Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Buchtitelbild

2019 | OriginalPaper | Buchkapitel

1. Pancreas Segmentation in CT and MRI via Task-Specific Network Design and Recurrent Neural Contextual Learning

verfasst von : Jinzheng Cai, Le Lu, Fuyong Xing, Lin Yang

Erschienen in: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Automatic pancreas segmentation in radiology images, e.g., computed tomography (CT), and magnetic resonance imaging (MRI), is frequently required by computer-aided screening, diagnosis, and quantitative assessment. Yet, pancreas is a challenging abdominal organ to segment due to the high inter-patient anatomical variability in both shape and volume metrics. Recently, convolutional neural networks (CNN) have demonstrated promising performance on accurate segmentation of pancreas. However, the CNN-based method often suffers from segmentation discontinuity for reasons such as noisy image quality and blurry pancreatic boundary. In this chapter, we first discuss the CNN configurations and training objectives that lead to the state-of-the-art performance on pancreas segmentation. We then present a recurrent neural network (RNN) to address the problem of segmentation spatial inconsistency across adjacent image slices. The RNN takes outputs of the CNN and refines the segmentation by improving the shape smoothness.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Nächstes Kapitel Deep Learning for Muscle Pathology Image Analysis
Literatur
1.
Cai J, Lu L, Xie Y, Xing F, Yang L (2017) Improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function. In: MICCAI, pp 674–682. Springer
2.
Cai J, Lu L, Zhang Z, Xing F, Yang L, Yin Q (2016) Pancreas segmentation in MRI using graph-based decision fusion on convolutional neural networks. In: MICCAI, pp 442–450. Springer
3.
Chen J, Yang L, Zhang Y, Alber MS, Chen DZ (2016) Combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation. In: NIPS, pp 3036–3044
4.
Chen L, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. TPAMI 40(4):834–848CrossRef
5.
Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: learning dense volumetric segmentation from sparse annotation. In: MICCAI, pp 424–432. Springer
6.
Clark KW, Vendt BA, Smith KE, Freymann JB, Kirby JS, Koppel P, Moore SM, Phillips SR, Maffitt DR, Pringle M, Tarbox L, Prior FW (2013) The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045–1057CrossRef
7.
Deng J, Dong W, Socher R, Li L, Li K, Li F (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE computer society conference on computer vision and pattern recognition (CVPR 2009), 20–25 June 2009, Miami, Florida, USA, pp 248–255
8.
Farag A, Lu L, Roth HR, Liu J, Turkbey E, Summers RM (2017) A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling. TMI 26(1):386–399MathSciNetMATH
9.
Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: AISTATS, pp 249–256
10.
Kamnitsas K, Ledig C, Newcombe VFJ, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017) Efficient multi-scale 3d CNN with fully connected CRF for accurate brain lesion segmentation. MIA 36:61–78
11.
Karasawa K, Oda M, Kitasaka T, Misawa K, Fujiwara M, Chu C, Zheng G, Rueckert D, Mori K (2017) Multi-atlas pancreas segmentation: Atlas selection based on vessel structure. MIA 39:18–28
12.
Lee C, Xie S, Gallagher PW, Zhang Z, Tu Z (2015) Deeply-supervised nets. In: AISTATS
13.
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: CVPR, pp 3431–3440
14.
Merkow J, Marsden A, Kriegman DJ, Tu Z (2016) Dense volume-to-volume vascular boundary detection. In: MICCAI, pp 371–379
15.
Milletari F, Navab N, Ahmadi S (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: International conference on 3D vision, pp 565–571
16.
Ng J, Hausknecht M, Vijayanarasimhan S, Vinyals O, Monga R, Toderici G (2015) Beyond short snippets: deep networks for video classification. In: ICCV, pp 4694–4702
17.
Oda M, Shimizu N, Karasawa K, Nimura Y, Kitasaka T, Misawa K, Rueckert D, Mori K (2016) Regression forest-based atlas localization and direction specific atlas generation for pancreas segmentation. In: MICCAI, pp 556–563. Springer
18.
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: MICCAI, pp 234–241
19.
Roth HR, Lu L, Farag A, Shin HC, Liu J, Turkbey EB, Summers RM (2015) Deeporgan: multi-level deep convolutional networks for automated pancreas segmentation. In: MICCAI, pp 556–564. Springer
20.
Roth HR, Lu L, Farag A, Sohn A, Summers RM (2016) Spatial aggregation of holistically-nested networks for automated pancreas segmentation. In: MICCAI, pp 450–451. Springer
21.
Roth HR, Lu L, Lay N, Harrison AP, Farag A, Summers RM (2018) Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation. MIA 45:94–107
22.
Rotha HR, Odaa H, Zhoub X, Shimizua N, Yanga Y, Hayashia Y, Odaa M, Fujiwarac M, Misawad K, Moria K (2018) An application of cascaded 3D fully convolutional networks for medical image segmentation. ArXiv e-prints
23.
Shi X, Chen Z, Wang H, Yeung D, Wong W, Woo W (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: NIPS, pp 802–810
24.
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations, pp 1–14
25.
Stollenga MF, Byeon W, Liwicki M, Schmidhuber J (2015) Parallel multi-dimensional lstm, with application to fast biomedical volumetric image segmentation. In: NIPS, pp 2998–3006
26.
Tong T, Wolz R, Wang Z, Gao Q, Misawa K, Fujiwara M, Mori K, Hajnal JV, Rueckert D (2015) Discriminative dictionary learning for abdominal multi-organ segmentation. MIA 23(1):92–104
27.
Wolz R, Chu C, Misawa K, Fujiwara M, Mori K, Rueckert D (2013) Automated abdominal multi-organ segmentation with subject-specific atlas generation. TMI 32(9):1723–1730
28.
Xie S, Tu Z (2015) Holistically-nested edge detection. In: ICCV, pp 1395–1403
29.
Zheng S, Jayasumana S, Romera-Paredes B, Vineet V, Su Z, Du D, Huang C, Torr PH (2015) Conditional random fields as recurrent neural networks. In: ICCV, pp 1529–1537
30.
Metadaten
Titel
Pancreas Segmentation in CT and MRI via Task-Specific Network Design and Recurrent Neural Contextual Learning
verfasst von
Jinzheng Cai
Le Lu
Fuyong Xing
Lin Yang
Copyright-Jahr
2019
Buch
Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

Print ISBN: 978-3-030-13968-1

Electronic ISBN: 978-3-030-13969-8

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-13969-8_1

Premium Partner

    Bildnachweise