Skip to main content

2019 | OriginalPaper | Buchkapitel

Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities

verfasst von : Jose Dolz, Ismail Ben Ayed, Christian Desrosiers

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

Verlag: Springer International Publishing

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

search-config
loading …

Abstract

Delineating infarcted tissue in ischemic stroke lesions is crucial to determine the extend of damage and optimal treatment for this life-threatening condition. However, this problem remains challenging due to high variability of ischemic strokes’ location and shape.

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!

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!

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!

Literatur
1.
Zurück zum Zitat Aygün, M., Şahin, Y.H., Ünal, G.: Multi modal convolutional neural networks for brain tumor segmentation. arXiv preprint arXiv:1809.06191 (2018) Aygün, M., Şahin, Y.H., Ünal, G.: Multi modal convolutional neural networks for brain tumor segmentation. arXiv preprint arXiv:​1809.​06191 (2018)
2.
Zurück zum Zitat Barber, P., et al.: Imaging of the brain in acute ischaemic stroke: comparison of computed tomography and magnetic resonance diffusion-weighted imaging. J. Neurol. Neurosurg. Psychiatry 76(11), 1528–1533 (2005)CrossRef Barber, P., et al.: Imaging of the brain in acute ischaemic stroke: comparison of computed tomography and magnetic resonance diffusion-weighted imaging. J. Neurol. Neurosurg. Psychiatry 76(11), 1528–1533 (2005)CrossRef
3.
Zurück zum Zitat Chalela, J.A., et al.: Magnetic resonance imaging and computed tomography in emergency assessment of patients with suspected acute stroke: a prospective comparison. Lancet 369(9558), 293–298 (2007)CrossRef Chalela, J.A., et al.: Magnetic resonance imaging and computed tomography in emergency assessment of patients with suspected acute stroke: a prospective comparison. Lancet 369(9558), 293–298 (2007)CrossRef
4.
Zurück zum Zitat Chen, L., Bentley, P., Rueckert, D.: Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. NeuroImage: Clin. 15, 633–643 (2017)CrossRef Chen, L., Bentley, P., Rueckert, D.: Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. NeuroImage: Clin. 15, 633–643 (2017)CrossRef
5.
Zurück zum Zitat Chen, L., Wu, Y., DSouza, A.M., Abidin, A.Z., Wismüller, A., Xu, C.: MRI tumor segmentation with densely connected 3D CNN. In: Medical Imaging 2018: Image Processing. International Society for Optics and Photonics (2018) Chen, L., Wu, Y., DSouza, A.M., Abidin, A.Z., Wismüller, A., Xu, C.: MRI tumor segmentation with densely connected 3D CNN. In: Medical Imaging 2018: Image Processing. International Society for Optics and Photonics (2018)
6.
Zurück zum Zitat Chen, Y., Wang, H., Long, Y.: Regularization of convolutional neural networks using shufflenode. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), pp. 355–360. IEEE (2017) Chen, Y., Wang, H., Long, Y.: Regularization of convolutional neural networks using shufflenode. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), pp. 355–360. IEEE (2017)
7.
Zurück zum Zitat Choi, Y., Kwon, Y., Lee, H., Kim, B.J., Paik, M.C., Won, J.H.: Ensemble of deep convolutional neural networks for prognosis of ischemic stroke. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds.) BrainLes 2016. LNCS, vol. 10154, pp. 231–243. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-55524-9_22CrossRef Choi, Y., Kwon, Y., Lee, H., Kim, B.J., Paik, M.C., Won, J.H.: Ensemble of deep convolutional neural networks for prognosis of ischemic stroke. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds.) BrainLes 2016. LNCS, vol. 10154, pp. 231–243. Springer, Cham (2016). https://​doi.​org/​10.​1007/​978-3-319-55524-9_​22CrossRef
8.
Zurück zum Zitat Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49CrossRef Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://​doi.​org/​10.​1007/​978-3-319-46723-8_​49CrossRef
9.
Zurück zum Zitat Dolz, J., Ben Ayed, I., Yuan, J., Desrosiers, C.: Isointense infant brain segmentation with a hyper-dense connected convolutional neural network. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 616–620. IEEE (2018) Dolz, J., Ben Ayed, I., Yuan, J., Desrosiers, C.: Isointense infant brain segmentation with a hyper-dense connected convolutional neural network. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 616–620. IEEE (2018)
