34cc356be4c9e81d10ba7deabc3fd
123 Nc879d37e2ede4313b513016122bd5de9 schema:name Springer Nature
124 rdf:type schema:Organisation
125 Nca3da13213a940dfb0e3ffd2f52072bf rdf:first sg:person.010634710437.18
126 rdf:rest N5829f3fa42e04c8e979b892a64604f34
127 Ndecfcc6b4d7b4257b693ed00e166e4ea schema:familyName Crimi
128 schema:givenName Alessandro
129 rdf:type schema:Person
130 Ne6e2f6de97384cd6bb1c1054476b6304 schema:familyName Bakas
131 schema:givenName Spyridon
132 rdf:type schema:Person
133 Ne80e9deb50864b03a26e8d191646dc27 schema:isbn 978-3-319-75237-2
134 978-3-319-75238-9
135 schema:name Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
136 rdf:type schema:Book
137 Nf1e83f767f4c461cb18846189c9baa92 rdf:first Nfb7f897cab074314a9ecd2fe874a3705
138 rdf:rest Nca3da13213a940dfb0e3ffd2f52072bf
139 Nf5da863c20a84ff98f779c8afaad326c rdf:first Ndecfcc6b4d7b4257b693ed00e166e4ea
140 rdf:rest N8460f3c2b8174c5ea98c2c1fe54ed945
141 Nfa3a4d31a4e342e1a31b03e3aee6b8ea schema:name doi
142 schema:value 10.1007/978-3-319-75238-9_27
143 rdf:type schema:PropertyValue
144 Nfb7f897cab074314a9ecd2fe874a3705 schema:affiliation grid-institutes:grid.417969.4
145 schema:familyName Amrutkar
146 schema:givenName Abhijit
147 rdf:type schema:Person
148 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
149 schema:name Information and Computing Sciences
150 rdf:type schema:DefinedTerm
151 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
152 schema:name Artificial Intelligence and Image Processing
153 rdf:type schema:DefinedTerm
154 sg:person.010634710437.18 schema:affiliation grid-institutes:grid.417969.4
155 schema:familyName Alex
156 schema:givenName Varghese
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010634710437.18
158 rdf:type schema:Person
159 sg:person.013774661125.64 schema:affiliation grid-institutes:grid.417969.4
160 schema:familyName Krishnamurthi
161 schema:givenName Ganapathy
162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013774661125.64
163 rdf:type schema:Person
164 sg:person.015174516220.27 schema:affiliation grid-institutes:grid.417969.4
165 schema:familyName Shaikh
166 schema:givenName Mazhar
167 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015174516220.27
168 rdf:type schema:Person
169 sg:person.016567457220.08 schema:affiliation grid-institutes:grid.417969.4
170 schema:familyName Acharya
171 schema:givenName Gagan
172 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016567457220.08
173 rdf:type schema:Person
174 grid-institutes:grid.417969.4 schema:alternateName Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India
175 schema:name Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India
176 rdf:type schema:Organization
 




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


...