10.
Zurück zum Zitat Dolz, J., Desrosiers, C., Wang, L., Yuan, J., Shen, D., Ayed, I.B.: Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation. arXiv preprint arXiv:1712.05319 (2017) Dolz, J., Desrosiers, C., Wang, L., Yuan, J., Shen, D., Ayed, I.B.: Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation. arXiv preprint arXiv:​1712.​05319 (2017)
11.
Zurück zum Zitat Dolz, J., Gopinath, K., Yuan, J., Lombaert, H., Desrosiers, C., Ayed, I.B.: HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation. arXiv preprint arXiv:1804.02967 (2018) Dolz, J., Gopinath, K., Yuan, J., Lombaert, H., Desrosiers, C., Ayed, I.B.: HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation. arXiv preprint arXiv:​1804.​02967 (2018)
13.
Zurück zum Zitat Feigin, V.L., Lawes, C.M., Bennett, D.A., Anderson, C.S.: Stroke epidemiology: a review of population-based studies of incidence, prevalence, and case-fatality in the late 20th century. Lancet Neurol. 2(1), 43–53 (2003)CrossRef Feigin, V.L., Lawes, C.M., Bennett, D.A., Anderson, C.S.: Stroke epidemiology: a review of population-based studies of incidence, prevalence, and case-fatality in the late 20th century. Lancet Neurol. 2(1), 43–53 (2003)CrossRef
14.
Zurück zum Zitat Guerrero, R., et al.: White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks. NeuroImage: Clin. 17, 918–934 (2018)CrossRef Guerrero, R., et al.: White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks. NeuroImage: Clin. 17, 918–934 (2018)CrossRef
15.
Zurück zum Zitat Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, vol. 1, p. 3 (2017) Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, vol. 1, p. 3 (2017)
16.
Zurück zum Zitat Kamnitsas, K., Chen, L., Ledig, C., Rueckert, D., Glocker, B.: Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI. Ischemic Stroke Lesion Segment. 13, 46 (2015) Kamnitsas, K., Chen, L., Ledig, C., Rueckert, D., Glocker, B.: Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI. Ischemic Stroke Lesion Segment. 13, 46 (2015)
17.
Zurück zum Zitat Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)CrossRef Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)CrossRef
18.
Zurück zum Zitat Lansberg, M.G., Albers, G.W., Beaulieu, C., Marks, M.P.: Comparison of diffusion-weighted MRI and CT in acute stroke. Neurology 54(8), 1557–1561 (2000)CrossRef Lansberg, M.G., Albers, G.W., Beaulieu, C., Marks, M.P.: Comparison of diffusion-weighted MRI and CT in acute stroke. Neurology 54(8), 1557–1561 (2000)CrossRef
19.
Zurück zum Zitat Lopez, A.D., Mathers, C.D., Ezzati, M., Jamison, D.T., Murray, C.J.: Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet 367(9524), 1747–1757 (2006)CrossRef Lopez, A.D., Mathers, C.D., Ezzati, M., Jamison, D.T., Murray, C.J.: Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet 367(9524), 1747–1757 (2006)CrossRef
20.
Zurück zum Zitat Maier, O., et al.: ISLES 2015-a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med. Image Anal. 35, 250–269 (2017)CrossRef Maier, O., et al.: ISLES 2015-a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med. Image Anal. 35, 250–269 (2017)CrossRef
21.
Zurück zum Zitat Maier, O., Schröder, C., Forkert, N.D., Martinetz, T., Handels, H.: Classifiers for ischemic stroke lesion segmentation: a comparison study. PloS One 10(12), e0145118 (2015)CrossRef Maier, O., Schröder, C., Forkert, N.D., Martinetz, T., Handels, H.: Classifiers for ischemic stroke lesion segmentation: a comparison study. PloS One 10(12), e0145118 (2015)CrossRef
22.
Zurück zum Zitat Moeskops, P., Viergever, M.A., Mendrik, A.M., de Vries, L.S., Benders, M.J., Išgum, I.: Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1252–1261 (2016)CrossRef Moeskops, P., Viergever, M.A., Mendrik, A.M., de Vries, L.S., Benders, M.J., Išgum, I.: Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1252–1261 (2016)CrossRef
23.
Zurück zum Zitat Nie, D., Wang, L., Gao, Y., Sken, D.: Fully convolutional networks for multi-modality isointense infant brain image segmentation. In: 2016 13th International Symposium on Biomedical Imaging (ISBI), pp. 1342–1345. IEEE (2016) Nie, D., Wang, L., Gao, Y., Sken, D.: Fully convolutional networks for multi-modality isointense infant brain image segmentation. In: 2016 13th International Symposium on Biomedical Imaging (ISBI), pp. 1342–1345. IEEE (2016)
24.
Zurück zum Zitat Praveen, G., Agrawal, A., Sundaram, P., Sardesai, S.: Ischemic stroke lesion segmentation using stacked sparse autoencoder. Comput. Biol. Med. 99, 38–52 (2018)CrossRef Praveen, G., Agrawal, A., Sundaram, P., Sardesai, S.: Ischemic stroke lesion segmentation using stacked sparse autoencoder. Comput. Biol. Med. 99, 38–52 (2018)CrossRef
25.
Zurück zum Zitat Rekik, I., Allassonnière, S., Carpenter, T.K., Wardlaw, J.M.: Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal. NeuroImage: Clin. 1(1), 164–178 (2012)CrossRef Rekik, I., Allassonnière, S., Carpenter, T.K., Wardlaw, J.M.: Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal. NeuroImage: Clin. 1(1), 164–178 (2012)CrossRef
26.
Zurück zum Zitat Romera, E., Alvarez, J.M., Bergasa, L.M., Arroyo, R.: ERFNet: efficient residual factorized ConvNet for real-time semantic segmentation. IEEE Trans. Intell. Transp. Syst. 19(1), 263–272 (2018)CrossRef Romera, E., Alvarez, J.M., Bergasa, L.M., Arroyo, R.: ERFNet: efficient residual factorized ConvNet for real-time semantic segmentation. IEEE Trans. Intell. Transp. Syst. 19(1), 263–272 (2018)CrossRef
28.
Zurück zum Zitat Seshadri, S., Wolf, P.A.: Lifetime risk of stroke and dementia: current concepts, and estimates from the framingham study. Lancet Neurol. 6(12), 1106–1114 (2007)CrossRef Seshadri, S., Wolf, P.A.: Lifetime risk of stroke and dementia: current concepts, and estimates from the framingham study. Lancet Neurol. 6(12), 1106–1114 (2007)CrossRef
29.
Zurück zum Zitat Sirinukunwattana, K., et al.: Gland segmentation in colon histology images: the glas challenge contest. Med. Image Anal. 35, 489–502 (2017)CrossRef Sirinukunwattana, K., et al.: Gland segmentation in colon histology images: the glas challenge contest. Med. Image Anal. 35, 489–502 (2017)CrossRef
30.
Zurück zum Zitat Srivastava, N., Salakhutdinov, R.: Multimodal learning with deep Boltzmann machines. J. Mach. Learn. Res. 15, 2949–2980 (2014)MathSciNetMATH Srivastava, N., Salakhutdinov, R.: Multimodal learning with deep Boltzmann machines. J. Mach. Learn. Res. 15, 2949–2980 (2014)MathSciNetMATH
31.
Zurück zum Zitat Sudlow, C., Warlow, C.: Comparable studies of the incidence of stroke and its pathological types: results from an international collaboration. Stroke 28(3), 491–499 (1997)CrossRef Sudlow, C., Warlow, C.: Comparable studies of the incidence of stroke and its pathological types: results from an international collaboration. Stroke 28(3), 491–499 (1997)CrossRef
32.
Zurück zum Zitat Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR, pp. 2818–2826 (2016) Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR, pp. 2818–2826 (2016)
33.
Zurück zum Zitat Valverde, S., et al.: Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. NeuroImage 155, 159–168 (2017)CrossRef Valverde, S., et al.: Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. NeuroImage 155, 159–168 (2017)CrossRef
34.
Zurück zum Zitat Van der Worp, H.B., van Gijn, J.: Acute ischemic stroke. N. Engl. J. Med. 357(6), 572–579 (2007)CrossRef Van der Worp, H.B., van Gijn, J.: Acute ischemic stroke. N. Engl. J. Med. 357(6), 572–579 (2007)CrossRef
35.
Zurück zum Zitat Winzeck, S., et al.: ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI. Front. Neurol. 9 (2018) Winzeck, S., et al.: ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI. Front. Neurol. 9 (2018)
38.
Zurück zum Zitat Zhang, T., Qi, G.-J., Xiao, B., Wang, J.: Interleaved group convolutions. In: CVPR, pp. 4373–4382 (2017) Zhang, T., Qi, G.-J., Xiao, B., Wang, J.: Interleaved group convolutions. In: CVPR, pp. 4373–4382 (2017)
39.
Zurück zum Zitat Zhang, W., et al.: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108, 214–224 (2015)CrossRef Zhang, W., et al.: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108, 214–224 (2015)CrossRef
40.
Zurück zum Zitat Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. arXiv preprint arXiv:1707.01083 (2017) Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. arXiv preprint arXiv:​1707.​01083 (2017)
Metadaten
Titel
Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities
verfasst von
Jose Dolz
Ismail Ben Ayed
Christian Desrosiers
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-11723-8_27

Premium Partner